DATA ANALYTICS AND MACHINE LEARNING FIELD OF THE INVENTION

Embodiments of the present invention generally relate to the field of weather forecasting, and, more particularly, to systems and methods for forecasting weather by using data analytics and machine learning.

BACKGROUND OF THE INVENTION

It is well known that real-time weather forecast data is accessible to businesses and developers through Application Programming Interfaces (APIs), provided by weather services such as Weather Underground, Accuweather and Forecast. io. This weather forecast data becomes a crucial variable to many businesses and interests, and ultimately determines huge financial gains and losses across all industries. As a result, this forecast data needs to be as accurate as possible and must have reliable data availability. Due to the inherent complexity of meteorology, weather forecasts contain a degree of uncertainty and are rarely perfect.

In addition, it is also evident that forecasts have improved over time, due to improvements made to Numerical Weather Prediction Models (NWPs), increased observational data and greater computational power. However, forecasts still contain errors. These uncertainties vary from one weather API provider to the next, due to differences in each providers methods and strategies, such as, for example:

(i) the use of different NWP models; and,

(ii) different observational data; updates times; etc. Consequently, as a result, the accuracy of each provider varies, and these accuracies are also location dependent, weather variable dependent and use case dependent.

Furthermore, weather APIs from individual weather services provide forecast data with reasonable accuracies, and are constantly attempting to improve their forecast output data and provide greater accuracies to their customers. This includes:

• Using an ensemble of NWP models and find the best combination of these NWP model outputs.

· Using greater amount of observational data and ensuring the data is clean and valid.

Additionally, the accuracies of Weather APIs are limited to the NWP models that feed into the providers own ensemble model. This means any errors in the NWP models will also be present in the Weather API output, depending on the degree which each NWP model is weighted. Observational data is an important input to NWP models. Some regions contain more observational data than others. For example, India lacks observations as compared with the spread of observations in USA and Europe. This effects the model outputs, especially impacting the accuracy of short-term forecasting.

However, present Weather APIs are not location-specific. Instead, the same methodologies and strategies are deployed in a global model. This means that the model parametrization schemes may be applicable to some regions, but not to others. These parametrizations are often set to suit regions with their greatest customer base (e.g. USA, Europe), and so the parametrizations are unsuitable for other regions. This leads to large biases in various locations globally. As a result of the above points, output from Weather APIs tend to contain bias and this bias fails to get adjusted in the short-term. This causes over/under - forecasting to be present and persist over several hours.

Accordingly, there remains a need in the art for innovative, novel, collaborative and interactive solutions providing systems and methods for forecasting weather by using data analytics and machine learning.

SUMMARY OF THE INVENTION

In accordance with an embodiment of the present invention, a method for forecasting weather by using data analytics and machine learning includes the steps of, retrieving a weather forecast information from a plurality of sources for one or more locations to create a plurality of datasets; applying machine learning algorithms on the plurality of datasets; comparing the weather forecast information retrieved from the plurality of sources with observational data obtained from the one or more locations to create a data analytics based forecast; and, combining the weather forecast information retrieved from the plurality of sources with the data analytics based forecast to create a weather forecast combiner output.

In accordance with an embodiment of the present invention, the method further includes the step of calculating at least one error in the weather forecast combiner output by comparing the weather forecast combiner output with the observational data obtained from the one or more locations. In use, the at least one error is likely to occur in future. The embodiments of the present disclosure have several features, no single one of which is solely responsible for their desirable attributes. Without limiting the scope of the present embodiments as expressed by the claims that follow, their more prominent features will now be discussed briefly. After considering this discussion, and particularly after reading the section entitled “Detailed Description”, one will understand how the features of the present embodiments provide advantages, which include providing systems and methods for forecasting weather by using data analytics and machine learning.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 illustrates a flow diagram of a method for forecasting weather by using data analytics and machine learning, according to an embodiment of the invention;

FIG. 2 illustrates a system architecture of a system for identification and verification of a product, according to an embodiment of the invention; and, FIG. 3A and FIG. 3B illustrate pictorial representation of results produced by systems and methods for identification and verification of a product, according to an embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

Various embodiments of the present invention are disclosed herein below, which relate to systems and methods for forecasting weather by using data analytics and machine learning.

