FIELD OF THE INVENTION

The invention relates to an energy measurement device, a building automation system, an energy measurement method, and a computer readable medium. BACKGROUND

Driven by the need to reduce operational costs and energy reduction, conventional lighting systems, often with limited controls, are being upgraded to more energy-efficient lighting systems with sensors and sophisticated controls. An example of an existing conventional lighting system may be fluorescent lighting with switching based occupant control. An example of an upgraded lighting system may be LED lighting with occupancy sensor based controls. A driver for such a change is promised energy saving. Measurement and verification (M&V) of energy savings and continuous performance monitoring of the upgraded lighting system is therefore important. Such M&V and performance monitoring can be achieved using energy meters. Based on the validation, problems in the upgrade process may be uncovered. For example, it may turn out that due to a misconfiguration the amount of energy savings is smaller than expected. It is therefore important to measure the energy reduction obtained due to the upgrade.

In the scenario in which a lighting system with controls is upgraded, lighting energy is monitored by means of energy meters that aggregate a large number of lighting units. The energy savings from the lighting upgrade needs to be validated based on measurements over a monitoring period with the pre- and post-upgrade lighting system. Lighting energy consumption over the monitoring period is dependent however on operational factors like occupancy, weather conditions and so on. As such, it is important to properly account for the effects of such factors so that the energy consumption pre- and post- upgrade may be fairly compared.

Procedures for M&V that involve a simple lighting technology upgrade (e.g., fluorescent to LED) are relatively straightforward. Procedures for M&V for monitoring lighting energy use for systems with advanced occupancy controls are however not straightforward. This is due to variability in operational hours and/or other operational parameters over the monitoring and verification periods. Thus, methods are required that enable proper assessment of energy savings due to lighting controls upgrades, while properly accounting for factors like occupancy that affect lighting energy use.

Lighting system usage varies vary with time, weather, nature of day (e.g., business day or holiday), and other operational factors. To properly account for all these factors, it would be desirable to measure lighting energy consumption over at least one year to ensure observing all influencing factors. However, this is impractical in practice and energy measurements are only done over a period of a few weeks, e.g., 2-3 months.

In US patent 5566084 A, "Process for identifying patterns of electric energy effects of proposed changes, and implementing such changes in the facility to conserve energy", included herein by reference, regression analysis over large periods (e.g. yearly) of occupancy hours per day is used to estimate influence of operational factors on energy use. Moreover, explicit knowledge of the standard occupancy hours of operation of a facility is used.

Also in US patent 8532808 B2, "Systems and methods for measuring and verifying energy savings in buildings", included herein by reference, regression analysis is used to obtain a model for energy use from influencing variables like occupancy, humidity, air temperature, that affect the energy use of buildings.

US6,996,508B1 discloses a method for remote energy consumption ident ificat ion for a facility, and includes receiv ing energy consumpt ion data associated with the facility, generating facility data associated with the facility, and receiving external variable data associated with the facility corresponding to the energy consumption data. The method also includes generating a first energy consumption model based on the facility data, the energy consumption data, and the external variable data. The method also includes generating a second energy consumption model based on the facility data and the external variable data. The method further includes determining energy consumption efficiency for the facility using the first and second energy consumption models and identifying a retrofit of an energy consumption system of the facility based on the energy consumption efficiency.

US2011/0130886A1 discloses a method for use with a building management system in a building. The method includes receiv ing historical data from the building management system. The method further includes using the historical data to automatically select a set of variables estimated to be significant to energy usage in the building. The method further includes applying a regression analysis to the selected set of variables to generate a baseline model for predict ing energy usage in the building. EP2096508A1 discloses a system for utility base lining records historic values of utility loads for regions within a facility, as well as historic values of independent variables such as outside temperature, time, date, workday versus non- workday, and occupancy. A similar data selector seeks out similar times in the past and submits the data from those times to a base line estimator that produces a baseline mean estimate and a baseline variance estimate. Differences between the current load and the baseline mean estimate are detected.

The known methods have various drawbacks. On the one hand, making an explicit model from influencing variables to the energy use in a lighting system requires a large amount of data, and explicit knowledge of the various factors that impact the energy use in a lighting system, e.g., occupancy and weather. Neither of these two is typically available. On the other hand, there is a desire of skilled person in the art of lighting systems to be able to validate their work, to verify that the new lighting system is working to specifications. Existing methods either fail to account for those factors that impact energy use that are different from the upgraded system itself or require too much data which is not available. Thus state-of-art methods either cannot be used, or are too inaccurate.

SUMMARY OF THE INVENTION

It would be advantageous to have an improved energy measurement device for monitoring energy use in energy using system and verifying that said energy using systems are working according to specifications.

