FIELD OF THE INVENTION
[001] The present invention relates to a method for Solar Power Forecasting. The invention more particularly relates a system and method with machine learning and Temporal Convolutional Neural Network for Solar Power Forecasting.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Further, the approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
[004] In current scenarios, solar energy production is a non-dispatchable power resource, meaning that energy production cannot be turned on or off in a relatively short amount of time in order to meet a demand. Other forms of non-dispatchable include wind power and wave energy.
[005] Variability of non-dispatchable renewable energy can pose challenges for electric grid operators due to uncertainty in determining the amount of energy that may be provided by the resource. System operators need to ensure that they have sufficient resources to accommodate significant up or down ramps in renewable generation to maintain system balance.
TCNN is a novel convolutional architecture designed for sequential modelling, which combines causal and dilated convolutions and residual connections. We compare the performance of TCNN with multi-layer feedforward neural networks, and also with recurrent networks, including the state-of-the-art LSTM and GRU recurrent networks. The evaluation is conducted on two Australian datasets containing historical solar and weather data, and weather forecast data for future days. Our results show that TCNN outperformed the other models in terms of accuracy and was able to maintain a longer effective history compared to the recurrent networks. This highlights the potential of convolutional architectures for solar power forecasting tasks.
[006] Accordingly, on the basis of aforesaid facts, there remains a need in the prior art to provide a system with machine learning and Temporal Convolutional Neural Network for Solar Power Forecasting. Therefore, it would be useful and desirable to have a system, method, apparatus and interface to meet the above-mentioned needs.
SUMMARY OF THE PRESENT INVENTION
[007] According to the present invention, a system and method with machine learning and Temporal Convolutional Neural Network for Solar Power Forecasting.
[008] Referring now to the drawings, these are illustrated in FIG. 1, the present invention that includes receiving four-dimensional (4D) weather forecast data, the 4D weather forecast data including a plurality of weather features. The method further includes processing the 4D weather forecast data using a chain of a plurality of processing blocks of a neural network to derive one or more of the plurality of weather features. Each of the plurality of processing blocks includes a convolutional layer, an activation layer, and a pooling layer. The convolution layer associates at least one filter to a region of the 4D weather forecast data across a plurality of layers in the 4D weather forecast data. The method further includes determining a solar power forecast for a predetermined location based upon the one or more derived weather features.
[009] A method for predicting photovoltaic power generation based on sliding window and local time series features. First, use one-dimensional convolutional neural network CNN with attention module to extract the spatial characteristics of photovoltaic power data, and then use long and short-term memory network LSTM to extract the space obtained The feature performs time series feature learning, extracts the time series feature, and uses the fully connected layer to perform regression prediction analysis on the extracted time series feature to obtain the final photovoltaic power prediction result.
The one-dimensional convolutional neural network CNN includes four convolutional layers and two pooling layers. After every two convolutional layers, a pooling layer is connected, and the attention module is embedded in the fourth convolutional layer.
[010] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[011] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[012] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[013] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element or group of elements with transitional phrases “consisting of”, “consisting”, “selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.
[014] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[015] The 4D weather forecast data includes two-dimensional position data, time data, and weather feature data which include forecast value data. Weather feature data includes weather forecast products generated from weather data measurements. In one or more embodiments, the weather forecast data is represented as an image raster containing weather forecast data including a plurality of weather forecast features to be mapped in a feature space of the temporal convolutional neural network. The weather forecast features include the weather forecast data to be processed by the temporal convolutional neural network, and the feature space is the space within the neural network in which the weather features are mapped to allow processing of the weather forecast data by the neural network. In particular embodiments, the four dimensions of the weather forecast data includes latitude data, longitude data, time series data, and weather feature data in a feature space.
The use of 4D weather forecast data to forecast solar power provides more accurate forecast than can be obtained by traditional methods. In traditional methods of solar power forecasting, the use of 4D weather forecast data is too computationally complex to be practical. The illustrative embodiments recognize that the use of 4D weather forecast data in a solar power forecast model allows for cloud cover distribution as well as the temporal evolution of cloud movement to be considered to provide a more accurate solar power forecast. However, the use of 4D weather data requires significant processing power in order to calculate an accurate solar power prediction. In accordance with one or more embodiments, a neural network based deep learning is used to calculate a solar power forecast using 4D weather forecasting data. In a particular embodiment, a temporal convolutional neural network is used to calculate a solar power forecast using the 4D weather data.
[016] In accordance with another embodiment of the present invention, each processing block, the convolutional layer processes the 4D weather forecast data and provides the processed 4D weather forecast data to the activation layer, the activation layer further processing the weather forecast data and providing the processed weather forecast data to the pooling layer.
wherein the neural network includes a temporal convolutional neural network.