Generally, it is well known that there are many resources for providing a service of very accurate, short-term weather forecast data, with global coverage and client-specific. This is provided in various formats via the internet - through an API, via FTP, or via downloadable data files. This weather forecast data has been tried and tested in the energy industry, such as for use in electricity load forecasting. However, it’s capabilities are not limited to the energy industry and is an important source of accurate weather forecast data for numerous business needs, as described hereinbelow.

Various systems and methods in accordance with multiple embodiments of this invention are novel and inventive because they optimize weather forecasts for a specific latitude and longitude, by using real-time observational data and applying machine-learning algorithms to provide highly accurate weather forecast data.

Those of ordinary skills in the art will appreciate that not even a single solution offered in prior art discloses the concept of applying machine learning algorithms to multiple Weather APIs to improve upon individual Weather API sources in a location-specific manner. It will be further appreciated that the use of machine learning algorithms requires advanced knowledge of how these algorithms works and how they can be applied to datasets. These algorithms are complex and require extensive testing and training to give successful outcomes. The optimization of very-short-term forecasts, by predicting the future error in the forecast, is an innovative solution to an obvious problem. The error in forecasts is not constant and tends to have little pattern or repetition. As a result, predicting the future error in the forecast cannot be judged by eye and is only possible through advanced statistical techniques in combination with novel and inventive aspects, as presented herein. In accordance with multiple embodiments of the present invention, the systems and methods as disclosed herein are aimed at providing multiple features to the users, such as, for example, but not limited to, increasing the accuracy of weather forecasts, particularly during extreme weather events.

Those of ordinary skills in the art will appreciate that the accuracy of weather forecasts is increased by combining the output of multiple weather service Application programming interface (hereinafter referred to as the“APIs”) and applying machine learning protocols (algorithms, applications etc.) to learn how much weighting to give each weather forecast provider, and hence improving upon the data provided by one single provider.

Various embodiments of the present invention are further aimed at providing systems and methods to the users that are capable of providing solutions for one or more specific locations required and for each weather variable of concern.

In accordance with multiple embodiments of the present invention, the systems and methods as disclosed herein disclose the novel and inventive aspect of a ‘weather forecast combiner’, which is further configured for very-short-term forecasting (such as, for example, but not limited to, 3 hours ahead) by comparing the recent weather forecast combiner output with the latest observational data for that same location. Subsequently, embodiments of the present invention predict the error in the forecast output for future timeblocks (such as, for example, but not limited to, the next 3 hours), thereby allowing the weather combiner output to be adjusted for these future timeblocks, as described hereinbelow. In accordance with an embodiment of the present invention, the Weather Forecast Combiner as disclosed hereinbelow functions in a manner such that the weather forecasts of multiple APIs from various global weather services are collected for the location of interest. Machine learning algorithms are applied to these datasets in order to understand how each individual provider compares with the observational data of the same location. Large historic datasets of these forecasts are used to train the model and the algorithms learn how much weighting to give each individual forecast provider in future outputs of the combined forecasts, as described below.

In accordance with an embodiment of the present invention, a very-short-term optimization of Weather Forecast Combiner works in a manner such that the recent output from the Weather Forecast Combiner is compared against the recent observational data, in order to predict the likely error that will occur in the future (next 3 hours) output from the Weather Forecast Combiner. This is achieved through linear regression of the recent error data, as described below. FIG. 1 illustrates a flow diagram of a method for forecasting weather by using data analytics and machine learning, according to an embodiment of the invention.

In accordance with an embodiment of the present invention, a method for forecasting weather by using data analytics and machine learning includes the steps of, retrieving a weather forecast information from a plurality of sources for one or more locations to create a plurality of datasets; applying machine learning algorithms on the plurality of datasets; comparing the weather forecast information retrieved from the plurality of sources with observational data obtained from the one or more locations to create a data analytics based forecast; and, combining the weather forecast information retrieved from the plurality of sources with the data analytics based forecast to create a weather forecast combiner output.

In accordance with an embodiment of the present invention, the method further includes the step of calculating at least one error in the weather forecast combiner output by comparing the weather forecast combiner output with the observational data obtained from the one or more locations. In use, the at least one error is likely to occur in future.

In accordance with an embodiment of the present invention, the at least one error is calculated through linear regression of recent error data. In use, the weather forecast combiner output and the at least one error are transmitted through a wired or wireless link to a communication network.

