Electronic device and method for predicting solar power generation

Abstract

Provided is an electronic device comprising: a memory; and a processor operatively connected to the memory, wherein the processor is configured to: receive multiple pieces of weather forecast data; acquire multiple pieces of characteristic data on the basis of the multiple pieces of weather forecast data; acquire integrated characteristic data obtained by integrating the multiple pieces of characteristic data on the basis of similarity between the multiple pieces of characteristic data; and generate a prediction value of solar power generation on the basis of the integrated characteristic data.

Description

Electronic device and method for predicting solar power generation

[0001] The present disclosure relates to an electronic device and method for predicting solar power generation.

[0002] As demand for renewable energy grows, interest in solar power generation is also growing. Solar power, a renewable energy source, is expected to play a crucial role in achieving greenhouse gas reduction goals, including combating climate change, in the Fourth Industrial Revolution. Accordingly, consistent support policies and technological advancements for the solar power industry are improving the lifespan and efficiency of solar panels, and solar power-related equipment is also becoming more sophisticated.

[0003] Meanwhile, the growing demand for renewable energy sources like solar power is highlighting the importance of energy management. Energy management focuses on balancing supply and demand. Unlike thermal power, whose supply can be freely adjusted, renewable energy sources transform natural forces into energy, requiring supply forecasting.

[0004] Many recent studies have proposed methods for predicting solar power generation using artificial neural networks. However, due to the uncertainty and volatility of weather forecasts, prediction accuracy and performance remain inconsistent. For example, existing methods for predicting solar power generation utilize weather observation data to train a prediction model and then predict solar power generation using only a single type of weather forecast data. However, this approach limits the available weather forecast data as input to the prediction model. In particular, using only a single forecast data source can limit the available information, potentially limiting solar power generation predictions. Furthermore, the performance of the prediction model is heavily dependent on the accuracy of the weather forecast data. If the forecast data differs significantly from the actual weather observation data, the prediction model is likely to output significantly different power generation output. Consequently, there is a pressing need to develop technologies that can improve the accuracy and stability of solar power generation predictions.

[0005] The present disclosure provides an electronic device and method for predicting solar power generation to solve the above-described problems.

[0006] The present disclosure can be implemented in various ways, including a computer-readable, non-transitory recording medium having recorded thereon methods, devices (systems), and / or instructions.

[0007] According to one embodiment of the present disclosure, an electronic device includes a memory and a processor operatively connected to the memory, wherein the processor is configured to receive a plurality of weather forecast data, obtain a plurality of characteristic data based on the plurality of weather forecast data, obtain integrated characteristic data that integrates the plurality of characteristic data based on similarities between the plurality of characteristic data, and generate a predicted value of a solar power generation amount based on the integrated characteristic data.

[0008] According to one embodiment, the plurality of weather forecast data may include first weather forecast data including weather prediction values ​​for a first time interval within a specified time range and second weather forecast data including weather prediction values ​​for a second time interval longer than the first time interval within the specified time range.

[0009] According to one embodiment, the processor is configured to obtain an embedding vector corresponding to each of a plurality of weather forecast data as a plurality of characteristic data, wherein the embedding vector includes time series information of weather forecast data corresponding to the embedding vector among the plurality of weather forecast data and may have a specified length.

[0010] According to one embodiment, the processor may be configured to generate an embedding vector using an attention-based LSTM.

[0011] According to one embodiment, the processor may be configured to generate a query matrix, a key matrix, and a value matrix corresponding to each of the plurality of characteristic data, which are used in the attention model, based on an embedding vector corresponding to each of the plurality of weather forecast data.

[0012] According to one embodiment, the processor may be configured to calculate a first attention score indicating a similarity between a query and a key based on a key matrix associated with first characteristic data among the plurality of characteristic data and a query matrix associated with second characteristic data among the plurality of characteristic data, calculate a first attention value based on the first attention score and a value matrix associated with the first characteristic data, calculate a second attention score indicating a similarity between the query and a key based on the key matrix associated with the second characteristic data and the query matrix of the first characteristic data, calculate a second attention value based on the second attention score and a value matrix associated with the second characteristic data, and generate integrated characteristic data by connecting the first attention value and the second attention value.

[0013] In one embodiment, the processor may be configured to apply integrated characteristic data to the transformer model.

[0014] According to one embodiment, the processor may be configured to generate a predicted value of solar power generation through a multilayer perceptron model using the integrated feature data.

[0015] According to one embodiment, the activation function of the multilayer perceptron may include a leaky ReLU function.

[0016] According to one embodiment of the present disclosure, a method for predicting solar power generation, performed by at least one processor, may include the steps of receiving a plurality of weather forecast data, obtaining a plurality of characteristic data based on the plurality of weather forecast data, obtaining integrated characteristic data that integrates the plurality of characteristic data based on similarities between the plurality of characteristic data, and generating a predicted value of solar power generation based on the integrated characteristic data.

[0017] According to some embodiments of the present disclosure, accurate and stable solar power generation forecasts can be achieved by generating predicted values ​​for solar power generation based on multiple weather forecast data. Furthermore, accurate solar power generation forecasts enable stable grid operation and minimize energy waste by optimizing the scheduling of energy storage devices. Furthermore, solar energy supply can be adjusted, improving solar energy utilization. Furthermore, performance monitoring of solar power plants can be achieved by comparing predicted and actual power generation.