An energy measurement device for a building automation system is provided. The energy measurement device comprises:

an input configured to receive a first and second sequence of energy use measurements from an energy meter coupled to the building automation system, the energy meter being configured to measure the aggregate energy consumption of the building automation system, the first sequence extending over a first time-period, the second sequence extending over a second time-period,

a processor circuit arranged to

partition the first and second time-period into multiple shorter time-periods, a shorter time-period being assigned to a class of multiple classes depending at least on the size of the energy use measurements falling in the shorter time-period,

compute an average energy use per class for the first sequence by averaging energy use measurements of the first sequence falling in the same class, and compute an average energy use per class for the second sequence by averaging energy use measurements of the second sequence falling in the same class,

compute a duration per class for the first sequence by adding the duration of the shorter period in the first time-period assigned to the class,

- compute a difference between the inner product of the average energy use per class for the first sequence and the duration per class for the first sequence and the inner product of the average energy use per class for the second sequence and the duration per class for the first sequence,

send an alarm signal if the difference shows that the inner product of the average energy use per class for the first sequence and the duration per class for the first sequence is lower than the inner product of the average energy use per class for the second sequence and the duration per class for the first sequence,

wherein the building automation system has been upgraded between the first and second time period.

In an embodiment, the building automation device comprises a lighting network. For example, the building automation system comprises multiple lighting units, the energy meter being configured to measure the aggregate energy consumption of at least the multiple lighting units. In an advantageous application of the energy measurement device the building automation system has been upgraded between the first and second time period. In this case the first and second time period do not overlap.

Interestingly, the energy measurement device derives a high level description of the building automation device both from energy measurements received in the first or second time period. This allows comparison of the energy use between the first and second period, even though the building automation network is not used in the same way in these period.

A method according to the invention may be implemented on a computer as a computer implemented method, or in dedicated hardware, or in a combination of both.

Executable code for a method according to the invention may be stored on a computer program product. Examples of computer program products include memory devices, optical storage devices, integrated circuits, servers, online software, etc. Preferably, the computer program product comprises non-transitory program code stored on a computer readable medium for performing a method according to the invention when said program product is executed on a computer. In a preferred embodiment, the computer program comprises computer program code adapted to perform all the steps of a method according to the invention when the computer program is run on a computer. Preferably, the computer program is embodied on a computer readable medium.

Another aspect of the invention provides a method of making the computer program available for downloading. This aspect is used when the computer program is uploaded into, e.g., Apple's App Store, Google's Play Store, or Microsoft's Windows Store, and when the computer program is available for downloading from such a store. BRIEF DESCRIPTION OF THE DRAWINGS

Further details, aspects, and embodiments of the invention will be described, by way of example only, with reference to the drawings. Elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. In the Figures, elements which correspond to elements already described may have the same reference numerals. In the drawings,

Fig. 1 schematically shows an example of an embodiment of an energy measurement device,

Fig. 2a shows an example of a graph of energy measurements of a lighting system before an upgrade,

Fig. 2b shows an example of a graph of energy measurements of a lighting system after an upgrade,

Fig. 2c shows an example of a table of energy measurements of a lighting system before and after an upgrade,

Fig. 3 a schematically shows an example of a partition of a time period before an upgrade,

Fig. 3b schematically shows an example of a partition of a time period after an upgrade,

Fig. 4 schematically shows a flow diagram of an energy measurement method, Fig. 5 a schematically shows a computer readable medium having a writable part comprising a computer program according to an embodiment,

Fig. 5b schematically shows a representation of a processor system according to an embodiment. List of Reference Numerals:

100 an energy measurement device

120 an input

130 a processor circuit

132 a partitioning unit

135 an averaging unit

140 a storage

142 an average energy use per class for the first sequence

144 a duration per class for the first sequence

150 a comparator

152 an alarm unit

162 an average energy use per class for the second sequence

164 a duration per class for the second sequence

170 a building automation system

172 an energy meter

174 a database

200, 250 a bar graph

210, 260 a peak energy use

212, 262 a high energy use period

214, 264 a transitory energy use period

216, 256 a low energy use period

220, 270 a non-business energy use period

310 a first time period

311-316 a transition between two energy use periods

320 a second time period

321-325 a transition between two energy use periods

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

While this invention is susceptible of embodiment in many different forms, there are shown in the drawings and will herein be described in detail one or more specific embodiments, with the understanding that the present disclosure is to be considered as exemplary of the principles of the invention and not intended to limit the invention to the specific embodiments shown and described. In the following, for the sake of understanding, elements of embodiments are described in operation. However, it will be apparent that the respective elements are arranged to perform the functions being described as performed by them.

Further, the invention is not limited to the embodiments, and the invention lies in each and every novel feature or combination of features described herein or recited in mutually different dependent claims.

Figure 1 schematically shows an example of an embodiment of an energy measurement device 100. Energy measurement device 100 may be used for monitoring energy use in any energy using system, for example in a building automation system, or in any indoor or outdoor energy using system. Monitoring of the energy use of an energy using system is used by skilled persons to obtain valuable information on the correct functionality of the energy using system; for example an energy use which is much lower or much higher than an expected energy use for that system may indicate that the energy using system is not functioning correctly. Monitoring of the energy use in energy using systems is also of relevance, for example, to accurately estimate energy costs of the energy use.

For example, building automation system 170 may comprise a lighting system. A particular problem in comparing energy use of modern lighting systems is that their energy use is strongly dependent on external factors, e.g., outside lighting conditions, occupancy use of the building, and the like. This becomes particular problematic when a lighting system is upgraded from a conventional lighting system, for example, comprising fluorescent lighting with switching based occupant control, to an upgraded lighting system, for example comprising LED lighting with occupancy sensor based controls. A driver for such a change may be energy saving, thus the upgraded lighting system may be less energy demanding and thus more environmental friendly. Skilled persons who implement such modern lighting systems have a desire to measure their energy use, and to compare it to the energy use before the upgrade. This is done so that configuration problems are brought to light. Short-circuits, broken units, installations of wrong equipment, etc., are problems that are traditionally easy to spot looking at the energy consumption. However, with modern lighting systems this becomes more complicated. A week with high energy consumption may correspond to a week with a high use of the building during an overcast day, and need not necessarily indicate a problem with the installation.