FIG. 2 illustrates a system architecture of a system for identification and verification of a product, according to an embodiment of the invention. In accordance with an embodiment of the present invention, a system 200 for forecasting weather by using data analytics and machine learning includes at least one processor; and, a plurality of computer-executable components for execution in the at least one processor including: a retrieving component 202 for retrieving a weather forecast information from a plurality of sources (2011 , 201 ₂ , 203 ₃ ...) for one or more locations to create a plurality of datasets (204i ,

204 ₂ , 204 ₃ ...); a machine learning component 206 for applying machine learning algorithms on the plurality of datasets (204i, 204 ₂ , 204 ₃ ...); a comparison component 208 for comparing the weather forecast information retrieved from the plurality of sources (2011, 201 ₂ , 203 ₃ ...) with observational data obtained from the at least one location to create a data analytics based forecast; and, a weather combiner component 210 for combining the weather forecast information retrieved from the plurality of sources (2011, 2012, 2033...) with the data analytics based forecast to create a weather forecast combiner output.

In accordance with an embodiment of the present invention, the plurality of computer-executable components of the system further includes an error calculator component 212 for calculating at least one error in the weather forecast combiner output by comparing the weather forecast combiner output with the observational data obtained from the one or more locations. In use, the at least one error is likely to occur in future.

In accordance with an embodiment of the present invention, the error calculator component 212 calculates the at least one error through linear regression of recent error data.

In accordance with an embodiment of the present invention, the plurality of computer-executable components of the system further includes a communication component for transmitting the weather forecast combiner output and the at least one error through a wired or wireless link to a communication network, which encompasses communication networks such as local-area networks (LAN) and wide-area networks (WAN). LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and the like. WAN technologies include, but are not limited to, point-to-point links, circuit switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet switching networks, and Digital Subscriber Lines (DSL).

Those of ordinary skills in the art will appreciate that systems and methods as disclosed by various embodiments of the present invention may require an exemplary environment for implementing various aspects of the invention, including a computer. The computer includes a processing unit, a system memory, and a system bus. The system bus couples system components including, but not limited to, the system memory to the processing unit. The processing unit can be any of various available processors. Dual microprocessors and other multiprocessor architectures also can be employed as the processing unit. The system bus can be any of several types of bus structure(s) including the memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any variety of available bus architectures including, but not limited to, 8-bit bus, Industrial Standard Architecture (ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), Universal Serial Bus (USB), Advanced Graphics Port (AGP), Personal Computer Memory Card International Association bus (PCMCIA), and Small Computer Systems Interface (SCSI). The system memory includes volatile memory and non-volatile memory. The basic input/output system (BIOS), containing the basic routines to transfer information between elements within the computer, such as during start-up, is stored in non-volatile memory. By way of illustration, and not limitation, non- volatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory includes random access memory (RAM), which acts as external cache memory.

By way of illustration and not limitation, RAM is available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). In accordance with an embodiment of the present invention, the Computer also includes removable/nonremovable, volatile/non-volatile computer storage media.

It is to be appreciated that the exemplary environment as disclosed herein describes an intermediary between users and the basic computer resources described in suitable operating environment.

FIG. 3A and FIG. 3B illustrate pictorial representation of results produced by systems and methods for identification and verification of a product, according to an embodiment of the invention. Those of ordinary skills in the art will appreciate that systems and methods as disclosed by various embodiments of the present invention provide significant advantages over prior art, such as, for example, but not limited to, reducing the inaccuracies of presently available solutions and reducing the forecast errors, especially in the very short-term time horizon (next 5 hours), providing 100% data availability and reliability, increasing the accuracy of weather forecasts, particularly during extreme weather events by combining the output of multiple weather service APIs and applying machine learning algorithms to learn how much weighting to give each weather forecast provider, and hence improving upon the data provided by one single provider. Conditional language used herein, such as, among others, “can,” “could,” “might,”“may,”“e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms "comprising," "including," 'having," and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term "or" is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term "or" means one, some, or all of the elements in the list.

While there has been shown and described the preferred embodiment of the instant invention it is to be appreciated that the invention may be embodied otherwise than is herein specifically shown and described and that, within said embodiment, certain changes may be made in the form and arrangement of the parts without departing from the underlying ideas or principles of this invention as set forth in the Claims appended herewith. Therefore, the appended claims are to be construed to cover all equivalents falling within the true scope and spirit of the invention.