ELECTRICITY MONITORING DEVICES AND SYSTEMS, AND METHODS OF USE

THEREOF

CROSS-REFERENCE TO RELATED APPLICATIONS

Pursuant to 35 U.S.C. § 119(e), this application claims the benefit of U.S. Provisional Patent Application Nos. 62/038,630, filed August 18, 2014 and 62/085,130, filed November 26, 2014, which applications are incorporated herein by reference in their entirety.

INTRODUCTION

Worldwide electric power infrastructure is undergoing a transformation from a centralized, producer-controlled network to an infrastructure that is less centralized and more consumer-oriented. This transformation requires advanced control and monitoring of energy usage at the load level. Many electric current measuring technologies have been developed to monitor the electrical power in commercial and residential settings. Most of them require sensing devices to be either connected between the load and the supply of current, or implemented using current transformers encircling the current-carrying conductors. However, applying these methods for sub-metering an existing building can be challenging and costly because their installation requires interruption of electrical service and/or installation by a skilled technician.

SUMMARY

An electricity monitoring device for monitoring electricity usage is provided. The electricity monitoring device includes electromagnetic field sensors configured to generate sensor outputs representing properties of an electric circuit containing a conductive substrate, a circuit board containing the sensors, wherein the device is configured to position the

electromagnetic field sensors proximal to an outer surface than to an inner surface of an enclosure that contains the conductive substrate. A kit and system that includes the present electricity monitoring device are disclosed. Also provided is a method of monitoring electricity usage using the present electricity monitoring device, wherein the method may include calibrating the electricity monitoring device and/or reducing the effect of cross talk from interfering electromagnetic fields. A method for monitoring electricity usage in a building is also provided.

The present electricity monitoring device for monitoring electricity usage of an electric circuit, wherein the electric circuit comprises one or more conductive substrates within an enclosure defining an outer and inner surfaces, may include one or more circuit boards containing one or more electromagnetic field sensors configured to generate one or more sensor outputs, wherein the device is configured to position the one or more electromagnetic field sensors proximal to the outer surface of the enclosure (e.g., more proximal to the outer surface than to the inner surface of the enclosure), and to generate one or more device outputs representing one or more electrical properties of the conductive substrates. In some

embodiments, the one or more conductive substrates contain an alternating electric current. In some embodiments, the enclosure includes an insulating element that electrically insulates the one or more conductive substrates from the outer surface of the enclosure. In some embodiments, the one or more sensor outputs include a digital output or an analog output. In some

embodiments, the device output comprises a digital output or an analog output. In some embodiments, the device comprises a digital processing unit configured to generate a digital device output.

In any embodiment, the one or more electrical properties may include an amplitude and/or phase angle of an electric current in the one or more conductive substrates, and/or an electric potential difference and/or phase angle thereof in the one or more conductive substrates.

In any embodiment, the device may be configured to implement a calibration algorithm to calibrate the one or more device outputs.

In any embodiment, the device may be configured to reduce the effect of cross talk from an interfering electromagnetic field on the one or more device outputs. In some embodiments, the device is configured to implement an interference mitigation algorithm to reduce the effect of cross talk from an interfering electromagnetic field on the one or more device outputs.

In any embodiment, the one or more electromagnetic field sensors may include one or more electromagnetic field sensors including one or more capacitive pickups configured to generate one or more sensor outputs containing a signal proportional to the electric potential difference, and in phase with an oscillatory variation therein, in the one or more conductive substrates. In any embodiment, the one or more electromagnetic field sensors may include one or more magnetic field sensors configured to generate one or more sensor outputs containing a signal proportional to the amplitude of, and in phase with an oscillatory variation in, an electric current in the one or more conductive substrates. In some cases, the magnetic field sensor is a non-mechanical magnetic field sensor. In some embodiments, the magnetic field sensor is a giant magnetoresistive sensor, a Hall effect magnetic sensor, an anisotropic magnetoresistive sensor, a tunneling magnetoresistive sensor, an internally magnetoresistive sensor, a giant magneto impedance sensor, other similar types of magnetoresistive sensors, or any type of magnetic sensor. In some embodiments, the electromagnetic field sensors include a plurality of magnetic field sensors, each having one or more axes of sensitivity defining a first axis of sensitivity, wherein the first axes of sensitivity of the plurality of magnetic field sensors are substantially parallel among each other. In some embodiments, the first axes of sensitivity of the plurality of magnetic field sensors are in substantially the same direction among each other. In some embodiments, the electromagnetic field sensors include a first and second magnetic field sensors, wherein the device is configured to position the first magnetic field sensor more proximally to the one or more conductive substrates than the second magnetic field sensor, align the first and second magnetic field sensors along a radial axis emanating from a point of rotational symmetry of a planar cross section of a magnetic field produced by an electric current in the conductive substrate, wherein the planar cross section is perpendicular to a direction of the electric current, and perpendicularly position the first axis of sensitivity of the magnetic field sensors relative to the direction of an electric current in the one or more conductive substrates. In some

embodiments, the electromagnetic field sensors include a first, second and third magnetic field sensors, wherein the device is configured to the first, second and third magnetic field sensors are configured to generate a first, second and third outputs, respectively, that collectively are sufficient to reduce the effect of cross talk from an interfering electromagnetic field on the device output. In certain embodiments, the first, second and third outputs collectively represent the magnetic field distribution on the surface of the enclosure. In certain cases, the circuit board defines a plane, the first axis of sensitivity of the first, second and third magnetic field sensors are substantially parallel to the plane of the circuit board and are substantially parallel to each other, the first and third magnetic field sensors are at substantially equal distances respectively from the second magnetic field sensor , wherein the first axis of sensitivity of the first and third magnetic field sensors are substantially in the same direction as each other, and the device is configured to position the first axis of sensitivity of the magnetic field sensors substantially parallel to the direction of an electric current in the one or more conductive substrates, position the second magnetic field sensor more proximally to the one or more conductive substrates than the first and third magnetic field sensors. In some embodiments, the the first axis of sensitivity of first magnetic field sensor is substantially in the same direction as the first axis of sensitivity of the second and third magnetic field sensors. In certain embodiments, the computational unit is configured to subtract from a sensor output of the second magnetic field sensor the average sensor output between the first and third magnetic field sensors.

In any embodiment, the enclosure may define a first side and a second side opposite the first side of the one or more conductive substrates, and the device is configured to position the one or more electromagnetic field sensors at a location more proximal to the first side than the second side of the one or more conductive substrates.

In any embodiment, the device may include an attachment element configured to position the device on the enclosure in a manner sufficient for monitoring of electricity usage. In some embodiments, the attachment element is an adhesive, a magnet, a hook-and-loop fastener, retaining frame, or a retaining enclosure. In some embodiments, the attachment element provides consistent positioning of the one or more electromagnetic field sensors relative to the one or more conductive substrates in the enclosure. In some embodiments, the consistent positioning includes consistent alignment and/or consistent distance between each of the one or more electromagnetic field sensors relative to the one or more conductive substrates in the enclosure.

In any embodiment, the device may include a computational unit configured to process the one or more sensor outputs to generate one or more device outputs representing one or more electrical properties of the conductive substrates. In some embodiments, the computational unit is configured to implement an interference mitigation algorithm to reduce the effect of cross talk from an interfering electromagnetic field on the one or more device outputs. In some

embodiments, the computational unit includes a processor, a communication unit and/or a data storage unit.

In any embodiment, the device may include an energy source unit, such as a battery, a supercapacitor, an electromagnetic energy harvester, and/or a power supply configured to connect to an external power source.

Also provided herein is a method of monitoring electricity usage in an electric circuit, the electric circuit including one or more conductive substrates in an enclosure defining an outer and inner surfaces, the method including the steps of i) obtaining data comprising one or more device outputs from an electricity monitoring device, as described herein, positioned on the outer surface of the enclosure, and ii) analyzing the obtained data to derive one or more measures of electricity usage. In some embodiments, the device output includes a first set of values proportional to an amplitude of, and in phase with an oscillatory variation in, an electric current in the one or more conductive substrates, and a second set of values proportional to an electric potential difference, and in phase with an oscillatory variation therein, in the one or more conductive substrates. In some cases, the one or more measures of electricity usage includes the amplitude, real and time-averaged root mean square (RMS) and/or phase angle values of the electric current, the amplitude, real and time averaged RMS and/or phase angle value of the voltagein the one or more conductive substrates, the real and apparent instantaneous power, the real and apparent time-averaged RMS value of the power, the spectral density of current, voltage, real power and/or apparent power, and/or the frequency spectrum of the electric current, voltage, real power and/or apparent power in the one or more conductive substrates.

In any embodiment, the electricity monitoring device is disposed on the outer surface of the enclosure in a manner sufficient to reduce the effect of cross talk on the device outputs of the electricity monitoring device by an interfering electromagnetic field. In some embodiments, the electric circuit is a branch circuit of an electrical wiring system comprising a plurality of branch circuits. In some embodiments, the interfering electromagnetic field is generated by one or more other branch circuits of the plurality of branch circuits.

In any embodiment, the method may include calibrating the electricity monitoring device to determine one or more constants of proportionality for the one or more device outputs, and the analyzing may include deriving the one or more measures of electricity usage using the one or more constants of proportionality. In some embodiments, the calibrating step includes reducing the effect of cross talk on the one or more device outputs by an interfering electromagnetic field. In some embodiments, the calibrating step includes obtaining the one or more device outputs from the electricity monitoring device under a load current of known current amplitude and/or phase, and/or under a known electric potential difference and/or phase, thereby obtaining one or more reference device outputs, identifying that the load current is a reference load current passing through the one or more conductive substrates in the electric circuit, and calculating the one or more constants of proportionality based on the known current amplitude and/or phase, and/or the known electric potential difference and/or phase, and the one or more reference device outputs. In some embodiments, the calibrating step includes drawing a predetermined reference load current of known amplitude and/or phase to the electric circuit. In some embodiments, the electric circuit is a branch circuit of an electrical wiring system comprising a plurality of branch circuits, and the calibrating step includes measuring the electric current amplitude and/or phase, and/or the electric potential difference and/or phase of the electrical wiring system under the load current, and obtaining data containing the electric current amplitude and/or phase of the plurality of branch circuits when the electrical wiring system is under the load current, and the identifying step includes identifying that the load current is a reference load current passing through the one or more conductive substrates in the branch circuit, wherein the one or more device outputs from the branch circuit is uniquely correlated with the measured current amplitude and/or phase for the load current among the plurality of branch circuits.

In any embodiment, the electric circuit may include a circuit breaker, a single conductor, a zip wire, power line, electrical relay, electrical panel, junction box, fuse box, raceway, etc.

In any embodiment, the method may include positioning the electricity monitoring device above or on the outer surface of the enclosure.

In any embodiment, the method may include comparing the one or more measures of electricity usage for the electric circuit with a reference measure of electricity usage. In some embodiments, the reference measure of electricity usage for the electric circuit is derived from a historical record including a historical pattern of electricity usage for the electric circuit, and the method includes identifying the one or more measures of electricity usage as being aberrant when there is a deviation of the one or more measures of electricity usage from the historical pattern. In some embodiments, the reference measure of electricity usage for the electric circuit is a predetermined measure of electricity usage of an electrical appliance, and the method further comprises identifying that the electrical appliance is in use when the one or more measures of electricity usage matches the predetermined measure of electricity usage of the electrical appliance.

Also provided herein is a system for monitoring electricity usage in an electric circuit, including i) one or more electricity monitoring devices, as described herein, and ii) a computer system configured to receive a device output from the one or more electricity monitoring devices. In some embodiments, the electric circuit is a branch circuit of an electrical wiring system containing a plurality of branch circuits and the system includes a main meter for monitoring electricity usage in the electrical wiring system, wherein the computer system is configured to receive electricity usage data from the main meter. In some embodiments, the main meter includes at least one of the electricity monitoring devices. In some embodiments, the main meter is a utility billing meter. In some embodiments, the computer system is configured to calibrate the one or more electricity monitoring devices based on the electricity usage data received from the main meter. In some embodiments, the electricity monitoring device comprises the computer system. In some embodiments, the computer system includes a wireless

communication unit to communicate with the one or more electricity monitoring devices and/or the main meter. Kits that include the one or more electricity monitoring devices, as described herein, are also disclosed.

Also disclosed herein is a method for monitoring electricity usage in a building containing one or more electricity meters configured to monitor electricity usage in a wiring system and/or subcircuit thereof of the building, and to communicate with a computer system, the method including the steps of i) predicting, on the computer system, electricity usage values for the wiring system and/or subcircuit thereof for each time point over a plurality of time points during a first time interval, ii) determining, on the computer system, a deviation in electricity usage using the predicted electricity usage values and measured electricity usage values for each time point over the plurality of time points during the first time interval, iii) diagnosing the electricity usage based on the deviation in electricity usage, thereby generating a diagnosis of electricity usage, and iv) improving the electricity usage of the wiring system and/or subcircuit thereof based on the diagnosis.

In any embodiment, the predicting step may include generating a predictive model for electricity usage, wherein the predictive model is constructed based on historical data for the building, and wherein the historical data defines a second time interval that immediately precedes the first time interval. In some embodiments, the historical data includes electricity usage values and outside air temperature values of the building for each time point over a plurality of time points during the second time interval. Generally, the generating step may include using any appropriate time series or regression based algorithms or combinations thereof. In certain embodiments, the generating step includes using, by way of example and not limitation, a Seasonal and Trend decomposition based on Loess (STL) algorithm, a General Linear Abstraction of Seasonality weighted moving average filter, Henderson weighted moving average filter, or a Kalman filter. In any embodiment, the deviation in electricity usage includes a residual of electricity usage based on the predicted and measured electricity usage values. In some embodiments, the determining step includes measuring electricity usage during the first time interval, thereby obtaining the measured electricity usage values, and subtracting the predicted electricity usage values from the measured electricity usage values, thereby determining the residual of electricity usage.

In any embodiment, the diagnosing step includes computing an average residual of electricity usage over a third time interval, wherein the third time interval is subsampled with replacement from the first time interval, and determining a deviation in the electricity usage based on the difference between the average residual and the residuals of electricity usage for all possible subsamples of a chosen number of time points in the plurality of time points during the first time interval. In some embodiments, the determining step includes determining that the absolute value of the difference between the average residual and the residual of electricity usage for a time point in the plurality of time points during the first time interval is above a control limit.