[0018] The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by a person having ordinary knowledge in the technical field to which the present disclosure belongs (referred to as “ordinary skilled person”) from the description of the claims.

[0019] Embodiments of the present disclosure will be described below with reference to the accompanying drawings, wherein like reference numerals represent similar elements, but are not limited thereto.

[0020] FIG. 1 is a diagram for explaining the configuration of an electronic device for predicting solar power generation according to one embodiment of the present disclosure.

[0021] FIG. 2 is a schematic diagram showing a configuration in which an information processing system is connected to enable communication with a plurality of user terminals in relation to data processing according to one embodiment of the present disclosure.

[0022] FIG. 3 is a block diagram showing the internal configuration of a user terminal and an information processing system according to one embodiment of the present disclosure.

[0023] FIG. 4 is a diagram for explaining a method for calculating a predicted value of solar power generation according to one embodiment of the present disclosure.

[0024] FIG. 5 is a diagram for explaining a method for calculating an attention value according to one embodiment of the present disclosure.

[0025] FIG. 6 is a diagram for explaining an artificial neural network model according to one embodiment of the present disclosure.

[0026] FIG. 7 is a diagram for explaining a method for predicting solar power generation according to one embodiment of the present disclosure.

[0027] Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, specific descriptions of widely known functions or configurations will be omitted if they may unnecessarily obscure the gist of the present disclosure.

[0028] In the attached drawings, identical or corresponding components are assigned the same reference numerals. Furthermore, in the description of the embodiments below, duplicate descriptions of identical or corresponding components may be omitted. However, even if a description of a component is omitted, it is not intended that such component is not included in any embodiment.

[0029] The advantages and features of the disclosed embodiments, and methods for achieving them, will become clearer with reference to the embodiments described below, along with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided solely to ensure the completeness of the disclosure and to fully inform those skilled in the art of the scope of the invention.

[0030] The terms used in this specification will be briefly explained, followed by a detailed description of the disclosed embodiments. The terms used in this specification have been selected from widely used, current terms, taking into account the functions of the present disclosure. However, these terms may vary depending on the intentions of engineers working in the relevant field, precedents, the emergence of new technologies, etc. Furthermore, in certain cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this disclosure should not be defined simply as names of terms, but rather based on their meanings and the overall content of the present disclosure.

[0031] In this specification, singular expressions include plural expressions unless the context clearly indicates otherwise. Furthermore, plural expressions include singular expressions unless the context clearly indicates otherwise. When a part of the specification is said to include a component, this does not exclude other components, but rather implies that other components may be included, unless otherwise specifically stated.

[0032] Also, the term 'module' or 'part' used in the specification means a software or hardware component, and the 'module' or 'part' performs certain roles. However, the 'module' or 'part' is not limited to software or hardware. The 'module' or 'part' may be configured to reside on an addressable storage medium and may be configured to execute one or more processors. Thus, as an example, the 'module' or 'part' may include at least one of components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, or variables. The functionality provided within the components and 'modules' or 'parts' may be combined into a smaller number of components and 'modules' or 'parts', or further separated into additional components and 'modules' or 'parts'.

[0033] According to one embodiment of the present disclosure, a 'module' or 'unit' may be implemented as a processor and a memory. 'Processor' should be broadly construed to include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, and the like. In some circumstances, a 'processor' may also refer to an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field-programmable gate array (FPGA), and the like. A 'processor' may also refer to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors in conjunction with a DSP core, or any other such combination of configurations. In addition, 'memory' should be broadly construed to include any electronic component capable of storing electronic information. 'Memory' may refer to various types of processor-readable media, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or marking data storage, registers, etc. Memory is said to be in electronic communication with the processor if the processor can read information from, and / or write information to, the memory. Memory integrated in a processor is in electronic communication with the processor.

[0034] In addition, terms such as first, second, A, B, (a), (b), etc. used in the following embodiments are only used to distinguish certain components from other components, and the nature, order, or sequence of the components are not limited by the terms.

[0035] Additionally, in the embodiments below, when it is described that a component is 'connected', 'coupled' or 'connected' to another component, it should be understood that the component may be directly connected or connected to the other component, but another component may also be 'connected', 'coupled' or 'connected' between each component.

[0036] Additionally, the terms 'comprises' and / or 'comprising' used in the following embodiments do not exclude the presence or addition of one or more other components, steps, operations and / or elements.

[0037] Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0038] FIG. 1 is a diagram illustrating the configuration of an electronic device (100) for predicting solar power generation according to one embodiment of the present disclosure. Referring to FIG. 1, the electronic device (100) for predicting solar power generation may include a memory (110) and a processor (130). However, the configuration of the electronic device (100) is not limited thereto. According to various embodiments, the electronic device (100) may further include at least one other component in addition to the components described above. According to one embodiment, the electronic device (100) may further include a communication circuit or a display. The communication circuit may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (100) and an external electronic device, and the performance of communication through the established communication channel. For example, the electronic device (100) may receive weather forecast data from an external electronic device connected via the communication circuit. The display may visually provide information to an external device (e.g., a user) of the electronic device (100). For example, the electronic device (100) can display a predicted value of solar power generation through a display. In another example, if the electronic device (100) is a server (e.g., the information processing system of FIGS. 2 and 3), the electronic device (100) can transmit a predicted value of solar power generation to a user terminal.