[017] In accordance with another embodiment of the present invention, wherein one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising:
program instructions to receive four-dimensional (4D) weather forecast data, the 4D weather forecast data including a plurality of weather features;
program instructions to process the 4D weather forecast data using a chain of a plurality of processing blocks of a neural network to derive one or more of the plurality of weather features, each of the plurality of processing blocks including a convolutional layer, an activation layer, and a pooling layer, wherein the convolution layer associates at least one filter to a region of the 4D weather forecast data across a plurality of layers in the 4D weather forecast data; and
program instructions to determine a solar power forecast for a predetermined location based upon the one or more derived weather features.
[018] In accordance with another embodiment of the present invention, the processing unit includes, but not limited to, a processor and a storage medium; wherein the storage medium stores a computer program adapted to be loaded by the processor and to perform the method steps of any of the above embodiments.
[019] Further, the exemplary computer system for implementing various embodiments consistent with the present disclosure, which may be used for implementing a method for Artificial neural network based photovoltaic module diagnosis by current–voltage curve classification. Computer system may comprise a central processing unit (“CPU” or “processor”). Processor may comprise at least one data processor for executing program components for executing user or system-generated requests. A user may include a person, a person using a device such as such as those included in this disclosure, or such a device itself. The processor may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM’s application, embedded or secure processors, IBM PowerPC, Intel’s Core, Itanium, Xeon, Celeron or other line of processors, etc. The processor may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), Field Programmable Gate Arrays (FPGAs), etc.
[020] Processor may be disposed in communication with one or more input/output (I/O) devices via I/O interfaces. The I/O interfaces may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.
[021] In some embodiments, the processor may be disposed in communication with one or more memory devices (e.g., RAM, ROM, etc.) via a storage interface. The storage interface may connect to memory devices including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), integrated drive electronics (IDE), IEEE-1394, universal serial bus (USB), fiber channel, small computer systems interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, redundant array of independent discs (RAID), solid-state memory devices, solid-state drives, etc. The memory devices may store a collection of program or database components, including, without limitation, an operating system, user interface application, web browser, mail server, mail client, user/application data (e.g., any data variables or data records discussed in this disclosure), etc. The operating system may facilitate resource management and operation of the computer system. Examples of operating systems include, without limitation, Apple Macintosh OS X, Unix, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBM OS/2, Microsoft Windows, Apple iOS, Google Android, Blackberry OS, or the like.
[022] The word “module,” “model” “algorithms” and the like as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as, for example, Java, C, Python or assembly. One or more software instructions in the modules may be embedded in firmware, such as an EPROM. It will be appreciated that modules may comprised connected logic units, such as gates and flip-flops, and may comprise programmable units, such as programmable gate arrays or processors. The modules described herein may be implemented as either software and/or hardware modules and may be stored in any type of computer-readable medium or other computer storage device. Further, in various embodiments, the processor is one of, but not limited to, a general-purpose processor, an application specific integrated circuit (ASIC) and a field-programmable gate array (FPGA) processor. Furthermore, the data repository may be a cloud-based storage or a hard disk drive (HDD), Solid state drive (SSD), flash drive, ROM or any other data storage means.
[023] The above-mentioned invention is provided with the preciseness in its real-world applications to provide a System for Leakage minimization and power optimization of Low Noise Amplifier and DAC in semiconductor circuits for VLSI application wherein a 4 bit R-2R DAC comprising of three PMOS transistors and five NMOS transistors and Two-input NAND gate and inverter are designed using these techniques and compared in Cadence Virtuoso at 90nm technology.
[024] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[025] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[026] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.

We Claims:

1. A method with machine learning and Temporal Convolutional Neural Network for Solar Power Forecasting comprising:
receiving four-dimensional (4D) weather forecast data, the weather forecast data including a plurality of weather features;
processing the 4D weather forecast data using a chain of a plurality of processing blocks of a neural network to derive one or more of the plurality of weather features, each of the plurality of processing blocks including a convolutional layer, an activation layer, and a pooling layer, wherein the convolution layer associates at least one filter to a region of the 4D weather forecast data across a plurality of layers in the 4D weather forecast data; and
determining a solar power forecast for a predetermined location based upon the one or more derived weather features.
2. The method as claimed in claim 1, wherein for each processing block, the convolutional layer processes the 4D weather forecast data and provides the processed 4D weather forecast data to the activation layer, the activation layer further processing the weather forecast data and providing the processed weather forecast data to the pooling layer.
3. The method as claimed in claim 1, wherein the neural network includes a temporal convolutional neural network.
4. The method as claimed in claim 1, wherein one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising:
program instructions to receive four-dimensional (4D) weather forecast data, the 4D weather forecast data including a plurality of weather features;
program instructions to process the 4D weather forecast data using a chain of a plurality of processing blocks of a neural network to derive one or more of the plurality of weather features, each of the plurality of processing blocks including a convolutional layer, an activation layer, and a pooling layer, wherein the convolution layer associates at least one filter to a region of the 4D weather forecast data across a plurality of layers in the 4D weather forecast data; and
program instructions to determine a solar power forecast for a predetermined location based upon the one or more derived weather features.