In any embodiment, the diagnosing step may include identifying residuals of electricity usage that are above a threshold percentile over all residuals in a fourth time interval, wherein the fourth time interval is a subinterval of the first time interval, thereby determining an outlier residual value, and determining a deviation in the electricity usage based on whether the absolute value of the residual for a time point in the plurality of time points during the first time interval is greater than the outlier residual value.

In any embodiment, the diagnosing step may include detecting the deviation in the electricity usage. In any embodiment, the method may further include identifying an electrical appliance that is responsible for the deviation in the electricity usage, wherein the electrical appliance is metered by an electricity meter of the one or more electricity meters. In some embodiments, the improving step includes replacing, repairing or upgrading the electrical appliance.

In any embodiment, the improving step may include commissioning the building. In some embodiments, the building is a newly built building and the commissioning is

commissioning the newly built building, or the building is an existing building and the commissioning is relrocommissioning or recommissioning the existing building. In any embodiment, the improving step results in reducing the magnitude of residuals of electricity usage values and/or the frequency of deviations in electricity usage compared to before the improving.

In any embodiment, the method may include generating one or more charts showing the electricity usage, residual of electricity usage, and/or the result of the diagnosis. In any embodiment, the method may include generating a report comprising results of the diagnosis of electricity usage for the building during the first time interval. In any embodiment, the method may include sending a notification to an individual, wherein the notification comprises a message to alert the individual of the diagnosis.

BRIEF DESCRIPTION OF THE FIGURES

The ordinarily skilled artisan will understand that the drawings, described below, are for illustration purposes only. The drawings are not intended to limit the scope of the present teachings in any way.

FIG. 1 is a collection of schematic representations of an electricity monitoring device, according to embodiments of the present disclosure.

FIG. 2 is a circuit diagram of a capacitive pickup voltage sensor that finds use in an electricity monitoring device, according to embodiments of the present disclosure.

FIG. 3 is graph showing a voltage output of an electricity monitoring device, according to embodiments of the present disclosure.

FIG. 4 is a collection of schematic diagrams showing the relative positions of magnetic field sensors in an electricity monitoring device and a conductive substrate for monitoring electricity usage in an electric circuit containing the conductive substrate through which an electric current flows, according to embodiments of the present disclosure.

FIG. 5 is an image showing the interior of a thermal-magnetic circuit breaker and the optimal location for placing an electricity monitoring device, according to embodiments of the present disclosure.

FIG. 6 is a schematic diagram showing an electricity monitoring device on a circuit breaker panel, according to embodiments of the present disclosure.

FIG. 7 is a collection of graphs showing simulation and experimental results of the x- component of a magnetic field, Bx produced by a current in circuit 2, as shown in Fig. 6. FIG. 8 is a collection of graphs showing simulation and experimental results of the x- component of the gradient of the magnetic field along the x-direction, Bxx, produced by the current in circuit 1, as shown in Fig. 6.

FIG. 9 is a collection of images showing an electricity monitoring device (left) and an electricity monitoring device placed on a circuit breaker panel, according to embodiments of the present disclosure.

FIG. 10 is a graph showing the result of a cross-talk mitigating, signal deconvolution algorithm, according to embodiments of the present disclosure.

FIG. 11 is a graph showing the mean absolute percent error for predicting electricity usage in two buildings, according to embodiments of the present disclosure.

FIG. 12 is a moving average chart for Building 1, according to embodiments of the present disclosure.

FIG. 13 is a moving average chart for Building 2, according to embodiments of the present disclosure.

FIG. 14 is a rolling outlier chart for residuals of power usage in Building 1, according to embodiments of the present disclosure.

FIG. 15 is a rolling outlier chart for residuals of power usage in Building 2, according to embodiments of the present disclosure.

FIG. 16 is a graph showing Out of Control and Hard to Predict time points in power usage in Building 1, according to embodiments of the present disclosure.

FIG. 17 is a graph showing Out of Control and Hard to Predict time points in power usage in Building 2, according to embodiments of the present disclosure.

FIG. 18 is a graph showing detection of an aberration in power usage in Building 1, according to embodiments of the present disclosure.

FIG. 19 is a graph showing detection of an aberration in power usage in Building 2, according to embodiments of the present disclosure.

FIG. 20 is a schematic diagram showing a microelectromechanical system (MEMS) electret-based current sensor, according to embodiments of the present disclosure.

FIGs. 21A and 21B are a collection of diagrams describing simulation parameters for multipoint sensing using 3 sensors, according to embodiments of the present disclosure.

FIG. 22 is a collection of figures showing simulation results for multipoint sensing using 3 sensors, according to embodiments of the present disclosure. FIG. 23 is a schematic illustration of an embodiment that includes a separate DAQ device provided in association with a plurality of electricity monitoring devices.

DEFINITIONS

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present teachings, some exemplary methods and materials are now described.

"Electric current," as used herein, refers to a flow of electric charge, e.g., flow of electrons, in a conductive medium. The electric current may be direct, time varying or alternating.

"Axis," as used in reference to the axis of sensitivity of an electromagnetic field sensor, denotes a preferred orientation relative to the direction of the electromagnetic field for the electromagnetic field sensor to detect the electromagnetic field.

"Align," as used herein, refers to positioning a first axis parallel to a second axis, or positioning a first direction parallel to and with the same polarity as a second direction.

"Electricity usage," as used herein, refers to one or more measures of a flow of electric charge in an electrical circuit that can be used to derive the amount of electric energy consumed by the electric circuit. The one or more measures include a sensor output of electromagnetic field sensors and/or a device output of an electricity monitoring device, the amplitude and phase angle of the electric current, and the electric potential difference across the electric circuit and phase angle thereof. In some instances, the electricity usage includes the electric power drawn by the electric circuit.

"Non-mechanical," as used in reference to a non-mechanical magnetic field sensor is a magnetic sensor that does not rely on mechanical movement of structures in the sensor to detect a magnetic field or transform a detected magnetic field into a sensor output. In some cases, a non-mechanical magnetic field sensor excludes sensors that rely on a piezoelectric effect.

"Signal," as used herein, refers to a detectable change in an electromagnetic sensor output caused by the electromagnetic field detected by the sensor. The signal in some cases may be a voltage signal produced by the sensor in response to an electromagnetic field, or a change thereof.

"Cross talk," as used herein, refers to the interfering effect of an electromagnetic field on an electromagnetic sensor other than the electromagnetic field generated by the target source the sensor is intended to measure. Cross talk may reduce the sensitivity and/or accuracy of the electromagnetic sensor to detect the electromagnetic field generated by the target source.

"Calibrating," as used herein, refers to a process of empirically determining the difference in the measured value of a physical process (e.g., a current amplitude derived from a voltage output from an electricity monitoring device measuring an electric current amplitude) and the actual value of the physical process (e.g., the actual electric current amplitude) to derive one or more calibration values that may be used to convert the measured value into an actual value of the physical process.

"Self-powered," as used in reference to a self -powered device includes a device that does not require a physical connection to an external source of power during operation of the device. The self -powered device may include energy storage elements that can store energy (e.g., a battery) and/or capture energy from the environment (e.g., an energy harvester). In some case, a self -powered device may use a physical connection to an external source for recharging the energy storage element (e.g., a rechargeable battery).

"Branch circuit," as used herein, refers to a portion of an electrical wiring system that extends beyond an overcurrent protective device, e.g., a circuit breaker. In some instances, the branch circuit extends beyond the final overcurrent protective device in the electrical wiring system.

"Historical," as used herein, refers to a time period before a reference time point.

"Diagnosing," as used herein, includes identifying a deviation in the behavior of a system from a prediction or expectation, and/or finding the cause of the deviation. In some instances, diagnosing an electricity usage includes identifying a specific branch circuit or appliance within an electrical wiring system that is likely to be responsible for the deviation. The deviation may be identified as a fault or error in that part of the system. In some cases, diagnosing an electricity usage includes predicting a future outcome or estimating the likelihood of a future outcome, or predicting a response to an intervention relating to the electricity use in the system.

"Commissioning," as used herein, refers to a quality assurance process for ensuring that the operation and/or maintenance of a system, e.g., the performance of a building, conforms to a certain standard. In some instances, a new building is commissioned to ensure adequate operation and/or maintenance through the designing, construction and operation processes. In some instances, an existing building is relrocommissioned by applying a commissioning process to improve the performance of the building. An existing building may be recommissioned after undergoing a commissioning/relrocommisioning process to improve the performance of the building. Relrocommissioning or recommissioning may resolve problems that occurred during design or construction, or that have developed over the course of using the building.

DETAILED DESCRIPTION

An electricity monitoring device for monitoring electricity usage is provided. The electricity monitoring device includes electromagnetic field sensors configured to generate sensor outputs representing electrical properties of an electric circuit containing a conductive substrate, a circuit board containing the sensors, and a computational unit configured to process the sensor outputs to generate device outputs, wherein the device is configured to position the electromagnetic field sensors proximal to an outer surface of an enclosure that contains the conductive substrate in an electric circuit. A kit and system that contains the present electricity monitoring device are disclosed. Also provided is a method of monitoring electricity usage using the present electricity monitoring device, wherein the method may include calibrating the electricity monitoring device and/or reducing the effect of cross talk from interfering

electromagnetic fields. A method for monitoring electricity usage in a building is also provided.

Before the various embodiments are described, it is to be understood that the teachings of this disclosure are not limited to the particular embodiments described, and as such can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present teachings will be limited only by the appended claims.

The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described in any way. While the present teachings are described in conjunction with various embodiments, it is not intended that the present teachings be limited to such embodiments. On the contrary, the present teachings encompass various alternatives, modifications, and equivalents, as will be appreciated by those of skill in the art. Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the present disclosure.

The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present claims are not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided can be different from the actual publication dates which can need to be independently confirmed.

It must be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It is further noted that the claims can be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as "solely," "only" and the like in connection with the recitation of claim elements, or use of a "negative" limitation.

As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which can be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present teachings. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

One with skill in the art will appreciate that embodiments of the present disclosure are not limited in its application to the details of construction, the arrangements of components, category selections, weightings, pre-determined signal limits, or the steps set forth in the description or drawings herein. The present disclosure may encompass other embodiments and may be practiced or be carried out in many different ways.

DEVICES

As summarized above, the present disclosure provides an electricity monitoring device for monitoring electricity usage from an outer surface of an enclosure that contains a conductive substrate in an electric circuit by sensing the electromagnetic fields generated by electric charges flowing in the conductive substrate. The device finds use in monitoring electricity usage at a variety of monitoring sites within an electric circuit and at a variety of types of enclosures containing the conductive substrates, such as on individual circuit breakers on a circuit breaker panel, overhead electric power lines, single wire conductors, zip cords, etc. The present electricity monitoring device may provide a way to non-invasively measure electricity usages from the different types of energized conductors. The device may be attached to the outside surface of an insulating enclosure surrounding the conductive substrate, thereby providing an electricity usage submetering tool for monitoring electricity usage in an existing electrical wiring system without having to provide galvanic contact (e.g., direct physical contact) to the conductive substrate of the circuit. In some instances, the present electricity monitoring device provides a metering tool for monitoring electricity usage in a branch circuit of an existing electrical wiring system. In some embodiments, the output from the device can be processed by a computer system configured to communicate with the device, and calibrate the measured electrical properties of the electric circuit and/or mitigate cross talk from interfering

electromagnetic fields in the environment based on the output from a single device, or based on data from more than one device and/or other utility meters. In some cases, the device provides on-board circuitry to calibrate the measured electrical properties of the electric circuit and/or to mitigate cross talk from interfering electromagnetic fields in the environment. These and other aspects of the present electricity monitoring device will be described in greater detail, with reference to the figures.

With reference to Fig. 1, the present electricity monitoring device 1100 includes one or more electromagnetic field sensors 1120 on one or more circuit boards, e.g., a circuit board 1110, such as a printed circuit board (PCB). The electricity monitoring device may be configured such that each electromagnetic field sensor on a circuit board within the device can couple to an electromagnetic field that surrounds the device and to which the sensor is sensitive, and generate sensor outputs that reflect properties of the electromagnetic field. The sensor output may include a digital output and/or an analog output.

The electromagnetic field may be produced by an electric charge, e.g., electrons, flowing through a conductive substrate, e.g., a conductive metal wire, in an electric circuit. As the electricity monitoring device employs an electromagnetic field sensor to detect the flowing electric charge in the conductive substrate, the conductive substrate may be enclosed within an enclosure such that the electricity monitoring device does not make galvanic contact (e.g., direct physical contact) with the conductive substrate. (See also, e.g., Fig. 4.) In employing the present electricity monitoring device to monitor electricity usage, the device may be positioned on the outer surface of the enclosure such that the electromagnetic field sensors detect an electromagnetic field generated by the flowing electric charge in the conductive substrate from outside the enclosure. (See also, e.g., Figs. 5 and 9.) Thus present electricity monitoring device may be configured such that when the device is positioned on the outer surface of the enclosure, the electromagnetic field sensors are positioned sufficiently close to the conductive substrate for the electromagnetic field sensors to detect an electromagnetic field generated by an electric current passing through the conductive substrate. The electricity monitoring device may be configured such that when the device is positioned to monitor electricity usage from the conductive substrate in an enclosure, the electromagnetic field sensors are closer to the outer surface than they are to the inner surface of the enclosure.

In some embodiments, the electric current representing the flowing electric charges in the conductive substrate is a time- varying electric current. In some embodiments, the time-varying electric current is an alternating electric current and has a frequency of 0.01 Hz or more, e.g., 0.1 Hz or more, 1 Hz or more, 5 Hz or more, 10 Hz or more, 40 Hz or more, 45 Hz or more, 50 Hz or more, 60 Hz or more, 100 Hz or more, including 300 Hz or more, and has a frequency of 1000 Hz or less, e.g., 800 Hz or less, 600 Hz or less, 500 Hz or less, 250 Hz or less, 100 Hz or less, 70 Hz or less, 65 Hz or less, 60 Hz or less, 50 Hz or less, 20 Hz or less, 10 Hz or less, including 1 Hz or less. In some cases, the alternating electric current has a frequency in the range of 0.01 to 1000 Hz, e.g., 0.1 to 800 Hz, 1 to 600 Hz, 10 to 500 Hz, 10 to 100 Hz, 40 to 70 Hz, 45 to 65 Hz, including 50 to 60 Hz. In some cases, the electric current representing the flowing electric charges in the conductive substrate is a direct current.