The inventors have realized that one of the main challenges for proper accounting of the energy savings of the upgrade in the lighting system derive from different operational conditions in which the pre-upgrade lighting system and the post-upgrade lighting system energy uses are measured. Such different operational conditions include different pre-upgrade and post-upgrade weather conditions, occupancy data, type of activity, or the like. Energy measurement device 100 is capable of properly accounting for the different operational conditions so that energy savings due to an upgrade of the lighting system can be computed. In an example below it is explained how the different operational conditions are taken into account for the computation of the energy savings in an upgraded lighting system.

Energy measurement device 100 comprises an input 120 configured to receive energy use measurements from an energy meter 172. The energy meter 172 is coupled to a building automation system 170. To further complicate matters, Energy meter 172 is configured to measure an aggregate energy consumption of the building automation system 170. For example, in an embodiment, building automation system 170 comprises multiple lighting units. Energy meter 172 is configured to measure the aggregate energy consumption of at least the multiple lighting units.

Energy measurement device 100 is configured to compare energy consumption between two periods. Accordingly, input 120 is configured to receive a first sequence and a second sequence of energy use measurements from energy meter 172. For example, an upgrade of the lighting system may be implemented between the first time period and the second time period.

For example, in an embodiment, energy measurement device 100 may be collected to energy meter 172 with a wired or wireless connection, e.g., a digital connection. For example, energy measurement device 100 may be collected to energy meter 172 through a digital computer network, e.g., the internet or the like. Through the connection with energy meter 172 the energy measurement device 100 receives update on the energy use of the building automation network. For example, in an embodiment, energy meter 172 may report a cumulative total of energy use, energy measurement device 100 may derive the energy use since the last received measurement by subtracting the previous measurement. For example, in an embodiment, energy meter 172 may report a total of energy use in a previous period, e.g., in the last hour, the last minute and the like. For example, energy meter 172 may report in unit like joule, watt-hour, kilowatt-hour and the like. In an embodiment, energy meter 172 may report in energy usage per time period, e.g., per hour or per second, e.g., in Watt.

Energy meter 172 is configured to measure the aggregate energy consumption of the building automation system 170. This means that information about energy use of individual units, or locations, e.g., rooms, is unavailable. A direct comparison of units while in use, or rooms while in use is not directly possible.

The first sequence extends over a first time-period. The second sequence extends over a second time-period. For example, the first and second time-periods may be a week, or a month, or less than a month, etc. The first and second time-period need not be of the same length.

Embodiments are suitable for monitoring lighting systems, especially before and after an upgrade. However, as mentioned above, the energy meter 172 may be coupled to any energy using system and be suitable for monitoring the energy use in said energy using system.

Energy measurement device 100 comprises a processor circuit 130. Processor circuit 130 implements a number of units, for example, as software programs, or as hardware, e.g., as circuits, or as hybrids.

An idea used in embodiments is to derive a global usage of the building in which the building automation system is installed. This is a high level classification. There may be different suitable classes. Per class an average energy use is computed, and furthermore, a duration per class. The duration may be a total time in a class, or a percentage of time in a class, etc. Clearly, knowing the average energy use per class, and the duration per class, the total energy use may be recomputed, e.g., by taking the dot product, also known as the inner product, of these two sequences of values. If the duration is total time per class, the total energy use is recovered, if the duration is average occupation per class, an average energy use is obtained. The inventors found that when an lighting system is upgraded there may be changes across two dimensions: changes in the average energy use per class, e.g., because more energy efficient devices are used, but also changes in the duration per class, e.g., because smarter detection of the needed lighting is used, e.g., due to light sensors, occupancy sensors and the like.

Due to this two changing aspects, e.g., after an upgrade a direct comparison of the energy use across two time periods, e.g., before and after an upgrade becomes less meaningful. For example, a reduction in energy use may be due to more efficient devices, or due to a less intense usage of the building automation system.

The inventors had the insight that this situation may be improved by comparing the energy use before the upgrade with the dot product of the durations per class before the upgrade and the average energy use per class after the upgrade. In this way, one of the two changing dimension is eliminated. Instead of taking the first time period before second time period, e.g., before respectively after the upgrade, one may also take the first time period after the second time period, e.g., after respectively before the upgrade. For example, on may compare the energy use after the upgrade with the dot product of the durations per class after the upgrade and the average energy use per class before the upgrade.