[0039] The memory (110) can store various data used by at least one component (e.g., the processor (130)) of the electronic device (100). The data can include, for example, input data or output data for software (or a program) and commands related thereto. The memory (110) can include volatile memory or non-volatile memory. According to one embodiment, the memory (110) can store weather forecast data.

[0040] The processor (130) may execute software (or a program) to control at least one other component (e.g., a hardware or software component) of the electronic device (100) connected to the processor (130) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (130) may load commands or data received from other components into volatile memory, process the commands or data stored in the volatile memory, and store the resulting data in non-volatile memory.

[0041] According to one embodiment, the processor (130) may acquire a plurality of characteristic data based on a plurality of weather forecast data. Thereafter, based on the similarity between the plurality of characteristic data, integrated characteristic data may be acquired by integrating the plurality of characteristic data, and a predicted value of solar power generation may be generated based on the integrated characteristic data. To this end, the processor (130) may include a characteristic data acquisition module (132), a characteristic data integration module (134), and a solar power generation prediction module (136). However, the types of modules included in the processor (130) are classified according to functions related to the prediction of solar power generation, and the types and numbers thereof are not limited thereto. In addition, at least one of the modules included in the processor (130) may be implemented in the form of instructions stored in the memory (110).

[0042] In the present disclosure, the number of weather forecast data used to predict solar power generation may be at least two (e.g., two, three, etc.), and the types thereof may also vary. For example, when the weather forecast data used have overlapping time ranges, the weather forecast data that may be used in the present disclosure may include at least two of ultra-short-term forecast data, short-term forecast data, medium-term forecast data, or long-term forecast data. However, in the embodiments described below, for the convenience of explanation, a method for calculating a predicted value of solar power generation using two weather forecast data will be described. For example, the plurality of weather forecast data may include first weather forecast data including a weather predicted value for a first time interval within a specified time range, and second weather forecast data including a weather predicted value for a second time interval longer than the first time interval within the specified time range.

[0043] In one embodiment, the first weather forecast data may include forecast data obtained through a Local Data Assimilation and Prediction System (LDAPS), and the second weather forecast data may include forecast data obtained through a Global Data Assimilation and Prediction System (GDAPS). In one embodiment, the first weather forecast data and the second weather forecast data may be forecast data with different time ranges, time intervals, and / or regional scopes.

[0044] The characteristic data acquisition module (132) can acquire a plurality of characteristic data based on a plurality of weather forecast data. For example, characteristic data can be acquired / generated for each weather forecast data. The process by which the characteristic data acquisition module (132) acquires a plurality of characteristic data can be referred to as a time series fusion process. The time series fusion process can include a process of aligning the time length of each weather forecast data. First, if the time ranges (e.g., forecast periods) of the plurality of weather forecast data are different, the characteristic data acquisition module (132) can extract data corresponding to the overlapping time ranges from each of the plurality of weather forecast data. In addition, if the time intervals (e.g., forecast intervals) of the plurality of weather forecast data are different, the characteristic data acquisition module (132) can perform a function of aligning the time length / intervals in order to integrate (or mix) and use the plurality of weather forecast data. To this end, the characteristic data acquisition module (132) can acquire characteristic data including weather forecast information while preserving the time series information from each of the plurality of weather forecast data.

[0045] According to one embodiment, the feature data acquisition module (132) may acquire an embedding vector corresponding to each of a plurality of weather forecast data as a plurality of feature data. Here, the embedding vector may be a method of expressing the data as a fixed-dimensional real-valued vector or a result value obtained through the method. When the semantic similarity between the data is high, the embedding vectors corresponding to the data may be located adjacent to each other in the expressed space. The embedding vector corresponding to each of the plurality of weather forecast data may include time-series information of the corresponding weather forecast data and have a specified length (e.g., dimension). For example, if multiple weather forecast data include first weather forecast data having first time series information and second weather forecast data having second time series information, a first embedding vector corresponding to the first weather forecast data and a second embedding vector corresponding to the second weather forecast data include the first time series information and the second time series information, respectively, but the length (e.g., dimension) of the first embedding vector and the length (e.g., dimension) of the second embedding vector may be the same.

[0046] According to one embodiment, the feature data acquisition module (132) can generate an embedding vector using an attention-based LSTM (Long Short-Term Memory). Here, the attention-based LSTM can be referred to as an Attentive LSTM. LSTM shows high accuracy in the field of time series prediction and can represent a model that serves as the basis for many time series prediction models. For example, feature vector x t When receiving input, LSTM combines the two state vectors h from the previous time t-1 and c t-1 Update and update the new state vector h t and c t , the hidden state vector h generated at the last point in time is output. Tcan be the final output value of LSTM. However, LSTM has a limitation that it cannot use values ​​before the hidden state vector of the final time point. Attentive LSTM can be used to compensate for this. Attentive LSTM can calculate the attention value by utilizing the hidden state vector of each time point instead of using the hidden state vector of the last time point as the output value. Then, the attentive LSTM can calculate the weighted sum of the hidden state vectors using each attention value as a weight and output a single context vector C. Through this process, weather forecast data of different lengths can be expressed as an embedding vector of the same length while preserving the time series information. Through the following mathematical equation 1, the attention value a of each time point of the attentive LSTM i And the context vector C can be produced.