The device may include a computational unit 1140 that contains the frontend circuitry to process sensor outputs from the electromagnetic field sensors 1120 and generate the device outputs from the electricity monitoring device 1100. In certain embodiments, the computational unit is configured to condition the sensor outputs, and/or apply built-in and/or programmed algorithms to process the sensor outputs. (See also, e.g., Fig. 2.) Thus, in some cases the computational unit may include differential amplifiers, e.g., operational amplifiers, configured to condition the sensor output from the electromagnetic field sensors. The algorithms applied by the computational unit may extract from the electromagnetic field sensor outputs a signal representing properties of the conductive substrate through which electric charges are flowing, and the extracted signal may be passed on as a device output. The computational unit may be configured to apply to the sensor output (conditioned or not) a number of suitable algorithms, including a calibration algorithm and a cross talk mitigation algorithm, as described herein. In some embodiments, the computational unit contains a processor to implement the processes and algorithms, e.g., implement calibration techniques and/or cross talk mitigation algorithms; a communication unit 1142 to, e.g., communicate with a computer system and/or other electricity monitoring devices; and/or a data storage unit 1144. The computational unit may also include any suitable components for processing the sensor output to generate a device output. Additional components of the computational unit include, but are not limited to, a rectifying element, analog-to-digital converter, a current- to- voltage converter, an inverter, a low-pass filter, a resistor, etc. The communication unit may be a wired and/or a wireless communication unit. The device output may be a digital output and/or an analog output.

In some embodiments, the electricity monitoring device 1100 contains a data acquisition (DAQ) device configured to interface the electricity monitoring device with a remote computer system, e.g, a remote server. In some cases, the DAQ device can be further configured to process the sensor outputs, and/or the DAQ device may relay the device outputs to a remote computer system where processing may occur. In some cases, the DAQ device is part of the computational unit 1140. In some cases, the DAQ device and the computational unit are part of the electricity monitoring device.

In some embodiments, the electricity monitoring device 1100 and the DAQ device may be provided as separate components. FIG. 23 is a schematic illustration of an embodiment that includes a separate DAQ device 1178 provided in association with a plurality of electricity monitoring devices 1100. The DAQ device 1178 may be configured to receive sensor data from the various electricity monitoring devices 1100 and to condition the sensor data for further processing and transmission across a network 1182. In some embodiments, the DAQ device 1178 may include wired or wireless communication components configured for data

communication between the DAQ device 1178 and the network 1182. For example, the DAQ device 1178 may include an antenna that allows the DAQ device 1178 to communicate across a wireless local area network (WLAN) or the like. Generally, the DAQ device 1178 may implement any communication protocol that allows the DAQ device 1178 to communicate across a network. The DAQ device 1178 may implement such communication protocols as IEEE 802.11, Bluetooth, ZigBee, etc. Through its network connectivity, the DAQ device 1178 may communicate with remote network nodes on the Internet, cloud-based network nodes, or the like. In this way, the DAQ device 1178 may provide sensor data to a remote server 1184 which applies further processing to the sensor data as described herein. In the configuration of FIG. 23, the DAQ device 1178 and the electricity monitoring devices 1100 are positioned within an enclosure 1174 that provides access to a number of circuit breakers. As shown in FIG. 23, the electricity monitoring devices 1100 are positioned relative to the circuit breakers and configured to sense one or more electrical properties and to transmit sensor data to the external the DAQ device 1178. It should be appreciated that the configuration of FIG. 23 is shown by way of example and not limitation. In some embodiments, an external DAQ device 1178 may be used in connection with electricity monitoring devices 1100 that sense electrical parameters from other conductive substrates as described herein, such as a circuit breaker panel, overhead electric power lines, single wire conductors, zip cords, etc.

The output of the present electricity monitoring device 1100 may contain a signal that represents various properties of the flowing electric charge, depending on the electromagnetic field sensor present on the present device. In certain aspects, the electricity monitoring device outputs, or outputs from a collection of electricity monitoring devices, contain information about the monitored electric circuit that is sufficient for electricity usage information that is specific for the electric circuit to be derived upon processing the output. In some embodiments, the electricity monitoring device outputs include the amplitude and/or phase angle of the electric current in conductive substrate, and/or the electric potential difference and/or phase angle thereof, of the conductive substrate. The electric potential difference may be relative to a suitable reference potential. In some cases, the electricity monitoring device has a floating voltage. In some cases, the electricity monitoring device is grounded.

In certain embodiments, the present electricity monitoring device 1100 is configured to reduce or minimize the effect of cross talk from an interfering electromagnetic field on the output of the device, as described herein. The interfering electromagnetic field may be generated from one or more current sources, other than the conductive substrate being monitored, that are near enough to the electricity monitoring device such that the electromagnetic field generated by the current sources cross-couples with the electricity monitoring device.

In certain embodiments, the electromagnetic field sensor 1120 includes a capacitive pickup 1121, configured to generate sensor outputs that contains a signal proportional to the electric potential difference, i.e., the voltage, and in phase with an oscillatory variation therein, of the conductive substrate through which electric charges are flowing, when the electricity monitoring device 1100 is suitably placed on the surface of an enclosure surrounding the conductive substrate. The capacitive pickup may be any suitable conductive element. In some embodiments, the capacitive pickup is a conductive plate. In some embodiments, the capacitive pickup is made of copper, or a heavily doped silicon material. In some embodiments, the capacitive pickup forms a part of the circuit board. The dimensions of the capacitive pickup may vary depending on the material with which it is made and the desired sensitivity level, and can be any convenient dimensions. The shape of the capacitive pickup plate may be rectangular, square, circular, hexagonal, semicircular, etc. In some cases the capacitive pickup plate may have a width that is 0.001 mm or more, e.g., 0.01 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, including 10 mm or more, and may have a width that is 100 mm or less, e.g., 10 mm or less, 8 mm or less, 5 mm or less, 1 mm or less, 0.5 mm or less, 0.1 mm or less, including 0.01 mm or less. In some embodiments, the capacitive pickup plate may have a width in the range of about 0.001 mm to about 100 mm, e.g., about 0.01 mm to about 10 mm, about 0.01 mm to about 8 mm, including about 0.1 mm to about 5 mm. The capacitive pickup plate may have length that is 0.001 mm or more, e.g., 0.01 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, including 10 mm or more, and may have a length that is 100 mm or less, e.g., 10 mm or less, 8 mm or less, 5 mm or less, 1 mm or less, 0.5 mm or less, 0.1 mm or less, including 0.01 mm or less. In some cases, the capacitive pickup plate may have a length in the range of about 0.001 mm to about 100 mm, e.g., about 0.01 mm to about 10 mm, about 0.01 mm to about 8 mm, including about 0.1 mm to about 5 mm.

In some embodiments, the capacitive pickup 1121 is configured on a circuit board 1110 of the present electricity monitoring device 1100 such that, when the device is suitably placed on the surface of an enclosure surrounding a conductive substrate with an alternating electric current, the alternating electric field forms between the conductive substrate and the capacitive pickup. Thus, in such cases, the conductive substrate contains an alternating electric current. As a result, an electric current is produced by the capacitive pickup. The magnitude of the current is proportional to the voltage in the conductive substrate. The circuit board and computational unit 1140 may be configured to convert the electric current into a voltage output that is proportional to the magnitude of the voltage in the conductive substrate and is synchronized in phase with the alternating voltage being measured. In some cases, the computational unit may not be in phase with the voltage sensor output from the capacitive pickup. The capacitive pickup, computational unit (if present), and circuit board may be configured in any suitable way. In some embodiments, the computational unit includes a current- to- voltage converter, an inverter, a low-pass filter, and a resistor, arranged with the capacitive pickup in the present device according to the circuit diagram shown in Fig. 2.

In some embodiments, the electromagnetic field sensor 1120 includes 1 or more, e.g., 2 or more, 3 or more, including 5 or more capacitive pickups, and may include 5 or fewer, e.g., 4 or fewer, 3 or fewer, including 2 or fewer capacitive pickups. In certain embodiments, the electromagnetic field sensor includes a range of 1 to 5, e.g., 1 to 3, including 1 or 2 capacitive pickups. In some cases, the electromagnetic field sensor includes one capacitive pickup.

In certain embodiments, the electromagnetic field sensor 1120 includes a magnetic field sensor 1125, configured to generate sensor outputs that contain a signal proportional to the amplitude of, and in phase with an oscillatory variation in, an electric current in the conductive substrate when the electricity monitoring device 1100 is suitably placed on the surface of an enclosure surrounding a conductive substrate passing an electric current. The magnetic field sensor may be sensitive to the magnetic field produced by an electric current. Without being held to theory, the magnetic field may be proportional to and in phase with the electric current according to the Biot-Savart Law.

The magnetic field sensor may be any suitable magnetic field sensor for use in the present device. In some embodiments, the magnetic field sensor is a bipolar magnetic field sensor or a unipolar magnetic field sensor. The magnetic field sensor may have one or more, e.g., two or more, including three or more axes of sensitivity. In some cases, the magnetic field sensor has two or three axes of sensitivity, where each axis is perpendicular among each other.

In some embodiments, the magnetic field sensor is a non-mechanical magnetic field sensor, such that the operating principle of the sensor does not include changes in shape or movement of the sensor components due to a magnetic field. In some embodiments, the operating principle of the magnetic field sensor includes a change in resistance of a conductive element or a separation of charge in the sensor due to a magnetic field. In some embodiments, the magnetic field sensor is a Wheatstone bridge magnetic sensor.

In some instances, the magnetic field sensor continuously consumes an electric current of 0.1 mA or more, e.g., 0.5 mA or more, 1 mA or more, including 2 mA or more, and may continuously consume an electric current of 10 mA or less, e.g., 5 mA or less, 4 mA or less, 3 mA or less, 1 mA or less, including 0.1 mA or less. In certain embodiments, the magnetic field sensor continuously consumes an electric current in the range of 0.1 mA to 10 mA, e.g., 0.5 mA to 5 mA, including 1 mA to 4 mA. In certain embodiments, where the magnetic field sensor is powered by a 3 voltage source, the magnetic field sensor consumes power at 0.3 mW or more, e.g., 1.5 mW or more, 3 mW or more, including 6 mW or more, and may consume power at 30 mW or less, e.g., 15 mW or less, 12 mW or less, 9 mW or less, 3 mW or less, including 0.3 mW or less. In some embodiments, where the magnetic field sensor is powered by a 3 voltage source, the magnetic field sensor consumes power in the range of 0.3 mW to 30 mW, e.g., 1.5 mW to 15 mW, including 3 mW to 12 mW.

In certain embodiments, the magnetic field sensor 1125 is a magnetoresistive sensor, including, but not limited to a giant magnetoresistive sensor, an anisotropic magnetoresistive sensor, a tunneling magnetoresistive sensor, or an internally magnetoresistive sensor. In some embodiments, magnetoresistive sensors include a soft ferromagnetic layer of, e.g., NiFe or NiFe/CoFe, whose magnetization direction can turn depending on the external magnetic field. In anisotropic magnetoresistance (AMR) sensors, the resistance varies with the angle Θ between the magnetization of the soft layer and the direction of the current generating the magnetic field. In giant magnetoresistance (GMR) or tunnel magnetoresistance (TMR) sensors, the resistance varies with the angle Θ between the magnetization of the soft layer and the magnetization of a hard ferromagnetic layer that constitutes a reference layer. The above sensors are given by way of example and not limitation. Generally, any type of magnetic field sensor may be used.

In some cases, the magnetic field sensor 1125 is a giant magneto impedance (GMI) sensor. In some embodiments, the magnetic field sensor 1125 is a Hall effect magnetic sensor. Any other magnetic field sensor may be used.

In some embodiments, the magnetic field sensor 1125 is configured on a circuit board 1110 of the present electricity monitoring device 1100 such that, when the device is placed on the surface of an enclosure surrounding a conductive substrate with an electric current, the magnetic field produced by the electric current is detected by the magnetic field sensor. As the magnetic field is proportional to the current and further in phase with an oscillatory electric current, the magnitude of the current can be obtained by knowing the proportionality of the sensor's response to the magnetic field. The proportionality may be obtained through calibration or by knowing the magnetic field distribution as a priori information, as described herein.

In some embodiments, the electromagnetic field sensor 1120 includes 1 or more, e.g., 2 or more, 3 or more, including 5 or more magnetic field sensors, and may include 10 or fewer, e.g., 8 or fewer, 5 or fewer, 4 or fewer, 3 or fewer, including 2 or fewer magnetic field sensors. In certain embodiments, the electromagnetic field sensor includes a range of 1 to 10, e.g., 1 to 5, including 1 or 3 magnetic field sensors. In some cases, the electromagnetic field sensor includes one, two or three magnetic field sensors.

In some embodiments, the electromagnetic field sensors 1120 include a plurality of magnetic field sensors, where each of the plurality of magnetic field sensors have at least one axis of sensitivity that is substantially parallel to the axis of sensitivity of one or more, or all of the other magnetic field sensors. In some embodiments, the magnetic field sensors have at least one axis of sensitivity that is substantially parallel to and in substantially the same direction as an axis of sensitivity of one or more, or all of the other magnetic field sensors.

In certain embodiments, the electricity monitoring device contains two or more magnetic field sensors. In some embodiments, two magnetic field sensors are positioned in the device such that the first magnetic field sensor is more proximally positioned to the conductive substrate(s) being monitored than the second magnetic field sensor. The first and second magnetic field sensors may be further aligned relative to the magnetic field generated by the electric current in the conductive substrate(s), such that the two sensors are on a radial axis originating from a point of rotational symmetry of a planar cross section of the magnetic field, wherein the planar cross section is perpendicular to the direction of current flow in the conductive substrate(s). The axes of sensitivity of the magnetic field sensors are further positioned perpendicularly relative to the direction of an electric current in the conductive substrate(s). (See also, Fig. 4.) In some embodiments, the axes of sensitivity of the magnetic field sensors are further positioned substantially perpendicularly to the aforementioned radial axis. In some embodiments, the axes of sensitivity of the magnetic field sensors are further positioned substantially along the aforementioned radial axis.