One may define classes based on energy use. For example, the use of the building automation system may be classified into three classes: high, medium and low usage. Medium usage is also referred to herein as transient usage. More or fewer energy classes are also possible. For example, in some office use, it may be appropriate to define two energy classes, e.g., high and low; or more than three classes, e.g., four classes, high, high- medium, low-medium and low. Additionally, other sources of information that pertain to the usage of the building may be used in the classification; For example, whether the day was a business day or a non-business day. In an embodiment, one may have four classes, business day-high, business day-medium, business day-low, and non-business day. If nonbusiness day may have significant activities, e.g., in a hospital one may further subdivide non-business days. For example, one may have six classes, business_day-high, business_day- medium, business_day-low, non-business_day-high, one may have four classes,

business_day-high, business_day-medium, business_day-low, and non-business day- medium, non-business day-low. Classes may be numbered, e.g., Class I to Class IV, etc.

To obtain this information, processor circuit 130 may implement a partitioning unit 132 and an averaging unit 135.

For example, partitioning unit 132 is arranged to partition the first and second time-period into multiple shorter time-periods. A shorter time-period is assigned to a class of multiple classes depending at least on the size of the energy measurements falling in the shorter time-period. For example, partitioning unit 132 may assign each shorter time period to one of the classes defined herein. Averaging unit 135 is arranged to compute an average energy use 142 per class for the first sequence by averaging energy use measurements.

Averaging unit 135 is further arranged to compute an average energy use 162 per class for the second sequence by averaging energy use measurements of the second sequence falling in the same class. In an embodiment, averaging unit 135 is also arranged to compute a duration 144 per class for the first sequence by adding the duration of the shorter period in the first time-period assigned to the class, and a duration 164 per class for the first sequence by adding the duration of the shorter period in the first time-period assigned to the class.

Computing the duration may also be done by a separate unit. Duration may be for example, duration per class per period. For example, 2 hours in class II per day, or 10 hours in class II per week, etc. For example, duration per class, may be an absolute number, e.g., 1 1 hours in class II (total). Duration may be a relative number, e.g., a percentage, e.g., 24.87% in class II. The computed durations 144 and 164 and energy uses 142 and 162 may be stored in a storage 140. Note that duration per class is optional for one of the two periods, as it is not used during the computation. For example, durations 164 may be omitted.

Processor circuit 130 implements a comparator 150 arranged to compute a difference between the inner product of the average energy use per class for the first sequence and the duration per class for the first sequence and the inner product of the average energy use per class for the second sequence and the duration per class for the first sequence. For example, a dot product may be computed for a first sequence of average energy uses e _t and durations d _i as∑ _£ e _t d _i . The latter sum runs over the number of defined classes, e.g., in the above example, four or six. In the above formula, the energy uses e _t and durations d _t may be taken from the same period, in which case traditional energy use is obtained for that period, or an energy use may be combined with durations from a different time period. In that case an energy use is obtained corrected for the different use of the building automation system. The difference may be computed in various ways. For example, one may compute E ¹ —∑ _£ ef d\, wherein the super scripts refer to the time period and wherein E denotes the energy use in the first period. Similarly, one may compute Σ^ε*— ef)d}, etc. In these formulas, the superscripts 1 and 2 may be interchanged to interchange to ordering of the first and second time period.

In an embodiment, the first time period and the second time period do not overlap and building automation system 170 has been upgraded between the first time period and the second time period. Non-overlapping time periods simplify the computation. Note the upgrade may be between the first and second time period, if the first time period starts and ends before the upgrade, and the second time period starts and ends after the upgrade; or vice versa, wherein the second time period starts and ends before the upgrade, and the first time period starts and ends after the upgrade.

In an embodiment, the time periods may be allowed to overlap. For instance we may upgrade a different floor of a building, or a different site of the same customer, e.g., over the same or an overlapping time period. The concepts of the invention would also apply in these cases.

The building automation system, especially the upgraded building automation system, may comprise one or more occupancy sensors for determining presence of human activity in the upgraded building automation system. The occupancy sensors may be located in particular areas of the building automation system for detecting presence of human activity in said particular areas. Occupancy sensors may for example be used to control the automation system in said particular areas in dependence of the detected presence of human activity of the automation system in said areas. Lighting, and hence energy, may thus be dependent on occupancy. The lighting may be based on, e.g., occupancy sensors, switches, or both. Other factors may also be taken into account, e.g., light, time of day, date, etc.

In an embodiment, processor circuit 130 is configured to send an alarm signal, for example to an alarm unit 152, if the difference shows that the inner product of the average energy use per class for the first sequence and the duration per class for the first sequence is lower than the inner product of the average energy use per class for the second sequence and the duration per class for the first sequence. A comparator 150 may be configured to determine whether the inner product of the average energy use per class for the first sequence and the duration per class for the first sequence is lower than the inner product of the average energy use per class for the second sequence and the duration per class for the first sequence. Comparator 150 may trigger alarm unit 152 to send the alarm signal, for example, to an installer of the upgraded system. For example, the alarm signal may indicate that energy meter 172 that is monitoring the upgraded system does not function properly, or that the upgrade of building automation system 170 has not been implemented correctly, in any of these cases some failures may have occurred in the upgraded system.

For example, with reference to the embodiment of the lighting system described above, a stock of installed LEDs lamps in the upgraded lighting system may be defect or a short circuit may have damaged some of the installed LEDs lamps. In any of those occurrences, the alarm signal alerts the installer or who is in charge of maintaining the upgraded system that a failure has occurred because the energy use which is expected to decrease did not decrease after the upgrade.

Figure 2a shows an example of a bar graph 200 of energy measurements of a lighting system before an upgrade.