[0047]

[0048]

[0049] Here, h i represents the hidden state vector at time i (final when i=T), and a i can represent the attention value at time i.

[0050] The feature data integration module (134) can acquire integrated feature data by integrating multiple feature data based on the similarity between the multiple feature data. The process by which the feature data integration module (134) acquires the integrated feature data may be referred to as a feature fusion process. The feature fusion process may include a process of integrating multiple feature data by identifying highly relevant core information (or important information) from the multiple feature data.

[0051] According to one embodiment, the feature data integration module (134) can obtain integrated feature data by integrating a plurality of feature data using a cross attention mechanism. First, the feature data integration module (134) can generate a query matrix, a key matrix, and a value matrix corresponding to each of the plurality of feature data, which are used in the attention model, based on an embedding vector corresponding to each of the plurality of weather forecast data. For example, the feature data integration module (134) can generate a query matrix, a key matrix, and a value matrix associated with first feature data (e.g., a first embedding vector) corresponding to first weather forecast data, and can generate a query matrix, a key matrix, and a value matrix associated with second feature data (e.g., a second embedding vector) corresponding to second weather forecast data. According to one embodiment, the feature data integration module (134) can adjust the dimensions of the query matrix, the key matrix, and the value matrix through a linear projection on the query matrix, the key matrix, and the value matrix.

[0052] Then, the feature data integration module (134) may calculate a first attention score indicating a similarity between the query and the key based on a key matrix associated with the first feature data and a query matrix associated with the second feature data, and may calculate a second attention score indicating a similarity between the query and the key based on a key matrix associated with the second feature data and a query matrix of the first feature data. According to one embodiment, the feature data integration module (134) may calculate an attention score (e.g., the first attention score or the second attention score) using a matrix multiplication operation (or a dot product between matrices).

[0053] Then, the feature data integration module (134) can calculate the first attention value based on the first attention score and the value matrix associated with the first feature data, and can calculate the second attention value based on the second attention score and the value matrix associated with the second feature data. In this process, the feature data integration module (134) can calculate the attention weight by passing the attention score through a softmax function. For example, the feature data integration module (134) can calculate the first attention weight by applying the softmax function to the first attention score, and can calculate the second attention weight by applying the softmax function to the second attention score. Here, applying the softmax function is to obtain a probability distribution in which the sum of all values ​​is 1, which is called an attention distribution, and each value obtained by applying the softmax function, i.e., the attention weight, can represent the importance of each key corresponding to the query. According to one embodiment, the feature data integration module (134) may scale the attention scores before applying the softmax function. Here, performing the scaling operation may be to increase numerical stability and computational efficiency. For example, due to the nature of calculating the inner product between matrices, the longer the input value, the larger the number may be. In addition, the softmax function is structured to obtain a probability distribution by applying an exponential function to each element of the input vector and normalizing the result. Since the exponential function is used, the result value increases rapidly as the input value increases, which may cause overflow. Since this phenomenon causes the gradient vanishing problem of the softmax function, the feature data integration module (134) may reduce the size of the attention scores through a scaling operation before applying the softmax function, thereby increasing numerical stability and also increasing computational efficiency.In addition, the feature data integration module (134) can obtain the attention value by calculating a weighted sum for the value matrix through a matrix multiplication operation (or inner product) of the attention weight and the value matrix. For example, the feature data integration module (134) can calculate the first attention value through the weighted sum of the first attention weight and the value matrix associated with the first feature data, and can calculate the second attention value through the weighted sum of the second attention weight and the value matrix associated with the second feature data. According to one embodiment, the feature data integration module (134) can adjust the dimension of the attention value through linear projection on the calculated attention value (e.g., the first attention value or the second attention value).

[0054] Then, the feature data integration module (134) can concatenate the generated attention values ​​to generate integrated feature data. For example, the feature data integration module (134) can concatenate the first attention value and the second attention value to generate integrated feature data.

[0055] According to one embodiment, the feature data integration module (134) can apply the integrated feature data to a transformer model. For example, the feature data integration module (134) can extract key information (or important information) from the integrated feature data, i.e., the connected attention values, once more through the operation of an additional transformer. Here, the transformer model has a structure of an encoder that compresses an input sequence into a single vector representation and a decoder that generates an output sequence through the vector representation, but may be a model implemented using an attention mechanism without using a recurrent neural network (RNN). Since the transformer does not sequentially receive input values ​​according to the position of the input sequence, it can perform positional encoding that adds the position information of the input values ​​to the feature data (e.g., an embedding vector). In addition, the attention mechanism used in the transformer may include a self-attention mechanism. The self-attention mechanism is similar in structure to the cross-attention mechanism described above, but may differ in that the input query matrix, key matrix, and value matrix come from the same source. For example, when the feature data integration module (134) applies the integrated feature data to the transformer model, the operation of the transformer may include a self-attention operation, and the query matrix, key matrix, and value matrix used in the self-attention operation may be generated from the integrated feature data.