In some embodiments, the electricity monitoring device contains three or more magnetic field sensors, e.g., for preforming a multi-point sensing technique. In some embodiments, three magnetic field sensors are disposed in the device such that when the device is positioned on the surface of an enclosure for a conductive substrate, the first, second and third magnetic field sensors generate outputs that represent the magnetic field distribution on the surface of the enclosure. The computational unit may be configured to process the sensor outputs from the first, second and third magnetic field sensors to generate one or more device outputs with reduced cross talk from an interfering electromagnetic field. In some cases, the circuit board of the present device defines a plane, the axis of sensitivity of the first, second and third magnetic field sensors are substantially parallel to the plane of the circuit board, the first and third magnetic field sensors are at substantially equal distances respectively from the second magnetic field sensor and have substantially the same direction of sensitivity as each other, and the device is configured to position the axis of sensitivity of the magnetic field sensors substantially parallel to the direction of an electric current in the one or more conductive substrates, and position the second magnetic field sensor more proximally to the one or more conductive substrates than the first and third magnetic field sensors. (See also, Figs. 6 and 9.)

The multi-point sensing technique employs in some instances the use of three magnetic sensors for one electricity monitoring device, e.g., a single stick-on meter, as shown in Fig. 6, and described above. This technique is based on two observations on the distribution of the magnetic field on a surface of an enclosure containing multiple electric circuits distributed in a two dimensional plane, e.g., a circuit breaker panel.

Observation 1: The current flow path for most, or at least part of the circuit breakers may be substantially in the Y-Z plane. Therefore, the resulting magnetic field in the x-direction, can be symmetric with respect to the Y-Z plane. Therefore, the voltage outputs of sensor 1 and sensor 3 as a result of signal 2 in Fig. 6 may be substantially the same.

Observation 2: The gradient of the magnetic field produced by signals from neighboring circuits (signal 1 and signal 3 in this example) is very close to a constant at sensor 2. This implies that the voltage outputs of all three sensors caused by the interference signals are linearly dependent.

Deconvolution Algorithms: Based on these two observations, the output signal of a magnetic sensor j on the stick-on meter can be written as a linear combination of voltage output caused by the signal of interest ,

and the voltage output caused by the interfering signals ^ generated by the neighboring circuits, as follows,

where subscripts sig, int and j=1,2,3 represent signal, interference, and the index of sensors as shown in Fig. 6.

From observation 1, And from observation 2,

Therefore, substituting these two equations into the set of equation yields,

Therefore, the left hand side of the equation becomes independent of the signals from the neighboring circuits, and thus only responds to the signal of interest in circuit 2.

The generalized multi-point sensing technique using n magnetic sensors is as follows. The output signal of a given magnetic sensor on a stick-on meter can be expressed as a linear combination of the voltage output caused by the signal of interest

and the voltage output cause by the interfering signals existing in the

neighbouring circuits:

Two observations have been made when considering three magnetic field sensors on one stick- on meter:

Observation 1: Observation 2:

From these two experimental observations, an expression of the output signals which is independent of the interfering signals can be uncovered:

Extrapolating upon these observations made when considering three magnetic field sensors, an analogous expression of the output signals for n sensors which is independent of the interfering signals can be uncovered.

Experimental evidence shows that as long as the sensors are evenly placed, so that the middle sensor(s) is aligned to the center of the trace, the signal of interest is symmetric about the middle sensor(s) and the assumptions can be extrapolated. Figures 21A, 21B and 22, and Example 4 present the data and results used for the simulations ran which confirm the observations for 3 sensors and for n sensors. Suppose there are n output signals οϊ η magnetic sensors. Viewing these n output signals as entries of a vector in the n-dimensional vector space , we can consider the linear transformation φ: given by

then

Using the following notation:

Using the two observations made when considering three magnetic field sensors on one stick-on meter,

Indeed, expanding the left hand side it can be shown that

Thus, the left hand side of the equation becomes independent of the signals from the neighboring circuits for n circuits as well, and once again to not need to worry about interfering signals.

Thus, in the above embodiments of the electricity monitoring device containing three or more magnetic field sensors, the device may be configured, e.g., via components on the circuit board or via the computational unit, to subtract from a sensor output of the second magnetic field sensor the average sensor output between the first and third magnetic field sensors, if the sensitivity of the second magnetic field sensor is substantially parallel to and has the same sign as the sensitivity of the first and third magnetic field sensors. In certain embodiments, the device may include an inverting operational amplifier that takes in the output from the second sensor, and a summing amplifier that sums the output from the inverting operational amplifier with the output from the first and third sensors. The output from the implemented algorithm may then be a signal that is proportional to the electric current in the monitored conductive substrate and is less affected by the cross talk from interfering electromagnetic fields from, e.g., neighboring conductive substrates that are not being monitored by the particular device.

The algorithm may be implemented on the analog (voltage) signal generated by the sensors, or may be implemented on digital (numeric) data obtained by converting the analog signal from the sensors with digital-to-analog converters.

In some embodiments, the computational unit may be configured to sum a sensor output of the second magnetic field sensor with the average sensor output between the first and third magnetic field sensors, if the sensitivity of the second magnetic field sensor is substantially parallel to but has a negative sign as the sensitivity of the first and third magnetic field sensors.

This cross talk mitigation algorithm may alternatively be implemented off the device, e.g., on a remote computer system that is configured to communicate with the device.

The reduction in the effect of cross talk on the output signal of the electricity monitoring device may be determined by comparing the output signal of a device monitoring electricity usage in an electric circuit in the presence and absence of an interfering electromagnetic field. In some embodiments, the electricity monitoring device is configured to allow implementation of an algorithm that reduces the effect of cross talk by 60% or more, e.g., 70% or more, 80% or more, 90 % or more, 95% or more, and that reduces the effect of cross talk by 100% or less, e.g., 99% or less, 98% or less, 97% or less, 95% or less, including 90% or less. In some cases, the electricity monitoring device is configured to allow implementation of an algorithm that reduces the effect of cross talk by a range of 60 to 100%, e.g., 70 to 99%, 80 to 98%, including 90 to 97%. In some embodiments, the electricity monitoring device is configured to reduce the effect of cross talk by reducing a change in the output signal of the device caused by an interfering electromagnetic field by 60% or more, e.g., 70% or more, 80% or more, 90 % or more, 95% or more, and in some cases reduce the change by 100% or less, e.g., 99% or less, 98% or less, 97% or less, 95% or less, including 90% or less. In some embodiments, the electricity monitoring device is configured to reduce the effect of cross talk by reducing a change in the output signal of the device caused by an interfering electromagnetic field by a range of 60 to 100%, e.g., 70 to 99%, 80 to 98%, including 90 to 97%.

In some embodiments, the magnetic field sensor has a low intrinsic noise level. The noise level of the magnetic field sensor may be about 1 gauss (G) or less, e.g., about 100 mG or less, about 10 mG or less, about 1 mG or less, about 0.3 mG or less, and may be about 0.001 mG or more, e.g., about 0.005 mG or more, about 0.01 mG or more, 0.02 mG or more, about 0.05 mG or more, including about 0.01 mG or more. In some embodiments, the noise level of the magnetic field sensor may be in the range of about 0.001 mG to about 1 G, e.g., about 0.001 mG to about 0.1 G, about 0.005 mG to about 10 mG, about 0.01 mG to about 1 mG, including about 0.01 mG to about 0.1 mG.

In certain embodiments, the electricity monitoring device has a substantially flat outer surface that is configured to be positioned on the outer surface of the conductive substrate enclosure when the device is positioned to monitor electricity usage in an electric circuit containing the conductive substrate through which an electric current flows.

In some embodiments, the enclosure surrounding the conductive substrate through which the electric charge flows has a first side and a second side opposite the first side, and the electromagnetic field sensors in the electricity monitoring device are arranged such that when the electricity monitoring device is positioned on or proximal to the enclosure for monitoring electricity usage, the electromagnetic field sensors are closer to the first side than they are to the second side of the enclosure. In some embodiments, the electricity monitoring device does not encircle or substantially surround the conductive substrate. In some embodiments, the electricity monitoring device does not encircle or substantially surround the conductive substrate with a current transformer to monitor electricity usage in the electric circuit containing the conductive substrate. In certain embodiments, the electricity monitoring device is configured to monitor electricity usage in a conductive substrate without making galvanic contact (e.g., making direct physical contact) with the conductive substrate, as described herein.

In certain embodiments, the electricity monitoring device is configured to generate one or more device outputs at a frequency (i.e., sampling frequency) of 50 Hz or more, e.g., 80 Hz or more, 100 Hz or more, 150 Hz or more, 200 Hz or more, 1 kHz or more, 10 kHz or more, including 100 kHz or more, and may generate one or more device outputs at a frequency of 1 MHz or less, e.g., 100 kHz or less, 10 kHz or less, 1 kHz or less, 500 Hz or less, 250 Hz or less, 100 Hz or less, including 50 Hz or less. In some embodiments, the electricity monitoring device is configured to generate one or more device outputs at a sampling frequency in the range of 50 Hz to 1 MHz, e.g., 50 Hz to 100 kHz, 50 Hz to 10 kHz, including 100 Hz to 1 kHz.

The length and width of the electricity monitoring device may be any suitable length and width. In some cases, the length may be 0.01 inches or more, e.g., 0.05 inches or more, 0.1 inches or more, 0.5 inches or more, 0.8 inches or more, 1 inch or more, 2 inches or more or 3 inches or more, and may be 4 inches or less, e.g., 3 inches or less, 2 inches or less, 1.5 inches or less, 1 inch or less, 0.5 inches or less, including 0.1 inches or less. In some embodiments, the length of the electricity monitoring device may be in the range of 0.01 to 4 inches, e.g., 0.05 to 3 inches, 0.1 to 2 inches, including 0.5 to 1.5 inches. In some embodiments, the width of the electricity monitoring device may be 0.01 inches or more, e.g., 0.05 inches or more, 0.1 inches or more, 0.2 inches or more, 0.3 inches or more, 0.5 inches or more, or 1 inch or more, and may be 3 inches or less, e.g., 2 inches or less, 1 inch or less, 0.8 inches or less, 0.5 inches or less, including 0.1 inches or less. In some embodiments, the width may be in the range of 0.01 to 3 inches, e.g., 0.05 to 2 inches, 0.1 to 1 inches, including 0.2 to 0.8 inches.

In certain embodiments, the electricity monitoring device includes an attachment element that allows the device to be attached to and/or positioned appropriately on the surface of an enclosure surrounding a conductive element of an electric circuit. The device may be attached to the surface of the enclosure using any convenient attachment element, including adhesives, magnets, a clip, elastic band, an enclosing structure such as a strap or a ring, a hook-and-loop fastener, retaining frame, retaining enclosure, etc. The attachment element may be mechanically rigid or compliant. In some embodiments, the attachment element provides for consistent positioning of the electricity monitoring device relative to the conductive substrate in the enclosure. The consistent positioning may include consistent orientation of the axis or direction of sensitivity of the electromagnetic field sensors with respect to the direction of current flow in the conductive substrate, and consistent distance of the electromagnetic field sensors with respect to one or more points within the conductive substrate. In some embodiments, consistent positioning achieved by the attachment element obviates the need for, or reduces the frequency of, calibrating the electricity monitoring device upon installing the device at an electricity usage monitoring site.

In some embodiments, the electricity monitoring device 1100 includes an energy source unit 1160. Any convenient energy source may be used. Exemplary energy sources include, but are not limited to, a battery, a capacitor or supercapacitor, an electromagnetic energy harvester, and/or a power supply configured to connect to an external power source. The battery may be a rechargeable battery. The energy harvester may be configured to scavenge energy from electromagnetic field sensors installed in capacitors to power the present electricity monitoring device, e.g., power the device circuit components that condition the sensor outputs.

In certain embodiments, the electricity monitoring device 1100 comprises an outer case 1170 that encloses the rest of the electricity monitoring device. Any convenient container may be used as an outer case. In some embodiments, the outer case includes openings that, e.g., provide access to ports on the circuit board 1110 configured to transmit the device outputs and/or receive inputs to the device. In some embodiments, the outer case is substantially opening-free and the electricity monitoring device is configured with a wireless communication unit to transmit and/or receive data and/or software algorithms.

Also provided herein is a microelectromechanical system (MEMS) device having an electret-based self -powered current sensor for use on circuit-breaker panels and non-plug loads. In general terms, the MEMS device employs a long-lifetime source of electric energy that is coupled to a micromechanical structure that can measure electric currents and also produce output electrical energy that could power, e.g., a wireless radio or other devices. The MEMS device may contain an electrically conductive MEMS structure having a ferromagnetic structural component or an attached permanent magnet that can be placed near an alternating current (AC)- carrying conductor that induces vibratory motion of the component, causing a portion of the component to vary its distance from an electret that has been charged to a high surface potential. An electret is an electrical insulator (such as Teflon®) into which electric charge has been imbedded. It has been found that the embedded charge can remain in place for many decades while the electret is acting as a source of steady (DC) potential. A very large and stable electrical potential - thousands of volts - may exist just outside the electret. As a result of the vibratory motion relative to the electret of the MEMS structure, which might be a cantilever, a time- varying potential is induced in the structure. With proper placement near a current-carrying conductor, a MEMS element, such as a cantilever beam, fitted with small permanent magnets will be driven into substantial motion. This electric energy may be used to power a number of circuit elements including, but not restricted to, the following: a rectifying element that converts the time- varying potential to a steady potential; an energy storage element such as a rechargeable battery or a supercapacitor; a measuring element such as a GMR (giant magneto-resistance) sensor that measures the instantaneous magnetic field due to current flowing in a nearby conductive substrate; an analog-to-digital converter that produces a digital version of the amplitude of the measured current in the conductor; a radio that wirelessly transmits the value of the measured AC current; and a capacitive element that can be used to gauge the voltage of the electric conductor relative to its surroundings. In some embodiments, the MEMS device does not use power sources having relatively limited lifetimes or are subject to fatigue when driven excessively either mechanically or electrically, examples including mechanical-to-electrical power sources employing certain piezoelectric materials.