Figure 2b shows an example of a bar graph 250 of energy measurements of a lighting system after the upgrade.

The energy measurements given in both graphs 200 and 250 are given in Watt hours [Wh] and are taken every hour, 24 hours a day, for a week. The bar indicates the measured energy use over a specific hour. The energy may be measured by one or more energy meters coupled to the lighting system as described with reference to Figure 1. The energy meters are configured to measure the aggregate energy consumption of the lighting system. Each bar of figures 2a and 2b represents energy use in one hour. These hours are indicated on the horizontal axis. However, for clarity only selected hours have been indicated.

For example, the energy measurements of the bar graph 200 relate to a time period of a week, say a first week. The energy measurements of the bar graph 250 relate also to a time period of a week after the upgrade and after the first week, say a second week. In this case, the energy measurements were obtained before (figure 2a) and after (figure 2b) and upgrade of a lighting system.

The processor circuit is arranged to partition the first week into multiple shorter time periods which are classified (in this case) into four classes: business day-high, business day-medium, business day-low, and non-business day. A few of these shorter period have been indicated in figures 2a and 2b, such as for example period 216 including the afternoon of Tuesday and the night between Tuesday and Wednesday in the first week, or period 212 roughly including Thursday of the first week, or period 220 roughly including Saturday and Sunday of the first week.

Periods 216, 212 and 214 may be assigned to different classes: period 216 may be assigned to a class of low energy use periods, period 212 may be assigned to a class of high energy use periods and period 214 may be assigned to a class of transitory energy use periods.

For example, periods 216, 212 and 214 relate to the energy use during a business day period before the upgrade. Period 220 is a non-business energy use period before the upgrade. Bar 210 indicates a peak energy use in the pre-upgrade sequence during the business day period. As explained in an embodiment below, class assignment of periods 216, 212 and 214 in the business day period may be made by comparing the energy use in a particular hour with peak energy use 210.

Similarly, the processor circuit is arranged to partition the second week into multiple shorter time periods such as for example period 256 including the afternoon of Tuesday and the night between Tuesday and Wednesday in the second week, or period 262 roughly including Thursday of the second week, or period 270 roughly including Saturday and Sunday of the second week.

Period 256 may be assigned to a class of low energy use periods, period 262 to a class of high energy use periods. Period 270 is a non-business energy use period after the upgrade. Periods 256, 262 and 264 relate to the energy use during a business period after the upgrade. Bar 260 indicates a peak energy use in the post-upgrade sequence. Period 214 and 264 are assigned to class of transitory energy use periods for the first week before the upgrade and the second week after the upgrade, respectively.

The classes of high energy, transitory energy and low energy use periods start approximately, but not exactly, at the same time. Sometimes a class, especially the transitory energy use period class is skipped entirely.

Although non-business periods 220 and 270 have not been further portioned into any classes, a similar procedure of class assignment as used for the business day period may be applied to non-business day periods. For example, this may be useful for buildings such as homes, hospitals and the like.

One way to partition the first or second time period into classes is as follows.

First partition the first and second into time period that represent a single energy use measurement. For example, in the example shown in figures 2a and 2b a single energy measurement is obtained per hour. In this case, the first and second time periods may first be portioned into short time periods of one hour each. These one hour short time periods may each be classified individually. For example, a short time period may be assigned a class based on its energy use, and possibly other factors, e.g., whether or not it was a business day. If desired these shorter periods may be concatenated if periods that are neighboring in time are assigned to the same class. Alternatively, the one-measurement-periods may also be used as the portioning of the time period. In the examples give with respects to figures 2a, 2b, 3a, and 3b consecutive one-measurement periods (in this case 1 hour) have been concatenated if they are of the same class. In figure 2c classes are shown as assigned per hour.

Classes may be assigned based on the energy used. For example, in an embodiment, the energy measurements in a class of the multiple classes are higher than a lower threshold and/or lower than an upper threshold. In an embodiment, the upper and/or lower threshold is defined as a percentage of a peak energy use.

The latter option has been used in figures 2a and 2b; these figures show a peak energy use 210 and 260. The classes in each time period are defined with respect to the peak energy use in that period. For example, with reference to the bar graph 200, a hour of a business day may be assigned to the class of high energy use periods if the energy measurement in that hour is higher than a lower threshold, for example a percentage of the peak energy use 210, for example 60%, 66% or higher percentage of the peak energy use 210.

For example, with reference to the bar graph 200, an hour of a business day may be assigned to the class of transitory energy use periods if the energy measurement in that hour is lower than an upper threshold defined as an upper percentage of the peak energy use 210, and higher than a lower threshold defined as a lower percentage of the peak energy use 210.

The upper percentage of the peak energy use 210, may be, for example, 60%, 66% or higher percentage of the peak energy use 210. The lower percentage of the peak energy use 210, may be, for example, 30%>, 33% or lower percentage of the peak energy use 210.

With reference to the same bar graph 200, a hour of a business day may be assigned to the class of low energy use periods if the energy measurement in that hour is lower than the threshold defined as, for example, the lower percentage of the peak energy use 210 defined by way of an example above.

Hourly energy measurements for the bar graph 250 may be assigned to the different classes in the same way. Upper and lower energy thresholds may be defined as a percentage of the peak energy use 260 in the second sequence during a post-upgrade business day period.