[0056] The solar power generation prediction module (136) can generate a predicted value of solar power generation based on the integrated characteristic data. The process of the solar power generation prediction module (136) generating a predicted value of solar power generation may be referred to as a multi-layer perceptron process. The multi-layer perceptron process may include a process of generating at least one predicted value of solar power generation at a specified time interval (e.g., a prediction interval) in a specified time range (e.g., a prediction period or a prediction date) that is a prediction target through a multi-layer perceptron model using the integrated characteristic data. For example, the solar power generation prediction module (136) can generate predicted values ​​of solar power generation corresponding to 1-hour intervals for 24 hours on day t, which is a prediction target date (e.g., a total of 24 predicted values). Here, the multi-layer perceptron model is an artificial neural network model in which two or more hidden layers exist between the input layer and the output layer, and may be a model capable of identifying linear and nonlinear relationships in data. According to one embodiment, the solar power generation prediction module (136) may use a leaky ReLU (Rectified Linear Unit) function as an activation function of a multilayer perceptron.

[0057] As described above, the electronic device (100) for predicting solar power generation according to one embodiment of the present disclosure may acquire a plurality of characteristic data based on a plurality of weather forecast data, acquire integrated characteristic data by integrating the plurality of characteristic data based on the similarity between the plurality of characteristic data, and generate a predicted value of solar power generation based on the integrated characteristic data. For example, the electronic device (100) may use various information, i.e., multiple weather forecast data, as input values ​​to extract core information (or important information), identify their relationships, and integrate the weather forecast data into an embedding value (or embedding vector). In addition, the electronic device (100) may utilize the weather forecast data for both model training and testing in order to reduce dependence on the model forecast. For example, the electronic device (100) may train the model using the weather forecast data throughout the entire process in order to identify a pattern between the weather forecast data and the solar power generation.

[0058] In the present disclosure, weather forecast data, as source data (data used as input values ​​for a forecast model) used to predict solar power generation, may include various weather-related information / data. According to various embodiments, the weather forecast data may include not only forecast data obtained through a forecast model, but also various types of forecast / observation data or information related to weather. For example, the weather forecast data may include satellite imagery, SNS (Social Network Service) data generated by electronic devices located in areas adjacent to solar power plants, or web portal search data.

[0059] FIG. 2 is a schematic diagram illustrating a configuration in which an information processing system (230) is connected to a plurality of user terminals (210_1, 210_2, 210_3) so as to be able to communicate with each other, in relation to data processing according to one embodiment of the present disclosure. The information processing system (230) may include system(s) capable of providing a data processing service (e.g., a solar power generation prediction-based service). In one embodiment, the information processing system (230) may include one or more server devices and / or databases capable of storing, providing, and executing computer-executable programs (e.g., downloadable applications) and data related to the data processing service, or one or more distributed computing devices and / or distributed databases based on a cloud computing service. For example, the information processing system (230) may include separate systems (e.g., servers) for the data processing service.

[0060] Data processing services, etc. provided by the information processing system (230) can be provided to users through data processing applications, web browser applications, etc. installed on each of a plurality of user terminals (210_1, 210_2, 210_3).

[0061] A plurality of user terminals (210_1, 210_2, 210_3) can communicate with an information processing system (230) via a network (220). The network (220) can be configured to enable communication between the plurality of user terminals (210_1, 210_2, 210_3) and the information processing system (230). Depending on the installation environment, the network (220) can be configured as a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device, and RS-serial communication, a wireless network such as a mobile communication network, WLAN (Wireless LAN), Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and may include not only a communication method utilizing a communication network (e.g., a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) that the network (220) may include, but also short-range wireless communication between user terminals (210_1, 210_2, 210_3).

[0062] For example, multiple user terminals (210_1, 210_2, 210_3) can transmit data processing requests and commands related to user requests for data processing to an information processing system (230) via a network (220), and the information processing system (230) can receive them.

[0063] In FIG. 2, a mobile phone terminal (210_1), a tablet terminal (210_2), and a PC terminal (210_3) are illustrated as examples of user terminals, but are not limited thereto, and the user terminals (210_1, 210_2, 210_3) may be any computing device capable of wired and / or wireless communication and capable of installing and executing data processing applications, etc. For example, the user terminals may include smartphones, mobile phones, navigation devices, computers, laptops, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), tablet PCs, game consoles, wearable devices, IoT (Internet of Things) devices, VR (virtual reality) devices, AR (augmented reality) devices, etc. In addition, although FIG. 2 illustrates three user terminals (210_1, 210_2, 210_3) communicating with the information processing system (230) via the network (220), this is not limited thereto, and a different number of user terminals may be configured to communicate with the information processing system (230) via the network (220).

[0064] When the information processing system (230) provides a service based on solar power generation prediction, it can receive weather forecast data from user terminals (210_1, 210_2, 210_3). In this case, the information processing system (230) can generate a predicted value for solar power generation based on a plurality of weather forecast data. Thereafter, the information processing system (230) can transmit the generated predicted value for solar power generation to the user terminals (210_1, 210_2, 210_3).

[0065] FIG. 3 is a block diagram illustrating the internal configuration of a user terminal (210) and an information processing system (230) according to one embodiment of the present disclosure. The user terminal (210) may refer to any computing device capable of executing a data processing application and capable of wired / wireless communication, and may include, for example, a mobile phone terminal (210_1), a tablet terminal (210_2), a PC terminal (210_3) of FIG. 2 . As illustrated, the user terminal (210) may include a memory (312), a processor (314), a communication module (316), and an input / output interface (318). Similarly, the information processing system (230) may include a memory (332), a processor (334), a communication module (336), and an input / output interface (338). As illustrated in FIG. 3, the user terminal (210) and the information processing system (230) may be configured to communicate information and / or data via a network (220) using respective communication modules (316, 336). In addition, the input / output device (320) may be configured to input information and / or data to the user terminal (210) or output information and / or data generated from the user terminal (210) via the input / output interface (318).