With reference to Fig. 20, a MEMS device may contain a MEMS element 2, and a ferromagnetic element 3 physically associated with the MEMS element, wherein the

ferromagnetic element is configured to induce motion to the MEMS element when the ferromagnetic element is coupled to an electromagnetic field generated by a time- varying current. The MEMS element may include a cantilever structure. The motion of the MEMS element induced by ferromagnetic element may be an oscillatory motion. The ferromagnetic element may be a permanent magnet attached to the MEMS element, or the ferromagnetic element may be embedded in the MEMS element. In some embodiments, the MEMS element further includes a conductive substrate 4 having a shape, and the device includes an electret 5, wherein the distance between the conductive substrate and the electret changes as a function of the motion of the MEMS element. The electret may generate an electric field. The distance between the conductive substrate and the electret may be such that the strength of the electret electric field experienced by the conductive substrate varies depending on the position conductive substrate relative to the electret. As the time- varying current causes the MEMS element to move e.g., to move periodically or to vibrate, a time-varying potential is induced in the MEMS element, e.g., in an electrode plated on the backside of the electret. Although the charge embedded in a well-designed electret may stay in place, a time-varying current flows external to the electret because the MEMS component (assumed to have electrically conductive elements) works intermittently when moving in the electric field produced by the electret. The device may further contain a rectifier circuit 7 that converts the time- varying potential of the MEMS element to a steady direct current (DC) voltage. In some embodiments, the device contains an energy storage component 8, such as a rechargeable battery or a supercapacitor, that can be used to store energy temporarily. In some embodiments, the device contains a switch 9 to connect the energy source to other devices. In some embodiments, the device contains additional components 10 such as a radio for wirelessly transmitting data for interpretation and/or storage, and/or circuitry for measuring properties of the nearby atmosphere, signaling, cameras, diagnostic devices, micro controller, analog-to-digital converter, etc. In some embodiments, the device contains an enclosure 6 that is optionally evacuated, that prevents particles from being attracted to the electret. This conductor might be in use to power electrical devices such as lamps or motors, or might consist of wires carrying currents that represent data.

To maximize the coupling between the moving element 2 and the electret 5, it may be advantageous to contour the electret so that the coupling between the electret and the MEMS element (such as a cantilever) is maximized where the MEMS element is at the extremity of its motion. Such shaping of the (usually polymeric) electret maximizes the capacitive coupling of the MEMS element and the electret at their closest approach.

It has been found that charge retention by an electret is maximized if the lateral boundaries of the electret are relatively far apart (loss of stored charge has been observed in certain electret devices whose finely patterned surfaces interact). In the present device, lateral electret boundaries can be located far apart to minimize decay of the stored charge.

Any suitable material may be used for the electret. The electret may be organic or inorganic. In some embodiment, an organic electret includes Teflon®, polyethylene, or Cytop®, etc. An inorganic electret may contain silicon nitrite, and/or silicon oxide, etc. In some instances, an organic electret may be coated in a very thin layer, e.g., nanometer layer, of

hexamethyldisilazane (HMDS), and HMDS on the silicon nitrite / silicon oxide may prevent moisture from penetrating. In some instances, for each MEMS element, a current voltage converter is employed to condition the power output of the sensors, e.g., an Analog-to-Digital converter.

In certain embodiments, the MEMS element-containing device is an energy-efficient device. The MEMS element may be passively coupled to the electromagnetic field produced by a conductive substrate passing an electric current, as described above. Thus in certain

embodiments, the MEMS element is a self -powered sensor and may not require an external source of power to function as a sensor.

In certain embodiments, the MEMS element-containing device is a fully integrated device containing the MEMS sensors and device circuit components, where the sensors are fully integrated into a single chip, and part of the chip is the MEMS sensors. The rest of the chip may be application- specific circuitry designed to convert the discrete components into complementary metal-oxide semiconductor (CMOS)-based components. The integrated device may be made in one process flow for the entire semiconductor wafer, e.g., die, and may be encapsulated into an integrated circuit packaging that may measure about 1 mm x about 1 mm. Integrated circuit packaging systems of interest include small-outline transistor (SOT), or small-outline package (SOP), such as, but not limited to, SOT236 and SOP8. In some instances, the size of the die will be in an order of 1 mm x 1 mm so it can be encapsulated in any suitable type of integrated circuit packaging. In some cases, where the device is employed on a circuit breaker panel, the space for most circuit breakers may be about 2 cm x 3 cm. The device may be mounted on a PCB to monitor power usage, and the size may be about 2 cm x 2.5 cm In some embodiments, where a single wire conductor is monitored, two electromagnetic field sensors may be employed, one on both sides of the PCB. The integrated circuit packaging of the device may be small enough such that it is smaller than the diameter of the wire.

In some cases, the whole device may be about the same size as the diameter of the wire, depending on the gage of the wire. Differently-sized mechanical enclosures can be made to accommodate for different gages of wire. In some instances, the sensor may occupy the majority part of the die. In some cases, 2/3 of the area of the die will be the MEMS sensor, and 1/3 of the area would be the circuitry. In some embodiments, the integrated MEMS device, as described above, may be fabricated on a single wafer, which may contain 100 or more, e.g., 1000 or more, integrated MEMS devices. In some instances, the cost for the integrated MEMS device is $1 or less, e.g., 10 cents or less.

The present MEMS device finds use as stick-on sensors for monitoring electricity usage, i.e., submetering) in conventional circuit breaker boxes in dwellings, offices or factories. The powering element (the nearby conductor 1 shown in Fig. 20) and measuring elements may be used separately in cases where measuring electric current is not of primary interest. For example, this MEMS deice may be used to power atmospheric sensors that can be mounted on overhead electric power lines whose current-carrying conductors are the powering elements (e.g., to measure concentrations of carbon dioxide, methane, or atmospheric particulate matter). The MEMS device also finds use in monitoring power consumption of non-plug loads in buildings (examples: elevators, HVAC systems, electric motors, dishwashers, washing machines, clothes driers, large ovens, etc.). In these applications the device may be placed near the cables supplying electric power to the non-plug load, or attached to the slack power cables supplying the non-plug load. The MEMS device also finds use in monitoring currents flowing in individual overhead or underground power transmission or distribution conductors operated by electric power utilities. In some cases, the monitoring can be done wirelessly.

METHODS

Methods of Using an Electricity Monitoring Device

Also provided herein is a method of monitoring electricity usage in an electric circuit. In general terms, the present method may include obtaining data that includes one or more device outputs from an electricity monitoring device, as described above, where the device is positioned on or proximal to the outer surface of an enclosure containing one or more conductive substrates through which an electric charge is flowing, and analyzing the obtained data to derive one or more measures of electricity usage. In certain embodiments, the device output contains a first set of values proportional to an amplitude of, and in phase with an oscillatory variation in, an electric current in the one or more conductive substrates. The first set of values may be derived from one or more outputs from a magnetic field sensor disposed in the electricity monitoring device, as described above. In certain embodiments, the device output contains a second set of values proportional to an electric potential difference and in phase with an oscillatory variation therein, in the one or more conductive substrates. The second set of values may be derived from one or more outputs from sensor having a capacitive pickup disposed in the electricity monitoring device, as described above.

By processing the data obtained from the electricity monitoring device, various measures of electricity usage by the electric circuit may be derived. The measures of electricity usage that may be derived include the amplitude, root mean square (RMS) and/or phase angle values of the electric current; the phase angle value of the electric potential difference in the one or more conductive substrates; the RMS value of the power; and the frequency spectra of the electric current and/or the electric potential difference for the electric circuit. In certain embodiments, the frequency spectra of the electric current and/or the electric potential difference for the electric circuit may be obtained for up to ½ of the sampling frequency of the electricity monitoring device. In some cases, the frequency spectrum may in turn provide the harmonics drawn by a load connected to the electric circuit.

In certain embodiments, the present method includes obtaining data containing the one or more device outputs from the electricity monitoring device at a frequency (i.e., sampling frequency) of 10 Hz or more, e.g., 50 Hz or more, 80 Hz or more, 100 Hz or more, 150 Hz or more, 200 Hz or more, 1 kHz or more, 10 kHz or more, including 100 kHz or more, and in some cases obtaining the data at a frequency of 1 MHz or less, e.g., 100 kHz or less, 10 kHz or less, 1 kHz or less, 500 Hz or less, 250 Hz or less, 100 Hz or less, including 50 Hz or less. In some embodiments, the present method includes obtaining data containing the one or more device outputs from the electricity monitoring device at a sampling frequency in the range of 10 Hz to 1 MHz, e.g., 50 Hz to 100 kHz, 80 Hz to 10 kHz, including 80 Hz to 1 kHz.

The processed data can include one or more measures of electricity usage at a frequency that is about the same and/or less than the frequency at which the data is obtained from the electricity monitoring device. In some cases, the frequency of the one or more measures of electricity usage (e.g., the RMS power, current, voltage, or any other suitable measure, as described above) is at 0.01 mHz or more, e.g., 0.05 mHz or more, 0.1 mHz or more, 0.5 mHz or more, 1 Hz or more, 5 Hz or more, 10 Hz or more, including 30 Hz or more, and in some cases the frequency is at 1,000 Hz or less, e.g., 500 Hz or less, 200 Hz or less, 100 Hz or less, 80 Hz or less, including 65 Hz or less. In some embodiments, the frequency of the one or more measures of electricity usage is in the range of 0.01 mHz to 1,000 Hz, e.g., 0.05 mHz to 500 Hz, 0.01 mHz to 100 Hz, including 0.01 mHz to 80 Hz.

The processing of data obtained from the electricity monitoring device may be done by any convenient method for deriving the various measures of electricity usage by the electric circuit monitored by the present electricity monitoring device. In certain embodiments, the electricity monitoring device contains two or more magnetic field sensors, each having one or more axes of sensitivity. With reference to Fig. 4, in some embodiments, two magnetic field sensors on an electricity monitoring device may be used to monitor electricity usage in a single- conductor wire (Fig. 4, left) or a zip cord (Fig. 4, right). In Fig. 4, the wires containing conductive substrates are shown in cross section, where the cross section is in a plane perpendicular to the direction of an electrical current flowing through the conductive substrates. In the case of a single-conductor wire, a first magnetic field sensor 4125 and a second magnetic field sensor 4126 are aligned along the wire's radial direction with a known separation distance of D (The sensors and device are shown in a sagittal profile, and thus can have a dimensions extending into and/or out of the plane of the figure). Each sensor measures the circumferential field and outputs a proportional voltage signal at a sensitivity of a (a priori). Assuming the voltage output of the sensor closer to the conductor is VI and the voltage output of the other sensor is V2, the current, I carried by the wire can be given by,

To measure electric current from a zip cord, two magnetic field sensors 4225, 4226 are placed on a centerline of the zip cord (Fig. 4, right). The centerline of the zip cord may be defined as a line that bisects a cross-section of the zip cord into two equal halves, where the cross-section is perpendicular to the direction of electrical current flowing through the conductive substrates and the centerline separates the two conductive substrates from each other. The sensors are vertically aligned, and are sensitive to the vertical component of the magnetic field produced by the two wires. The separation distance, A, between the two conductors can be determined by the gauge of the wire as a priori knowledge. Assuming the voltage output of the sensor 4225 closer to the conductor is V ₁ and the voltage output of the other sensor 4226 is V ₂ , the current / carried by a zip-cord can be solved from,

In some embodiments, the present method of monitoring electricity usage reduces or eliminates the influence of cross talk from interfering magnetic fields at the site of monitoring on the results of the analysis. The interfering magnetic field may be generated from a number of sources, such as an electric current flowing in a conductive substrate nearby that is not the intended target for monitoring electricity usage. In some cases, the electric circuit that is being monitored for electricity usage according to the present method is a subcircuit of an electrical wiring system. Thus, in some instances, the electric circuit that is being monitored for electricity usage is a branch circuit of an electrical wiring system. In such cases, the interfering

electromagnetic field may be generated by one or more other branch circuits in the electrical wiring system, where the other branch circuits are not the intended target for monitoring electricity usage by that particular electricity monitoring device. This may be the case, for instance, if the electricity usage for the branch circuit is being monitored at a circuit breaker panel that includes multiple circuit breakers for multiple branch circuits, including the circuit breaker for the branch circuit of interest as well as circuit breakers for the other branch circuits that are not the target of monitoring by that particular electricity monitoring device.

An implementation of the present method reduces the effect of cross talk by reducing the change in the electricity monitoring device output caused by an interfering electromagnetic field by 60% or more, e.g., 70% or more, 80% or more, 90 % or more, 95% or more, and in some cases reducing by 100% or less, e.g., 99% or less, 98% or less, 97% or less, 95% or less, including 90% or less. In certain embodiments, the present method reduces the effect of cross talk by reducing the change in the electricity monitoring device output caused by an interfering electromagnetic field by a range of 60 to 100%, e.g., 70 to 99%, 80 to 98%, including 90 to 97%. In some embodiments, the present method does not substantially reduce the effect of cross talk from an interfering magnetic field.

As described above, in certain embodiments, the effect of cross talk is reduced by having on-board circuitry in the electricity monitoring device, and/or the arrangement of one or more electromagnetic sensors relative to each other and/or relative to the conductive substrate, that are together configured to reduce or minimize the effect of cross talk from an interfering

electromagnetic field. In some embodiments, the cross talk mitigation algorithm is implemented off the electricity monitoring device, e.g., on a computer system configured to communicate with the electricity monitoring device. Thus, in some embodiments, the electricity monitoring device is disposed on the outer surface of the enclosure in a manner sufficient to implement the onboard cross talk mitigation technique, on and/or off the electricity monitoring device. In some embodiments, the method includes positioning an electricity monitoring device on the outer surface of the enclosure in a manner sufficient to implement the on-board cross talk mitigation technique. In some embodiments, the electricity monitoring device is configured such that when the device is positioned on the outer surface of the enclosure, the distance between the electromagnetic field sensors and the conductive substrate is 100 mm or less, e.g., 10 mm or less, 5 mm or less, 2 mm or less, 1 mm or less, 0.5 mm or less, including 0.3 mm or less, and may be 0.1 mm or more, e.g., 0.2 mm or more, 0.5 mm or more, 1 mm or more, 3 mm or more, 5 mm or more, including 10 mm or more. In some embodiments, the distance between the

electromagnetic field sensors and the conductive substrate when the electricity monitoring device is positioned on the outer surface of the enclosure is in the range of 0.1 mm to 100 mm, e.g., 0.2 mm to 10 mm, including 0.5 mm to 2 mm. An exemplary cross talk mitigation process includes a multi-point sensing technique, such as that implemented by an electricity monitoring device with three magnetic field sensors described above. Thus, in some embodiments, the electricity monitoring device is disposed on the outer surface of the enclosure in a manner sufficient to implement the multi-point sensing technique. In some embodiments, the method includes positioning an electricity monitoring device on the outer surface of the enclosure in a manner sufficient to implement the multi-point sensing technique.