By using the classification of the time periods explained above, the energy use of the pre-upgrade and post-upgrade lighting system can be expressed analytically by defining the following quantities.

E _peak,b is the average energy use in all hours assigned to the class of high energy use periods during a business day period.

N _peak,b is ^me number of hours assigned to the class of high energy use periods during a business day period.

E _tran,b is ^me average energy use in all hours assigned to the class of transitory energy use periods during a business day period.

N _tran,b is the number of hours assigned to the class of transitory energy use periods during a business day period,

E _low,b is ^me average energy use in all hours assigned to the class of low energy use periods during a business day period.

E _low,b is the number of hours assigned to the class of low energy use periods during a business day period.

D _b is the number of business days in the monitoring period.

D _nb is the number of non-business days in the monitoring period.

N _b is the number of hours in a business day. N _nb is the number of hours in a non-business day.

Superscript (1) denotes quantities for the pre-upgrade lighting system, while superscript (2) denotes quantities for the post-upgrade lighting system. Having defined above quantities, aggregate average energy use before and after the upgrade can be written as:

In expressions (3) to (5), A _b and A _nb are respectively the number of business and non-business days annually. Note that these expression are dot-products between the energy use quantities E and duration quantities, e.g., N * D, N * A, etc. In this example, like terms have been group together to increase clarity.

Expression (3) represents the sum of the products of the average energy use of the pre-upgrade lighting system for each class and the corresponding duration for the same class for the same pre-upgrade sequence. In other words, equation (3) corresponds to the inner product of the average use per class for the pre-upgrade sequence and the duration per corresponding class for the same pre-upgrade sequence.

Equation (5) represents the sum of the products of the average energy use of the post-upgrade lighting system for each class and the corresponding duration for the same class of the pre-upgrade sequence. In other words, equation (5) corresponds to the inner product of the average use per class for the post-upgrade sequence and the duration per corresponding class for the pre-upgrade sequence.

The energy use of expressions (3) to (5) may be further extrapolated from a specific monitoring area wherein, for example, occupancy sensors may be installed to a specific site, to e.g., a whole building area, using available information for instance in the form of surface area or number of luminaires. By denoting Lsite and Lmon, with appropriate superscripts (1) and (2) for indicating the pre- and post-upgrade scenarios, the available information (surface area for the specific site and specific monitoring area), the extrapolated energy use may be computed as

The energy savings obtained with the upgrade of the system may be computed

Expression (9) represents the aggregate energy saving computed over the different classes, i.e. the difference between expressions (6) and (7), which are, as explained above, extrapolated versions of expressions (3) and (5), respectively.

By computing the aggregate average energy use for the post-upgrade sequence by using the duration per class for the pre-upgrade sequence and the average energy use per class for the post-upgrade sequence, computation of the energy saving takes better into account of the operational conditions which affect energy use in the pre-upgrade lighting system and post-upgrade lighting system and which are changing between the pre-upgrade lighting system and the post-upgrade lighting system.

This allows a more accurate computation of the energy savings because the aggregate average energy use for the post-upgrade sequence is computed taking into account the operational conditions encountered for the pre-upgrade sequence. This means that comparison between energy use in pre-upgrade scenario and energy use in the post-upgrade scenario is fairer. Further, the inventors have realized that by using the operational conditions of the pre-upgrade scenario for computing the aggregate energy use for the post-upgrade scenario, energy savings can be computed with good accuracy even when monitoring the energy use in the pre-upgrade and the post-upgrade system for relatively short time periods. The system can be validated at reduced costs and with less effort. For example, Figure 2c shows a table of energy measurements of a lighting system before and after an upgrade for the embodiment described above. Figure 2c shows part of the data used to draw figures 2a and 2b.

Table of Figure 2c shows hourly energy measurements during different time of a monitoring week. Energy measurements are expressed in Watt-hours. However, other energy units may be used, for example the energy measurements may be expressed in Joule, Joule per second or an equivalent thereof. Each measurement has a corresponding situation pre-upgrade and a corresponding situation post-upgrade. Each measurement is thus effected at a specific time in a week, for example on Wednesday at 6:00 o'clock, before and after the upgrade, respectively.

Each measurement is assigned a class which in this example can be one of the following classes: low energy use period indicated with LOW, transitory energy use period indicated with TRAN and high energy use period indicated with HIGH. By using the table shown in Figure 2c, average energy use per class can be computed by the processor circuit. The aggregated average energy use over all classes can be computed according to equations (3) and (5) as explained above.

Note that additional normalization may also be done to account for differences in light levels over the pre- and post-upgrade scenarios, if light values are measured or may be estimated.

For example, in an embodiment, the difference between the inner product of the average energy use per class for the first sequence and the duration per class for the first sequence, and the inner product of the average energy use per class for the second sequence and the duration per class for the first sequence, is a weighted difference. For example, the inner product of the average energy use per class for the first sequence and the duration per class for the first sequence is weighted with a factor less than 1. Alternatively or in combination, the inner product of the average energy use per class for the second sequence and the duration per class for the first sequence is weighted with a factor more than 1. The weighted difference is smaller than the difference computed without taking into account the weighting factors which means that estimation of the energy savings computed by taking into account the weighting factors is less than the estimation of the energy savings computed by not taking into account the weighting factors. This could be used to compensate for extra changes in the operational conditions during the pre-upgrade sequence and the post-upgrade sequence. For example, the pre- and post-upgrade lighting system may be monitored in two different periods of different seasons. For example, one monitoring period may fall in summer and the other one in winter. In this latter example, energy use between the two periods would be substantially different even by monitoring either only the pre-upgrade lighting system or only the post-upgrade lighting system in the two periods. In these cases compensation effected by introducing weighting factors in the computation may contribute to more realistic energy saving estimations.