[0066] The memory (312, 332) may include any non-transitory computer-readable recording medium. According to one embodiment, the memory (312, 332) may include a permanent mass storage device such as a read-only memory (ROM), a disk drive, a solid state drive (SSD), a flash memory, etc. As another example, a permanent mass storage device such as a ROM, an SSD, a flash memory, a disk drive, etc. may be included in the user terminal (210) or the information processing system (230) as a separate permanent storage device distinct from the memory. In addition, the memory (312, 332) may store an operating system and at least one program code (e.g., code for an application associated with a data processing service, etc.).

[0067] These software components may be loaded from a computer-readable recording medium separate from the memory (312, 332). This separate computer-readable recording medium may include a recording medium directly connectable to the user terminal (210) and the information processing system (230), and may include, for example, a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, etc. As another example, the software components may be loaded into the memory (312, 332) through a communication module (316, 336) other than a computer-readable recording medium. For example, at least one program may be loaded into the memory (312, 332) based on a computer program (e.g., an application associated with a data processing service, etc.) that is installed by files provided by developers or a file distribution system that distributes installation files of applications through a network (220).

[0068] The processor (314, 334) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (314, 334) by a memory (312, 332) or a communication module (316, 336). For example, the processor (314, 334) may be configured to execute instructions received according to program code stored in a storage device such as the memory (312, 332).

[0069] The communication module (316, 336) may provide a configuration or function for the user terminal (210) and the information processing system (230) to communicate with each other via the network (220), and may provide a configuration or function for the user terminal (210) and / or the information processing system (230) to communicate with another user terminal or another system (e.g., a separate cloud system, etc.). For example, a request or data (e.g., a data processing request or data, etc.) generated by the processor (314) of the user terminal (210) according to a program code stored in a recording device such as a memory (312) may be transmitted to the information processing system (230) via the network (220) under the control of the communication module (316). Conversely, a control signal or command provided under the control of the processor (334) of the information processing system (230) can be received by the user terminal (210) through the communication module (316) of the user terminal (210) via the communication module (336) and the network (220).

[0070] The input / output interface (318) may be a means for interfacing with an input / output device (320). As an example, the input device may include a device such as a camera, a keyboard, a microphone, a mouse, etc., including an audio sensor and / or an image sensor, and the output device may include a device such as a display, a speaker, a haptic feedback device, etc. As another example, the input / output interface (318) may be a means for interfacing with a device that has a configuration or function integrated into one for performing input and output, such as a touch screen. In FIG. 3, the input / output device (320) is illustrated as not being included in the user terminal (210), but is not limited thereto and may be configured as a single device with the user terminal (210). In addition, the input / output interface (338) of the information processing system (230) may be a means for interfacing with a device (not shown) for input or output that is connected to the information processing system (230) or that the information processing system (230) may include. In FIG. 3, the input / output interface (318, 338) is illustrated as an element configured separately from the processor (314, 334), but is not limited thereto, and the input / output interface (318, 338) may be configured to be included in the processor (314, 334).

[0071] The user terminal (210) and the information processing system (230) may include more components than those shown in FIG. 3. However, it is not necessary to explicitly illustrate most of the conventional technical components. In one embodiment, the user terminal (210) may be implemented to include at least some of the input / output devices (320) described above. In addition, the user terminal (210) may further include other components such as a transceiver, a Global Positioning System (GPS) module, a camera, various sensors, a database, etc. For example, if the user terminal (210) is a smartphone, it may include components that a smartphone generally includes, and for example, various components such as an acceleration sensor, a gyro sensor, a microphone module, a camera module, various physical buttons, buttons using a touch panel, input / output ports, and a vibrator for vibration may be implemented to be further included in the user terminal (210).

[0072] According to one embodiment, the processor (314) of the user terminal (210) may be configured to operate a data processing application or a web browser application that provides a data processing service. At this time, program code associated with the application may be loaded into the memory (312) of the user terminal (210). While the application is operating, the processor (314) of the user terminal (210) may receive information and / or data provided from the input / output device (320) through the input / output interface (318) or may receive information and / or data from the information processing system (230) through the communication module (316), and may process the received information and / or data and store it in the memory (312). In addition, such information and / or data may be provided to the information processing system (230) through the communication module (316).

[0073] While the data processing application is running, the processor (314) may receive voice data, text, images, videos, etc. input or selected through input devices such as a camera, microphone, including a touch screen, keyboard, audio sensor, and / or image sensor connected to the input / output interface (318), and may store the received voice data, text, images, and / or videos in the memory (312) or provide them to the information processing system (230) through the communication module (316) and the network (220). In one embodiment, the processor (314) may receive user input input through the input device, and provide data / requests corresponding to the received user input to the information processing system (230) through the network (220) and the communication module (316).

[0074] The processor (314) of the user terminal (210) can output information and / or data by transmitting the information and / or data to an input / output device (320) through an input / output interface (318). For example, the processor (314) of the user terminal (210) can output the processed information and / or data through an output device (320), such as a display output capable device (e.g., a touch screen, a display, etc.) or a voice output capable device (e.g., a speaker).