In certain embodiments, the effect of cross talk is reduced by applying a cross talk mitigation algorithm on the one or more outputs from the electricity monitoring device. In some embodiments, the cross talk mitigation is part of a calibration process. As described above, the output from the electromagnetic field sensors and/or the output from the electricity monitoring device may be proportional to the actual current amplitude or voltage of the electric circuit monitored by the electricity monitoring device. Thus, in some embodiments, the method includes calibrating the electricity monitoring device to determine one or more calibration values, such as constants of proportionality, for the one or more device outputs of the electricity monitoring device, and the analyzing includes deriving the one or more measures of electricity usage using the one or more calibration values, e.g., constants of proportionality.

In some cases, the calibration values and/or relationship between the measured and actual properties of the electric circuit are known a priori, and the calibration process may be built into the electricity monitoring device, e.g., by providing hardware components in the computation unit that are configured to apply the calibration values to the sensor outputs, or processed forms thereof, and/or by providing attachment means to ensure consistent positioning of the sensors relative to the conductive substrate, etc.

In some embodiments, the calibrating includes applying a calibration algorithm to the electricity monitoring device output. The calibrating step may include any number of calibrating techniques that are suitable for deriving one or more measures of electricity usage from the data obtained from the present electricity monitoring device. In certain embodiments, the calibrating step includes determining the current amplitude and/or phase, and/or the electric potential difference and/or phase of a load current drawn by the electric circuit that is being monitored. In some cases, these properties of the load current may be known because a load current of known current amplitude and/or phase, and/or known electric potential difference and/or phase is drawn by the electric circuit, e.g., by providing a reference load. In some embodiments, the calibrating may be automated/semi-automated, and may not require actively providing a reference load to the electric circuit. In some embodiments, the electric circuit that is being monitored for electricity usage by the present method is a branch circuit of an electrical wiring system, as described above, and the properties of the load current are measured at a main meter for the electrical wiring system. The main meter may be a utility billing meter installed on the electrical wiring system, may be another electricity monitoring device that is monitoring electricity usage of the electrical wiring system, or any other suitable meter for the electrical wiring system. In some embodiments, the main meter may be the electricity monitoring device 1100 described above in connection with FIG. 1. The main meter may provide the actual values of the properties of the electric circuit. The main meter may be directly connected to the electrical wiring system but may not be monitoring electricity usage at the individual branch circuits. The branch circuits may be monitored individually, e.g., by an electricity monitoring device, to generate data containing values representing the electric current amplitude and/or phase, and/or the electric potential difference and/or phase for the individual branch circuits. A comparison of the pattern of electrical change at the main meter with the patterns of change at each of the branch circuit may reveal the branch circuit that is responsible for drawing the load current that was measured at the main meter. Unique identification of a branch circuit as correlating with the load current measured at the main meter among all the other branch circuits may allow designation of the load current as the reference load current for the branch circuit. The calibration values of the electricity monitoring device may then be obtained by comparing the values of the current amplitude and/or phase, and/or electric potential difference and/or phase for the reference load current measured at the main meter with those measured at the electricity monitoring device monitoring the branch circuit.

In certain embodiments, the calibration algorithm applied to derive the actual values of the properties of the electric circuit that is being monitored is based on a model describing the relationship between the electric current in the conductive substrate and the output from the sensor and/or device. In some cases, where there are multiple electric circuits being monitored by multiple electricity monitoring devices, and where the electromagnetic field generated by each electric circuit can potentially contribute to the output of all devices, the measured output of each device is modeled as a linear combination of contributions from all the electric circuits. In such cases, a sensitivity matrix, indicating the degree of contribution by each electric circuit to the output of each device, provides the calibration values for converting the measured values to the actual current values. The elements of the sensitivity matrix may be determined by measuring the response of all the devices when a reference load of known electrical properties is applied individually to each circuit, as described herein.

An exemplary implementation of the calibrating step is described below. For proximity- based current measurement, such as measurements obtained using the present electricity monitoring device, the output of a linear sensor, such as a magnetoresistive sensor, TMR, GMI a Hall Effect sensor, or any other similar types of sensors, can be represented as a linear combination of all its inputs and can be expressed in following phasor form,

where V) and are the magnitude and the phase angle of the j sensor's voltage output, n is the number of total circuits to be measured, αj,k is the sensitivity of the j ^th sensor with respect to the k ^th circuit, and lastly, I _k and θ _k are the magnitude and the phase angle of the current in the k ^th circuit. Therefore, if m sensors are installed to measure currents in n circuits, the linear equation system can be described as,

Using the voltage output of each sensor, the current information can be extracted by solving this system of linear equations. Note that, to solve these equations requires the rank of the sensing matrix in the above equation to be left invertible. This can be satisfied by placing m>=n sensors on the surface of the breaker panel. The positions of the sensors will significantly affect the condition number of the sensitivity matrix. In practice, an ill-conditioned sensing matrix will result in amplified errors in the solution to the current values. For implementation on circuit breaker panels, the sensor arrangement on the circuit breaker panel may be sufficient to solve the system of linear equations. In some instances, the sensor arrangement may place m sensors on the location of m breakers.

In this method, after the sensors are installed, the sensing matrix can be obtained using calibration load. The calibration load is performed by consecutively plugging the calibration load to every circuit. The magnitudes and phase angles of the voltage outputs for each sensor is recorded. The entry αj,k of the sensing matrix can then be obtained through the following equation,

where index i, j, 0, t represent the index of sensor, index of circuit, the state before calibration and the state after calibration, respectively, and I _CL is the current drawn by the calibration load. The calibration does not require the knowledge of the currents carried by the conductors at the time of calibration. The calibration load-switching process can be automated such that it can be performed in a very short period.

In practice, a calibration load with a wireless transmitter can be built. When plugged into an outlet of a circuit, the calibration load may draw a known amount of current, and in some cases may automatically send a signal to a computer system configured to receive the signal, e.g., a data acquisition system. The computer system may register the changes of both magnitudes and phases of each sensor and apply the above equation to calculate the

corresponding column of the sensing matrix. The same procedure may be iterated for all circuits to complete the sensing matrix.

A calibration algorithm, including those described above, may be performed using a computer system, such as described above, or may be performed substantially by the electricity monitoring devices. Thus, in some embodiments, the electricity monitoring device may include a computational unit, as described above, configured to receive data containing the actual property values of a reference load, compare the actual property values with the values of the reference load measured by the device, and derive a calibration value, e.g., a proportionality value, based on the comparison, where the derived calibration values can be applied to the device outputs before the device outputs are transmitted to a user. As described above, the computational unit may contain any suitable component for use on the present electricity monitoring device, such as a processor, differential amplifiers, operational amplifiers, a rectifying element, analog-to-digital converter, a current-to-voltage converter, an inverter, a low-pass filter, a resistor, etc. The data received by the electricity monitoring device may include time stamp data that indicates when the reference load was drawn from the circuit being monitored by the device, thereby allowing the device to align the actual property values of the reference load with the measured values. In some cases, the electricity monitoring devices may be configured such that multiple electricity monitoring devices communicate with each other to implement a calibration algorithm based on a model for linear combination of contributions by multiple circuits on the measurement at each circuit, as described above.

The electric circuit monitored by the present method may include any suitable electric circuit components where electricity usage may be monitored. The electric circuit may include, e.g., a circuit breaker on a breaker panel, a single conductor wire, a zip wire, an overhead wire, power line, electrical relay, electrical panel, junction box, fuse box, raceway, etc. A "zip wire" as used herein, includes a cable containing two insulated conductors. A "breaker" or "circuit breaker" includes a control module connected to a conductor where the breaker contains an electric relay or switch configured to interrupt a flow of current through the conductor when the current flowing through the conductor exceeds some predetermined level. A circuit breaker may be an arc fault circuit interrupter (AFCI), earth leakage circuit breaker (ELCB), residual-current device (RCD) / residual-current circuit breaker (RCCB), or any other suitable type of circuit breaker. The circuit breaker may be present on a breaker panel containing a plurality of circuit breakers, where each circuit breaker serves a branch circuit of an electrical wiring system that contains multiple branch circuits.

In some embodiments, the method includes positioning the electricity monitoring device on the outer surface of the enclosure. Installation of the present device may be achieved in any convenient manner. As the present method and device do not require direct access to the conductive substrate, the device may be positioned at any convenient location along an electrical wiring system where the sensors can be placed close enough to the conductive substrate to detect the electromagnetic fields produced by the conductive substrate in an enclosure and generate a reliable output. Thus, installation of the device may not require an electrician or any other individual specialized in handling the electrical wiring system.

The present method of monitoring electricity usage can be a sensitive method of monitoring electricity usage. In some embodiments, the method detects electrical current usage of 1000 amperes or less, e.g., 100 amperes or less, 10 amperes or less, 5 amperes or less, 1 ampere or less, 0.5 ampere or less, including 0.2 amperes or less, and may detect electrical usage of 0.01 amperes or more, e.g., 0.05 amperes or more, including 0.1 amperes or more. In some embodiments, the method detects electrical current usage in a range of 0.01 to 1000 amperes, e.g., 0.01 to 100 amperes. In some embodiments, the minimum electrical current usage detected by the present method is in the range of 0.01 to 5 amperes, e.g., 0.01 to 1 ampere, including 0.05 to 0.5 amperes. In some embodiments, the method detects electrical current usage of 10,000 W or less, e.g., 5,000W or less, 1,000 W or less, 100 W or less, 50 W or less, 20 W or less, 10 W or less, including 1 W or less, and may detect electrical usage of 1 W or more, e.g., 2 W or more, 5 W or more, 10 W or more, 20 W or more, 50 W or more, 100 W or more, 1,000 W or more, including 10,000 W or more. In some embodiments, the method detects electrical current usage in a range of 1 to 10,000 W, e.g., 5 to 5,000 W. In some embodiments, the minimum electrical current usage detected by the present method may be 1,000 W or less, e.g., 500 W or less, 200 W or less, 100 W or less, 50 W or less, 20 W or less, 10 W or less, 1 W or less, 300 mW or less, 100 mW or less, 50 mW or less, 20 mW or less, including 10 mW or less, and may be 1 mW or more, e.g., 5 mW or more, 10 mW or more, 50 mW or more, 100 mW or more, 500 mW or more, 1 W or more, 3 W or more, 5 W or more, 10 W or more, 20 W or more, 50 W or more, 100 W or more, including 1,000 W or more. In some embodiments, the minimum electrical current usage detected by the present method is in the range of 1 mW to 500 W, e.g., 5 mW to 200 W, 10 mW to 100 W, including 100 mW to 50 W.

Method of Monitoring Electricity Usage in a Building

Also provided herein is a method of monitoring electricity usage in a building to provide a diagnosis of electricity use for the building, where the diagnosis may be used to improve the performance of the building in terms of electricity use. In general terms, the electricity usage pattern for a building that is to be monitored is predicted for a time interval using an appropriate predictive model. The predicted electricity usage is then compared with the actual electricity usage measured for the predicted interval and the difference between the two is calculated to determine a deviation in electricity usage from an expected usage for the predicted time interval. The deviation in electricity usage is then analyzed to provide a diagnosis of electricity use for the building. In general, a larger magnitude of the deviation may indicate an unexpected electrical problem in the building, which may need to be addressed.

In some cases, the deviation in electrical usage is a residual of electricity usage obtained by subtracting the predicted electricity usage from the actual electricity usage measured. The deviation in electrical usage may be any other suitable measure of difference in the predicted and actual electricity usage, such as a fraction, normalized residual, etc.

The predicted time interval for electricity usage may be any suitable time interval. The predicted time interval may be chosen so as that the error between the predicted value and the actual value is below a threshold error level. The error may be calculated using any suitable loss function, including, but not limited to, mean absolute percent error (MAPE), mean absolute deviation or error (MAD or MAE), root mean squared error (RMSE). In some embodiments, the predicted time interval is an interval that produces an error that is 50% or less, e.g., 40% or less, 30% or less, 20% or less, 10% or less, including 5% or less. In some embodiments, the predicted time interval is an interval that produces an error in the range of 0 to 50%, e.g., 1 to 40%, 2 to 30%, 3 to 20%, 4 to 15%, including 4 to 10%. The predicted time interval may be 5 minutes or more, e.g., 10 minutes or more, 20 minutes or more, 30 minutes or more, 45 minutes or more, 1 hour or more, 6 hours or more, 12 hours or more, 24 hours or more, 2 days or more, including 5 days or more, and may be 1 month or less, e.g., 2 weeks or less, 1 week or less, 5 days or less, 3 days or less, 24 hours or less, 10 hours or less, 5 hours or less, 3 hours or less, including 90 minutes or less. In some embodiments, the predicted time interval is in the range of 5 minutes to 1 month, e.g., 5 minutes to 2 weeks, 10 minutes to 1 week, 10 minutes to 3 days, 10 minutes to 24 hours, 10 minutes to 10 hours, 20 minutes to 5 hours, 30 minutes to 3 hours, including 45 minutes to 90 minutes.

The predictive model used to make a prediction about future electrical usage may be any suitable model. In some embodiments, the predictive model is generated based on de novo estimation of electrical usage based on information available at the time of prediction. In some cases, the information available may include the types and number of electrical appliances connected to the electrical wiring system of the building, environmental conditions such as outside temperature, the time of day, and/or scheduled electrical usage events.

In some embodiments, the predictive model is generated by using historical data of electricity usage, which may indicate a pattern of electrical usage for a known period of time. The pattern of electricity usage, as well as other potentially relevant data, such as outside temperature, is fed into a model for predicting the electricity usage for a future time interval. The model may be any suitable model for predicting the electricity usage for a future time interval. In some embodiments, the model is based on a Seasonal and Trend decomposition based on Loess (STL) algorithm, as described in, e.g., Cleveland et al., 1990. Journal of Official Statistics 6 (1), 3-33. Other suitable models include, but are not limited to, a General Linear Abstraction of Seasonality weighted moving average filter, Henderson weighted moving average filter, or a Kalman filter. The prediction is then compared with the actual electrical usage for the same time interval as the predicted time interval, and the difference between the two is recorded as the deviation, e.g., residual, of electricity usage. The process is repeated at each subsequent time points.

The time interval used to collect the historical data of electricity usage may vary, and may be 1 day or more, e.g., 3 days or more, 5 days or more, 1 week or more, 2 weeks or more, 1 month or more, 2 months or more, 3 months or more, 6 months or more, 12 months or more, 2 years or more, and may be 5 years or less, e.g., 4 years or less, 3 years or less, 2 years or less, 1 year or less, 9 months or less, 5 months or less, 2 months or less, 1 month or less, including 2 weeks or less. In some embodiments, the time interval used to collect the historical data of electricity usage may be in the range of 1 day to 5 years, e.g., 1 day to 3 years, 3 days to 1 year, 3 days to 9 months, 5 days to 5 months, 5 days to 2 months, including 1 week to 1 month.