In an embodiment, the building automation system has not been upgraded between the first time period and the second time period and the processor circuit is configured to send an alarm signal if the absolute value of the computed difference is larger than a threshold.

It is not necessary that there has been an upgrade of the system between a first time period and a second time period. The measurement device may be used to monitor the same energy using system during different time periods, e.g., in which operational factors are substantially changed. For example, the measurement device may be used to monitor energy use in an air cooling system. Energy use may be monitored in two different time periods during the same year, for example during a first period falling in summer and during a second falling in another season, for example in spring or autumn. Energy use in summer may be much higher than energy use of the air cooling system in, e.g., spring. A fair comparison of the energy use of the air cooling system during the two periods may be provided by the measurement device described above. A large energy difference, for example larger than the threshold, may indicate a defect in the air cooling system which triggers an alarm signal to be sent to the installer or maintainer of the air cooling system.

By computing the absolute value of the difference between the inner product of the average energy use per class for the first sequence and the duration per class for the first sequence and the inner product of the average energy use per class for the second sequence and the duration per class for the first sequence and controlling whether said absolute value is larger or not than a threshold, the measurement device of the invention allows to warn of possible defects in the system. When said absolute value is larger than the threshold, an alarm signal is sent indicating that a defect in the system has been detected. In this way detection of defects in the system is possible even by monitoring a system which has not been upgraded in two different periods of the year wherein energy use is typically expected to be substantially different.

Figure 3 a schematically shows an example of a partition of a time period 310 before an upgrade. In figures 3a, and also in figure 3b below, time runs from left to right. The time period 310, e.g., a week, is partitioned into multiple shorted periods. Each of the shorter time periods is classified according to a classification scheme. In this case only energy use is taken into account.

For example, in figure 3a:

Monday 0:00 (311) a first shorter period with classification 'low' is started,

Monday 07:00 (312) a second shorter period with classification 'peak' is started,

Monday 20:00 (313) a third shorter period with classification 'transition' is started,

Monday 21 :00 (314) a fourth shorter period with classification 'low' is started,

Tuesday 07:00 (315) a fifth shorter period with classification 'tran' is started,

Tuesday 08:00 (316) a sixth shorter period with classification 'peak' is started,

Etc.

Figure 3b schematically shows an example of a partition of a time period after an upgrade. The time period 320, e.g., also a week, is partitioned into multiple shorted periods. Each of the shorter time periods is classified according to the same classification scheme as in figure 3a. The time periods 310 and 320 may have a different length.

For example, in figure 3b:

Monday 0:00 (311) a first shorter period with classification 'low' is started,

Monday 08:00 (312) a second shorter period with classification 'peak' is started,

Monday 19:00 (313) a third shorter period with classification 'transition' is started,

Monday 20:00 (314) a fourth shorter period with classification 'low' is started,

Tuesday 08:00 (315) a fifth shorter period with classification 'peak' is started,

Etc.

Partition of Figure 3a and 3b may be performed by partition unit 132 described with reference to Figure 1.

The amount of time visibly represented for time periods 310 and 320 in figures

3a and 3b is the same, but otherwise figures 3a and 3b are now drawn to scale. Figures 2a, 2b, 2c, 3a and 3b are all based on the same data.

In each of the shorter periods shown in figures 3a and 3b all energy

measurement have the same classification. Sometimes it happens that a shorter time period only contains a single measurement. This can happen, e.g., if the number of measurement points is relatively infrequent, e.g., once per hour, as in this example.

Figure 4 schematically shows a flow diagram of an energy measurement method 400. Energy measurement method 400 may be performed by measurement device 100 of Figure 1. The energy measurement method comprises:

(a) receiving 410 a first and second sequence of energy use measurements, the first sequence extending over a first time-period, the second sequence extending over a second time-period;

(b) partitioning 420 the first and second time-period into multiple shorter time- periods, a shorter time-period being assigned to a class of multiple classes depending at least on the size of the energy use measurements falling in the shorter time-period;

(c) computing 430 an average energy use per class for the first sequence by averaging energy use measurements of the first sequence falling in the same class, and (d) computing 440 an average energy use per class for the second sequence by averaging energy use measurements of the second sequence falling in the same class;

(e) computing 450 a duration per class for the first sequence by adding the duration of the shorter period in the first time-period assigned to the class; and

(f) computing 460 a difference between the inner product of the average energy use per class for the first sequence and the duration per class for the first sequence and the inner product of the average energy use per class for the second sequence and the duration per class for the first sequence.

Method 400 may be performed for monitoring energy use in a building automation system, lighting system or in energy using system wherein, for example, it is desired to estimate the impact of implementing energy saving measures in the overall energy use of the system, or to identify failures in the system. Any of the steps (a)-(f) may be performed by processor circuit 130.