[0075] The processor (334) of the information processing system (230) may be configured to manage, process, and / or store information and / or data received from multiple user terminals (210) and / or multiple external systems. Information and / or data processed by the processor (334) may be provided to the user terminal (210) via a communication module (336) and a network (220).

[0076] FIG. 4 is a diagram illustrating a method for calculating a predicted value of solar power generation according to one embodiment of the present disclosure. Referring to FIG. 4, an electronic device for predicting solar power generation (e.g., electronic device (100) of FIG. 1) may generate a predicted value (490) of solar power generation using a plurality of weather forecast data (e.g., first weather forecast data (412) and second weather forecast data (414)). In the following description, for convenience of explanation, a case in which the plurality of weather forecast data are two weather forecast data is described, but the number of weather forecast data is not limited thereto. For example, three or more weather forecast data may be used.

[0077] Looking at the process of generating a predicted value (490) of solar power generation, first, the electronic device (or at least one processor of the electronic device) can obtain first characteristic data (432) and second characteristic data (434) based on first weather forecast data (412) and second weather forecast data (414), respectively. In this process, if the time ranges (e.g., forecast periods) of the first weather forecast data (412) and the second weather forecast data (414) are different, the electronic device can extract data corresponding to the overlapping time ranges of the first weather forecast data (412) and the second weather forecast data (414). Additionally, the electronic device may obtain first feature data (432) from the first weather forecast data (412) (or the extracted first weather forecast data) using an attention-based LSTM (422), and obtain second feature data (434) from the second weather forecast data (414) (or the extracted second weather forecast data) using an attention-based LSTM (424). According to one embodiment, the first feature data (432) may include a first embedding vector, and the second feature data (434) may include a second embedding vector. For example, the electronic device can use an attention-based LSTM (422, 424) to generate embedding vectors (e.g., a first embedding vector and a second embedding vector) having the same length (e.g., dimension) from each weather forecast data (412, 414) while preserving the time series information of each weather forecast data (412, 414).

[0078] Then, the electronic device can obtain integrated characteristic data (460) that integrates the first characteristic data (432) and the second characteristic data (434) based on the similarity between the first characteristic data (432) and the second characteristic data (434). In this process, the electronic device can generate a query matrix, a key matrix, and a value matrix corresponding to each of the first characteristic data (432) and the second characteristic data (434). For example, the electronic device can generate a query matrix, a key matrix, and a value matrix associated with the first characteristic data (432) (e.g., a first embedding vector), and generate a query matrix, a key matrix, and a value matrix associated with the second characteristic data (434) (e.g., a second embedding vector).

[0079] Thereafter, the electronic device can obtain the first attention value (452) and the second attention value (454) based on the first characteristic data (432) and the second characteristic data (434) through the cross attention (442, 444) mechanism. For example, the electronic device can use a query associated with the second characteristic data (434) for the cross attention (442) to calculate the first attention value (452), and can use a query associated with the first characteristic data (432) for the cross attention (444) to calculate the second attention value (454). In addition, the electronic device can concatenate the first attention value (452) and the second attention value (454) to generate integrated characteristic data (460). The cross attention (442, 444) mechanism will be described in detail with reference to FIG. 5.

[0080] Then, the electronic device can apply the integrated feature data (460) to the transformer (470) model. For example, the electronic device can extract key information (or important information) from the integrated feature data (460) once more through the operation of an additional transformer (470). Here, the attention mechanism used in the transformer (470) may include a self-attention mechanism. The self-attention mechanism may have a similar structure to the cross-attention (442, 444) mechanism, but may differ in that the input query matrix, key matrix, and value matrix come from the same source (e.g., the integrated feature data (460)).

[0081] Then, the electronic device can generate a predicted value (490) of the solar power generation amount based on the integrated characteristic data (460). For example, the electronic device can generate a predicted value (490) of the solar power generation amount based on the integrated characteristic data (460) additionally applied to the transformer (470) model. According to one embodiment, the electronic device can input the integrated characteristic data (460) as an input value to a multilayer perceptron (480) model and obtain a predicted value (490) of the solar power generation amount as an output value. Here, the electronic device can use a leaky ReLU function as an activation function of the multilayer perceptron. The multilayer perceptron (480) model will be described in detail with reference to FIG. 6.

[0082] FIG. 5 is a diagram illustrating a method for calculating an attention value according to one embodiment of the present disclosure. Referring to FIG. 5, an electronic device for predicting solar power generation (e.g., the electronic device (100) of FIG. 1 ) can calculate an attention value (580) through an attention mechanism. Here, the attention mechanism can dynamically adjust the weights for the outputs of each input element to help the model focus on important information. The attention mechanism may include a self-attention mechanism, a cross-attention mechanism, and the like.

[0083] Looking at the process of calculating the attention value (580) through the attention mechanism, first, the electronic device can obtain a query matrix (512, a key matrix (514), and a value matrix (516) based on characteristic data (e.g., an embedding vector). For example, the electronic device can generate a query matrix, a key matrix, and a value matrix associated with first characteristic data (e.g., a first embedding vector) corresponding to first weather forecast data, and generate a query matrix, a key matrix, and a value matrix associated with second characteristic data (e.g., a second embedding vector) corresponding to second weather forecast data.