The deviation, e.g., residual values may then be used to diagnose whether there is an unexpected change in the pattern of energy use in the building during any given time interval. In some embodiments, a moving average of the deviations, e.g., residuals is calculated over a rolling window and the moving average is compared with a threshold value or control limit. If the moving average extends beyond the control limit, the time point is flagged as "out of control," potentially indicating an electrical usage problem in the building. In some

embodiments, the deviation, e.g., residuals, over a rolling window is ordered to provide a 99 ^th percentile value of the deviation, e.g., residual, above which a deviation, e.g., residual, value determined for a time point at the end of a time interval is flagged as "hard to predict," potentially indicating an electrical usage problem in the building. In certain embodiments, the combination of the moving average and the 99 ^th percentile value is used to identify a potential electrical usage problem in the building.

The time interval for the rolling window may vary, and may be 1 day or more, e.g., 3 days or more, 5 days or more, 7 days or more, 10 days or more, 2 weeks or more, or 3 weeks or more, and may be 10 weeks or less, e.g., 8 weeks or less, 6 weeks or less, 5 weeks or less, 4 weeks or less, including 3 weeks or less. In certain embodiments, the time interval for the rolling window may be in the range of 1 day to 10 weeks, e.g., 3 days to 7 weeks, 1 week to 5 weeks, including 1 week to 3 weeks.

Once a potential electrical usage problem in the building is identified, a more detailed analysis of the energy usage data can be performed to determine if the potential problem is an actual problem, and/or which part of the wiring circuit and/or which electrical appliance was responsible for the problem in electrical usage. In some embodiments, in addition to the main meter monitoring electrical usage in the entire wiring system of the building, each branch circuit of the wiring system may be individually monitored, e.g., using an electricity monitoring device as described herein, to provide electricity usage for each branch circuit. The main meter may be a utility billing meter installed on the electrical wiring system, may be an electricity monitoring device that is monitoring electricity usage of the electrical wiring system, or any other suitable meter for the electrical wiring system.

Once the problem of electricity usage has been identified and localized to a source, actions may be taken to resolve the problem. In some cases, a defective appliance may be the source of the aberrant electricity use, and the method may include replacing, repairing or upgrading the electrical appliance. In some embodiments, the building may undergo a commissioning process, including retrocommissioning or recommissioning to improve electricity usage in the building. Resolution of the problem may be reflected in reduction in the magnitude of the deviation, e.g., residual, of electricity usage and/or the frequency with which a time point is flagged as being out of control and/or hard to predict.

In certain embodiments, a report, chart, and/or notification are generated based on the diagnosis.

Any suitable part of the present method of monitoring electricity usage in a building to provide a diagnosis of electricity use can be performed on a computer system configured to implement the method. The computer system may include any arrangement of components as is commonly used in the art. The computer system may include a memory, a processor, input and ouput devices, a network interface, storage devices, power sources, and the like. The computer system may also include a wireless communications unit. The memory or storage device may be configured to store instructions that enable the processor to implement any part of the present method of monitoring electricity usage in a building by processing and executing the instructions stored in the memory or storage device.

In some embodiments, a system includes a user interface, such as a graphical user interface (GUI), that is adapted or configured to receive input from a user, and to execute any part of one or more of the methods as described herein. In some embodiments, a GUI is configured to display data or information to a user. The system may include any suitable application program interfaces (API) written in any suitable programming language, e.g., Java®, C, C++, for controlling the GUI, or any other devices included in the system. Also provided herein is a non-transitory computer-readable storage medium containing instructions, executable by at least one processing device, that when executed cause the processing device to perform any part of the present method of monitoring electricity usage in a building.

SYSTEMS

Provided herein is a system for monitoring electricity usage in an electric circuit, where the system includes one or more electricity monitoring devices, as described herein, and a computer system that is configured to receive a device output from the one or more electricity monitoring devices. In some embodiments, the system is configured to monitor electricity usage from an electrical wiring system containing a plurality of branch circuits, where at least some of branch circuits are monitored by at least one of a plurality of electricity monitoring devices. In some cases, all the branch circuits are monitored by at least one electricity monitoring device. The computer system may also be configured to communicate with a main meter that monitors electricity usage for the whole electrical wiring system, thereby obtaining information that would be useful to calibrate the individual electricity monitoring devices, as described herein. In some cases, communication between the computer system and the electricity monitoring devices, as well as between the electricity monitoring devices may be by wireless communication.

In some embodiments, the various components of the system, as described above are included with the electricity monitoring device as a single unit, e.g., by providing the

components on a common substrate as the electricity monitoring device, such as on the circuit board of the electricity monitoring device, and/or providing the components in a common container or enclosure.

A computer system for implementing any part of the present method of monitoring electricity usage may include any arrangement of components as is commonly used in the art. The computer system may include a memory, a processor, input and ouput devices, a network interface, storage devices, power sources, data acquisition (DAQ) interfaces, and the like. The computer system may also include a wireless communications unit. The memory or storage device may be configured to store instructions that enable the processor to implement any part of the present method of monitoring electricity usage by processing and executing the instructions stored in the memory or storage device. The memory can be any form of memory device, for example, volatile or non- volatile memory, solid state storage devices, magnetic devices, etc. In certain aspects, the memory includes a non-transitory storage medium (e.g., a storage medium that is not a transitory wave or signal). The processor can comprise more than one distinct processing device, for example to handle different functions within the system. Input device receives input data and can include, for example, a keyboard, a pointer device such as a pen-like device or a mouse, audio receiving device for voice controlled activation such as a microphone, data receiver or antenna such as a modem or wireless data adaptor, data acquisition card, etc. Input data can come from different sources, for example keyboard instructions in conjunction with data received via a network.

The output device produces or generates output data and can include, for example, a display device or monitor in which case output data is visual, a printer in which case output data is printed, a port, a peripheral component adaptor, a data transmitter or antenna such as a modem or wireless network adaptor, etc. Output data can be distinct and derived from different output devices, for example a visual display on a monitor in conjunction with data transmitted to a network. A user can view data output, or an interpretation of the data output, on, for example, a monitor or using a printer. The storage device can be any form of data or information storage means, for example, volatile or non- volatile memory, solid state storage devices, magnetic devices, etc.

In some cases, the computer system is a personal computer, a server, a router, a network PC, a peer device, or other common network node, and typically includes many or all of the elements described above. In some embodiments, the computer system is a server, e.g., a remote server, that may be part of a local area network (LAN) or a wide area network (WAN), and/or may also include other networks such as a personal area network (PAN).

In some embodiments, a system includes a user interface, such as a graphical user interface (GUI), that is adapted or configured to receive input from a user, and to execute any part of one or more of the methods as described herein. In some embodiments, a GUI is configured to display data or information to a user. The system may include any suitable application program interfaces (API) written in any suitable programming language, e.g., Java®, C, C++, for controlling the GUI, or any other devices included in the system.

Also provided herein is a non-transitory computer-readable storage medium containing instructions, executable by at least one processing device, that when executed cause the processing device to perform any part of the present method of monitoring electricity usage, as described above.

UTILITY

The present device and system, and methods of use thereof find use in many situations where it is desirable to monitor electricity usage in an electric circuit, including an electric circuit that is part of a building's wiring system. As described above, the present device and system, and methods of use thereof monitor electricity usage on various surfaces of enclosures surrounding a conductive substrate, such as a circuit breaker, single-conductor wire, zip cord, overhead or underground power lines, etc. The submetering ability provided by the present device and system, and methods of use thereof, may be applied to any suitable electrical wiring systems. In some embodiments, the electrical wiring system is in a building, e.g., an office building, factory, residential building, commercial building, etc. The device and system, and methods of use thereof allow measurement of power consumed by loads on individual circuit breakers in an electrical wiring system. Power consumed by non-plug loads (e.g., elevators, heating ventilation air-conditioning (HVAC) systems, electric motors) may also be monitored by placing the present device near cables supplying electric power to the non-plug load. Currents flowing in individual overhead or underground power transmission or distribution conductors operated by electric power utilities may also be monitored using the present device and system, and methods of use thereof.

In some embodiments, the measure of electricity usage obtained from the present electricity monitoring device and system may be compared with reference measures of electricity usage representing a usage pattern of known origin. In comparing the measured values with the reference values, it may be possible to determine if the measured values are the same as, or similar to the reference values. In some embodiments, the reference measure is a known measure of electricity usage of an electrical appliance. The electrical appliance may be any suitable electrical appliance for monitoring electricity usage using the present device, including, but not limited to a heating ventilation air-conditioning (HVAC) unit, refrigerator, electric motor, elevator, freezer, toaster, lamp, computer, light fixture, electric stove, electric oven, television, washing machine, dryer, dishwasher, adapters, etc. In some embodiments, each electrical appliance may have a signature pattern of electricity usage that is distinguishable from other appliances and allows the appliance to be identified in a measured electricity usage pattern. In some cases, an electrical appliance may be characterized by reference measures for the amplitude, Root Mean Square (RMS) and phase values of the current, phase value of the voltage, RMS value of power, the spectra of current or voltage, or a combination thereof. In some cases, the reference measures for an electrical appliance may be determined by knowing when the electrical appliance is turned on or off to alter the load on an electric circuit that is being monitored, e.g., by a method as described herein. In some cases, the turning on or off of the electrical appliance is done manually, or is done automatically, e.g., by a scheduling mechanism or a timer.

The comparison may in some cases indicate a deviation from expectation. In some embodiments, the reference measure is derived from a historical record of electricity usage pattern for the electric circuit. The historical record may be used to make a prediction about the electrical usage in the near future, thereby providing a reference measure of electricity usage. A comparison of the actual measure or electricity usage with the reference measure of electricity usage may reveal a deviation from expectation indicating a problem in the electric circuit. In certain embodiments, the deviation may indicate an electric appliance that has stopped working or is not working properly, or may not work properly in the near future. In some embodiments, the deviation may indicate an electric appliance that fails to turn on or off at a scheduled time.

The present device and system, and methods of use thereof also find use in diagnosing and improving electricity usage in a building. In certain embodiments, the method for diagnosing and improving electricity usage in a building may find use in identifying inefficiencies in electricity use in a building and remedy the identified inefficiencies. In certain embodiments, the method for diagnosing and improving electricity usage in a building may provide rapid and accurate monitoring capabilities for building operation and maintenance. In certain

embodiments, building commissioning and relrocommissioning programs may benefit from the diagnostic information obtained from the present device and system, and methods of use thereof. In certain embodiments, the method for diagnosing and improving electricity usage in a building may also allow automation of procedures that are presently carried manually and may speed up the diagnosis of building electricity use.

KITS

Also provided herein is a kit that finds use in implementing the present method of monitoring electricity usage. The kit may contain one or more electricity monitoring devices, as described herein, and instructions for monitoring electricity usage in an electric circuit by performing the method described herein using the one or more electricity monitoring devices.

The instructions for practicing the subject methods are generally recorded on a suitable recording medium. For example, the instructions may be printed on a substrate, such as paper or plastic, etc. As such, the instructions may be present in the kits as a package insert, in the labeling of the container of the kit or components thereof (i.e., associated with the packaging or subpackaging) etc. In other embodiments, the instructions are present as an electronic storage data file present on a suitable computer readable storage medium, e.g. CD-ROM, digital versatile disc (DVD), flash drive, Blue-ray Disc™ etc. In yet other embodiments, the actual instructions are not present in the kit, but methods for obtaining the instructions from a remote source, e.g. via the internet, are provided. An example of this embodiment is a kit that includes a web address where the instructions can be viewed and/or from which the instructions can be downloaded. As with the instructions, the methods for obtaining the instructions are recorded on a suitable substrate.

In some embodiments, the present kit includes packaging suitable for holding the electricity monitoring devices. The packaging may be made of any suitable material, such as plastic, cardboard, paper, etc. The packaging may be disposable packaging, or may be reusable packaging.

In some embodiments, the kit includes an attachment element for positioning the electricity monitoring devices to an enclosure surrounding a conductive substrate, as described above. In some embodiments, the attachment element includes an adaptor element configured to hold a plurality of electricity monitoring devices such that each electricity monitoring device aligns to a position corresponding to a circuit breaker on a circuit breaker panel when the adaptor holding the electricity monitoring devices is positioned on the circuit breaker panel.

Components of a subject kit can be in separate containers; or can be combined in a single container.

The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior invention. EXAMPLES

As can be appreciated from the disclosure provided above, the present disclosure has a wide variety of applications. Accordingly, the following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use embodiments of the present disclosure, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Those of skill in the art will readily recognize a variety of noncritical parameters that could be changed or modified to yield essentially similar results. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, dimensions, etc.) but some experimental errors and deviations should be accounted for

EXAMPLE 1: VOLTAGE SENSING USING A STICK-ON VOLTAGE SENSOR

A conductive plate was used as a capacitive pickup for voltage measurement. An analog frontend circuit (Fig. 2) was used to convert this electric current into a voltage output that was not only proportional in magnitude, but also synchronized in phase with the measured voltage. Fig. 3 shows the voltage output of the stick-on voltage sensor vs. the line voltage being measured.

Figure 3. Experimental result showing voltage output of the stick-on voltage sensor vs. the line voltage.

EXAMPLE 2: MEASURING ELECTRIC CURRENT AND VOLTAGE IN BREAKER CIRCUITS

Significant magnetic field ( 10mG - 10G) and electric field can be detected on the surface of a circuit breaker panel. The waveforms of electric current and voltage in all breaker circuits were accurately measured from these electromagnetic fields on the outside of the panel.

Location for sensor placement: The sensitivity of the magnetic field sensors to the electric current depends on the distance between the sensor and the conductor. The larger this distance, the smaller is the sensitivity. Fig. 5 shows the internal structure of a thermal-magnetic circuit breaker. The current flow path is highlighted. The location where the current path is closest to the circuit breaker panel was identified as the optimal location for sensor placement.

Figure 5. The internal structure of a thermal magnetic breaker. EXAMPLE 3: MULTI-POINT SENSING ON A CIRCUIT BREAKER PANEL

As shown in Fig. 6, the multi-point sensing technique employed three magnetic sensors for one stick-on meter. This technique was based on two observations on the distribution of the magnetic field on the breaker panel.

Figure 6. Illustration of multi-point sensing topology for interference mitigation.