In the various embodiments, input 120 may be selected from various alternatives. For example, input may be a network interface to a local or wide area network, e.g., the Internet, a storage interface to an internal or external data storage, a keyboard, etc.

Typically, the measurement device 100 comprises a microprocessor (not separately shown) which executes appropriate software stored at the device; for example, that software may have been downloaded and/or stored in a corresponding memory, e.g., a volatile memory such as RAM or a non- volatile memory such as Flash (not separately shown). The device 100 may also be equipped with microprocessors and memories (not separately shown). Alternatively, the device 100, in whole or in part, be implemented in programmable logic, e.g., as field-programmable gate array (FPGA). Devices 100 may be implemented, in whole or in part, as a so-called application- specific integrated circuit (ASIC), i.e. an integrated circuit (IC) customized for their particular use. For example, the circuits may be implemented in CMOS, e.g., using a hardware description language such as Verilog, VHDL etc.

A processor circuit may be implemented in a distributed fashion, e.g., as multiple sub-processor circuits. A storage may be distributed over multiple distributed sub- storages. Part or all of the memory may be an electronic memory, magnetic memory, etc. For example, the storage may have volatile and a non- volatile part. Part of the storage may be read-only.

Many different ways of executing the method 400 are possible, as will be apparent to a person skilled in the art. For example, the order of the steps can be varied or some steps may be executed in parallel. Moreover, in between steps other method steps may be inserted. The inserted steps may represent refinements of the method such as described herein, or may be unrelated to the method. For example, steps 430 and 440 may be executed, at least partially, in parallel. Moreover, a given step may not have finished completely before a next step is started.

A method according to the invention may be executed using software, which comprises instructions for causing a processor system to perform method 400. Software may only include those steps taken by a particular sub-entity of the system. The software may be stored in a suitable storage medium, such as a hard disk, a floppy, a memory, an optical disc, etc. The software may be sent as a signal along a wire, or wireless, or using a data network, e.g., the Internet. The software may be made available for download and/or for remote usage on a server. A method according to the invention may be executed using a bitstream arranged to configure programmable logic, e.g., a field-programmable gate array (FPGA), to perform the method.

It will be appreciated that the invention also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the invention into practice. The program may be in the form of source code, object code, a code intermediate source, and object code such as partially compiled form, or in any other form suitable for use in the implementation of the method according to the invention. An embodiment relating to a computer program product comprises computer executable instructions corresponding to each of the processing steps of at least one of the methods set forth. These instructions may be subdivided into subroutines and/or be stored in one or more files that may be linked statically or dynamically. Another embodiment relating to a computer program product comprises computer executable instructions corresponding to each of the means of at least one of the systems and/or products set forth. Figure 5 a shows a computer readable medium 1000 having a writable part 1010 comprising a computer program 1020, the computer program 1020 comprising instructions for causing a processor system to perform an energy measurement method 400 according to an embodiment described with reference to Figure 4. The computer program 1020 may be embodied on the computer readable medium 1000 as physical marks or by means of magnetization of the computer readable medium 1000. However, any other suitable embodiment is conceivable as well. Furthermore, it will be appreciated that, although the computer readable medium 1000 is shown here as an optical disc, the computer readable medium 1000 may be any suitable computer readable medium, such as a hard disk, solid state memory, flash memory, etc., and may be non-recordable or recordable. The computer program 1020 comprises instructions for causing a processor system to perform said energy measurement method 400.

Figure 5b shows in a schematic representation of a processor system 1140 according to an embodiment. The processor system comprises one or more integrated circuits 1110. The architecture of the one or more integrated circuits 1110 is schematically shown in Figure 5b. Circuit 1110 comprises a processing unit 1120, e.g., a CPU, for running computer program components to execute a method according to an embodiment and/or implement its modules or units. Circuit 1110 comprises a memory 1122 for storing programming code, data, etc. Part of memory 1122 may be read-only. Circuit 1110 may comprise a

communication element 1126, e.g., an antenna, connectors or both, and the like. Circuit 1110 may comprise a dedicated integrated circuit 1124 for performing part or all of the processing defined in the method. Processor 1120, memory 1122, dedicated IC 1124 and communication element 1126 may be connected to each other via an interconnect 1130, say a bus. The processor system 1110 may be arranged for contact and/or contact-less communication, using an antenna and/or connectors, respectively.

For example, in an embodiment, energy measurement device may comprise in addition to processor circuit 130, a memory circuit, processor circuit 130 being arranged to execute software stored in the memory circuit. For example, processor circuit 130 may be an Intel Core \Ί processor, ARM Cortex-R8, etc. The memory circuit may be an ROM circuit, or a non-volatile memory, e.g., a flash memory. The memory circuit may be a volatile memory, e.g., an SRAM memory. In the latter case, the verification device may comprise a nonvolatile software interface, e.g., a hard drive, a network interface, etc., arranged for providing the software. It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. Use of the verb "comprise" and its conjugations does not exclude the presence of elements or steps other than those stated in a claim. The article "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

In the claims references in parentheses refer to reference signs in drawings of exemplifying embodiments or to formulas of embodiments, thus increasing the intelligibility of the claim. These references shall not be construed as limiting the claim.