Observation 1: It was found that for most of the circuit breakers, the current flow path is constrained in the Y-Z plane, as indicated in Fig. 6. The resulting magnetic field in the x- direction was confirmed by both finite element method (FEM) simulation (Fig. 7, left) and experimental results (Fig. 7, right) to be symmetric with respect to the Y-Z plane. Therefore, the voltage outputs of sensor 1 and sensor 3 as a result of signal 2 in Fig. 6 were exactly the same.

Figure 7. Simulation and experimental results of the x-component of the magnetic field, Bx produced by the current in circuit 2.

Observation 2: The gradient of the magnetic field produced by signals from neighboring circuits (signal 1 and signal 3 in this example) was confirmed by both experimental results (Fig. 8, right) and FEM modeling (Fig. 8, left) to be very close to a constant at sensor 2.

Figure 8. Simulation and experimental results of the x-component of the gradient of the magnetic field along the x-direction, Bxx, produced by the current in circuit 1.

Fig. 9 shows the experimental results of applying the signal deconvolution algorithm, as described herein and derived from the two observations, in mitigating the cross talk from the current carried by the neighboring circuits using multi-point sensing with three magnetic field sensors. In this experiment, a stick-on meter with three magnetic field sensors (See Fig. 9 left) was installed on a second circuit breaker labeled as Breaker B in Fig. 9, right. During the experiment, the load on circuit B was first switched on for 30 seconds. Within those 30 seconds, an interfering load on circuit A was switched on for about 15 seconds to generate a cross talk signal. It was confirmed that with the signal deconvolution algorithm, the corrected measurement was not affected by the cross talk, and only responded to the signal in circuit B (Fig. 10). 94% of cross talk was eliminated by the signal deconvolution algorithm.

Figure 9. Experimental setup for testing the noise mitigation algorithm.

Figure 10. Measurement of electricity use by the stick-on meter with three magnetic field sensors, with and without using the noise mitigation algorithm. EXAMPLE 4: GENERALIZATION OF MULTI-POINT SENSING TECHNIQUE TO N SENSORS

Simulations were ran for 3 current carrying traces with 1" pitch (FIGs. 21A and B). Each trace had three coplanar sensors placed at a vertical offset of 0.2". The space between adjacent sensors was 0.2", and the sensors measured the field along the Y axis.

The sensor signal S _i , _j = S _{i,j Sig} + S _{i,j int} , where S _{i,j Sig} is the signal component from trace T _j , and S _{i,j int} is the signal component from all traces except T _j .

The simulation was ran for the following currents and fields above T ₂ and analyzed to confirm the basic assumptions of the model.

Results: When T ₂ signal only was present (T ₂ = 5A, T ₁ = T ₃ = 0A), S ₂ , ₂ , _Sig = 37.6 μΤ, S ₁ , ₂ , _Sig = 29.74 μΤ, S _3,2,sig = 29.69 μΤ, confirming the assumption . The

difference in values of S _1,2,S i _g and S _3,2 , _S i _g was within numerical precision of the solver.

When T ₂ interference only was present (T ₂ = 0A, T ₁ = 1A, T ₃ = 7A), S ₂ , ₂ ,int = 19.5 μΤ, S _1,2 ,int = 17.72 μΤ, S ₃ , ₂ , _int = 21.52 μΤ, where 0.5*(S _1,2 , _int + S ₃ , ₂ , _int ) = 19.62 μΤ, confirming the assertion that Again, the difference in value was within

numerical precision of the solver.

When T ₂ signal and T ₂ interference were present (T ₂ = 5A, T ₁ = 1 A, T ₃ = 7A), S _2,2 = 57.293 μΤ, S _1,2 = 47.686 μΤ, S _3,2 = 51.272 μΤ, where S _2,2 - 0.5*(S _1,2 + S _3,2 ) = 7.81 μΤ and S _2,2,sig

- 0.5*( S _1,2,sig + S _3,2,sig ) = 7.61 μΤ, confirming the assertion that

2

The basic assumptions for a 3 sensor stick on meter were validated.

The simulated electromagnetic field used to demonstrate one instance of the cross-talk mitigation algorithm is shown (FIG. 22). The sensors were positioned as in FIGs. 21A and B.

FIG. 22, upper left, shows a graph of the simulated electromagnetic field measured along the line shown in the upper right box. In the lower box in FIG. 22, the plot of the interference from the neighboring breaker only is shown.

The basic assumptions for a 5 and 7 sensor system will also be validated.

EXAMPLE 5: A STATISTICAL PROCESS CONTROL APPROACH TO COMMISSIONING BUILDINGS

A preliminary methodology is outlined for determining when building operation parameters have changed, attempt to identify them, and suggest solutions, given a top-down monitoring methodology combined with a bottom-up fault diagnostics approach. The approach in this paper will combine the use of free open- source graphical analytic tools with whole building interval energy consumption data for the development of control charts. This technique leverages advancements in computing and smart grid technology along with industrial process quality control methods to facilitate fault detection and diagnostics in commercial buildings. This technique will be discussed, from technical requirements to implementation, with special attention paid to the progress of a test case deployment.

Methods

All of the data were analyzed using R - a free software environment for statistical computing. Building power use data for this paper are stored on the sMAP repository at

(new(dot)openbms(dot)org). sMAP is an open source standard for communicating sensor data, similar to how HTTP is a standard for internet communications. More information on sMAP can be found at (www(dot)cs(dot)berkeley(dot)edu/~stevedh/smap2/ ). More information on R can be found at (www(dot)r-project(dot)org/ ).

Data was gathered from two commercial buildings in different locations: for the purpose of anonymity, the buildings will only be referred to by a name and number. Table 1 gives the building names and a brief description of their characteristics.

Table 1: Building names and Descriptions

Building Name Description

Building 1 100,000 sq.ft 1990's vintage office building in California thermal

zone 3. This building is a high tech sector 3-story office building with significant lab loads. The building is primarily a VAV Reheat system, with additional cooling via fan coils serving high load

areas.

Building 2 200,000 sq.ft 1980' s vintage office building near Chicago, IL. The

12-story all-electric office building is mostly tenant occupied office space with a detached non-enclosed three-level parking garage.

In order to predict the behavior of a building an hour ahead for each time step, a seasonal- trend-loess (STL) model was trained on six months of previous data in order to predict one hour ahead at each step. The entire prediction was then moved one hour, so that at each step in the procedure, one hour of data was predicted. An hour ahead was chosen because the power of the predictive method chosen decays quickly as the prediction horizon extends. The STL method decomposes time series into three parts: a seasonal component, a trend component, and a remainder. The particular STL model used here took as input only previous power use of the building and outside air temperature.

It is hypothesized that it is a necessary but not sufficient condition for a building to be predictable in order for it to be well tuned. In order to test this theory, a residual based control chart technique was employed. Following this predictive method, each hour of prediction was subtracted from the actual power use to yield the residual power use, or error in prediction. This error term, denotes how far off at each hour the prediction is from the actual behavior of the building. This information of error was then fed into the control chart methodology, which allowed the diagnosing of buildings at an accelerated rate.

In this case, three control charts were used - a moving average (MA) control chart, a rolling outlier (RO) control chart, and a dual measure control chart - combining the metrics of the MA and RO charts. The moving average control (figure of merit designated as: Mt), assumed that at the start of the process, the mean of the process should be stationary and computed the mean of the prediction process residual (actual - predicted) as a two week rolling average - This was updated as the process moves along, and each point in the control chart was then developed using a two-week (336 hour) rolling window. In statistical process control (SPC), upper and lower control limits (UCL and LCL, respectively) are designated to specify when a process is "out of control" - for the MA chart, the control limits used were:

where σ is the standard deviation of the process up to time within window w, n is the number of samples averaged at each time point, and w is the total number of samples in the rolling window. For the purposes of this examination, n=4 and w = 336 were used, with measurements in hours. The RO chart was constructed in a manner that allowed visualization of points that were hard to predict on a weekly basis. For every hour in the data set after the first week, the 99% percentile of residuals in the preceding week was calculated. Following this calculation, a point was flagged as "hard to predict" if its residual was above the 99% percentile for the previous week, giving the hardest points to predict in each week's rolling window. In short, the MA chart showed when the mean of the prediction process shifted, and the RO chart showed when it produced extreme outliers. The construction of control charts may be viewed as a convenient method for data mining. The two charts previously constructed were therefore combined to allow for a "vote" on when the building was malfunctioning. The combined chart was a dual measure (DM) chart, which told when the prediction process was producing outliers at the same time that the prediction process mean shifted. This methodology could be extended to any number of possible rule sets.

Experimental Design

The analysis of data was done through a two-phase process - automated data analysis and manual examination of flagged points for errors. It was important to test whether the data analysis methodology presented could identify faults with only whole building interval power data and local weather data. Building energy interval data was transmitted to the sMAP repository where it was retrieved for analysis. No identifying information was sent about the building except for identifying the nearest weather station. The buildings chosen were two that are currently undergoing relrocommissioning work at the time of this study.

Results were reviewed by engineers with familiarity with the buildings to explore what information the control charts exposed and to examine whether the flagged points appeared to be errors. This review consisted of first looking at the control charts generated by the automated analysis. Flagged timestamps corresponding with known building events were filtered out to focus on timestamps where potential faults were identified. Heatmap charts of daily peak kW and total kW for periods surrounding the remaining flagged timestamps were reviewed to identify suspicious variations in peak load and daily energy consumption. Finally building load profiles were reviewed for days and times where flags were identified.

Results

For each of the two buildings presented here, the mean absolute percent error (MAPE) is presented as a function of time from the forecast horizon in Figure 11. The cases of a building with low error and one with high error were examined, each of which yielded different information. For the purposes of analyzing results, it should be noted that the hour ahead forecasts were most reliable, so the horizon was trimmed there for purposes of this analysis. The MAPE for hour-ahead predictions in building 1 was 4.94%, while the MAPE in building 2 was 19.2% for hour-ahead predictions. Error was defined as actual minus predicted value in this case.

Figure 11. Mean Absolute Percent Error for buildings 1 and 2 over a 168 hour prediction frame. Because predictability decays with time, 1 hour ahead predictions were used.

RESIDUAL BASED CONTROL CHARTS The control charts for the buildings in the analysis are presented in Figures 12-15. It should be noted that this is an example of a technique that can be used for any building, but for this application, due to time constraints, only the two buildings in this data set were analyzed.

Figure 12. Moving Average Chart for Building 1 - The points on this chart represent a 4 hour moving average of the distance that the predicted values are from the actual values. The lines on the graph are the upper and lower control limits (UCL, LCL). The points outside of the black lines are "out of control." Note the "cone" toward the end of the graph - this corresponded to a major retrofit.

Figure 13. The points on this chart represent a 4 hour moving average of the distance that the predicted values are from the actual values. The points outside of the black lines are "out of control."

Figure 14. Rolling outlier chart for residuals of power prediction in building 1. This chart demonstrates how the rolling outlier technique picks out points that are harder to predict than 99% of points in the previous week. There was a "cone" that is noted by brackets in this graph, and this was a point when there was a major retrofit in this building.

Figure 15. Rolling Outlier chart for building 2: some data points can been seen to be outside of the 99 ^th percentile of errors over a one week rolling window.

The "cone" seen in the chart for Figure 15 corresponded to a point where the chiller in that building was replaced and subsequently commissioned. As the commissioning went on, the predictability of the building changed.

For reference, Figures 16 and 17 shows the power use of each building with both Hard to Predict and Out of Control points embedded in the graphs.

Figure 16. Power use in building 1 with Out of Control and Hard to Predict, as described above.

Figure 17. Power use in building 2 with Out of Control and Hard to Predict, as described above.

EXAMPLE 6: EXAMPLE ROOT CAUSE ANALYSIS

Upon analysis, scheduling errors and equipment commissioning problems were picked out of the data from Example 4 by being flagged and then zoomed in on with deeper interval data analysis around the flagged timestamp. This would have normally taken an engineer a large amount of time examining time series of HVAC systems. An example point that was flagged by the algorithm on Saturday, Nov 2, 2013 in building 1 turned out to be equipment cycling overnight because of a control problem during commissioning. A graph of the power use on days in question is presented in Figure 18.

Figure 18. Cycling at night over a week picked up using the present method (line under dates). The cycling at night was not detected by the engineer before employing this tool.

The application can also find errors that are more apparent but also time consuming to find, such as anomalous nighttime power usage. In normal applications of RCx, the engineer would sift through the data looking for these points. With the present method, it showed up as one of several suspect points on a graph and may be simply investigated that way in a speedy fashion. Figure 19 shows a period in which building 2 used an unexplained amount of power in the time period of Thursday, September 12 at night, when the building failed to power down correctly.

Figure 19. Unexplained nighttime power picked up quickly and effectively by the present method (line under dates), in a much shorter time than an engineer combing data would have taken.

In addition to charting metrics and mining predicted power use data so that faults in buildings can be identified faster, there is value in looking at examples of derived metrics that stem from the experience of building commissioning agents. Much information is to be gained through careful construction and charting of informative metrics in buildings that are being or have been commissioned. This information can be presented in the form of graphical interfaces that can be interpreted by engineers working in the buildings or by a computer program that synthesizes the information and presents it to the engineer.

APPLICATION TO PERSISTENCE IN MONITORING BASED COMMISSIONING

As buildings are retrocommissioned, their behavior should become more predictable, and thus more in control, according to the present algorithms. In the case of building 1 -the building becoming more predictable toward the end of the "cone" that is illustrated could be seen - this corresponded to a chiller replacement. This could have applications for the persistence of retrocommissioning (RCx) measures - once a building is tuned, to continue monitoring that building in a manner that does not require human intervention at every step, and could provide automatic notification of building drift.

Conclusions

It was possible to determine when some energy efficiency flaws are happening in a building by using statistical process control methods. Using these methods, errors in a building that would take an engineer many hours to pull out by hand were found. A method for determining if there are scheduling faults in a building by simply running a predictive algorithm on whole building meter data was demonstrated.

Although the foregoing embodiments of the present disclosure have been described in some detail by way of illustration and example for purposes of clarity of understanding, it is readily apparent to those of ordinary skill in the art in light of the teachings of the present disclosure that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

Accordingly, the preceding merely illustrates the principles of the present disclosure. It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the present disclosure and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the present disclosure and the concepts contributed by the inventors to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. The scope of the present disclosure, therefore, is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present disclosure is embodied by the appended claims.