[0084] Then, the electronic device can perform linear projection (522, 524, 526) operations on the query matrix (512), the key matrix (514), and the value matrix (516). For example, the electronic device can adjust the dimensions of the query matrix (512), the key matrix (514), and the value matrix (516) through linear projection (522, 524, 526).

[0085] Then, the electronic device can calculate an attention score indicating the similarity between the query matrix (512) and the key matrix (514) based on the query matrix (512) and the key matrix (514). For example, the electronic device can calculate the attention score through a matrix multiplication operation (530) (or inner product) on the query matrix (512) and the key matrix (514). The query matrix (512) used in this process may be different in the self-attention mechanism and the cross-attention mechanism. For example, in the self-attention mechanism, the query matrix (512) and the key matrix (514) are obtained from the same source (e.g., the same embedding vector), but in the cross-attention mechanism, the query matrix (512) and the key matrix (514) may be obtained from different sources (e.g., different embedding vectors).

[0086] Then, the electronic device can scale (540) the attention score. For example, the electronic device can reduce the size of the attention score through a scaling operation (540) to increase numerical stability and computational efficiency.

[0087] The electronic device can then apply a softmax function to the scaled attention scores (550). For example, the electronic device can input the attention scores into the softmax function to produce attention weights. Here, the attention weights can represent the importance of each key matrix (514) corresponding to the query matrix (512).

[0088] Then, the electronic device can calculate the attention value (580) through a matrix multiplication operation (560) (or inner product) of the attention weight and the value matrix (516). For example, the electronic device can calculate the attention value (580) through a weighted sum of the attention weight and the value matrix (516). Thereafter, the electronic device can adjust the dimension of the attention value (580) through linear projection on the calculated attention value (580).

[0089] FIG. 6 is a diagram illustrating an artificial neural network model (600) according to one embodiment of the present disclosure. Referring to FIG. 6, the artificial neural network model (600) is an example of a machine learning model, and in machine learning technology and cognitive science, may represent a statistical learning algorithm implemented based on a biological neural network structure or a structure that executes the algorithm.

[0090] According to one embodiment, the artificial neural network model (600) can represent a machine learning model having problem-solving capabilities by learning that nodes, which are artificial neurons that form a network by combining synapses like a biological neural network, repeatedly adjust the weights of synapses so that the error between the correct output corresponding to a specific input and the inferred output is reduced.

[0091] In one embodiment, the above-described solar power generation prediction model may be generated in the form of an artificial neural network model (600). For example, the artificial neural network model (600) may receive weather forecast data for an area associated with a solar power plant and estimate the expected power generation based thereon.

[0092] The artificial neural network model (600) can be implemented as a multilayer perceptron composed of nodes in multiple layers and connections therebetween. The artificial neural network model (600) according to the present embodiment can be implemented using one of various artificial neural network model structures including a multilayer perceptron. The artificial neural network model (600) can be configured with an input layer (620) that receives input data (610) (or input signal) from the outside, an output layer (640) that outputs output data (650) (or output signal) corresponding to the input data (610), and n hidden layers (630_1 to 630_n) located between the input layer (620) and the output layer (640) that receive signals from the input layer (620), extract features, and transmit them to the output layer (640) (where n is a positive integer). Here, the output layer (640) can receive signals from the hidden layers (630_1 to 630_n) and output them to the outside.

[0093] The learning method of the artificial neural network model (600) may include a supervised learning method that learns to optimize problem solving by inputting a correct teacher signal (or label), and an unsupervised learning method that does not require a teacher signal. According to one embodiment, an electronic device (e.g., the electronic device (100) of FIG. 1) according to one embodiment of the present disclosure may train the artificial neural network model (600) using a plurality of weather forecast data.

[0094] In one embodiment, the electronic device can generate training data for training an artificial neural network model (600). For example, the electronic device can generate a training data set containing multiple weather forecast data. Then, the electronic device can train an artificial neural network model (600) to produce a predicted value for solar power generation based on the generated training data set.

[0095] According to one embodiment, the input variables of the artificial neural network model (600) may include a plurality of weather forecast data. When the input variables described above are input through the input layer (620), the output variables output from the output layer (640) of the artificial neural network model (600) may be predicted values ​​of solar power generation.

[0096] In this way, a plurality of input variables and a plurality of corresponding output variables are respectively matched to the input layer (620) and the output layer (640) of the artificial neural network model (600), and the synapse values ​​between the nodes included in the input layer (620), the hidden layers (630_1 to 630_n), and the output layer (640) are adjusted, so that learning can be performed so that the correct output corresponding to a specific input can be extracted. Through this learning process, the characteristics hidden in the input variables of the artificial neural network model (600) can be identified, and the synapse values ​​(or weights) between the nodes of the artificial neural network model (600) can be adjusted so that the error between the output variables calculated based on the input variables and the target output is reduced. In addition, the electronic device can learn an algorithm that receives a plurality of weather forecast data as input, and learn in a manner that minimizes the loss with respect to the predicted value of the solar power generation amount (i.e., annotation information).

[0097] Using the artificial neural network model (600) learned in this way, the predicted value of solar power generation can be estimated.

[0098] FIG. 7 is a diagram for explaining a method for predicting solar power generation according to one embodiment of the present disclosure. Referring to FIG. 7, a processor (e.g., processor (130) of FIG. 1) of an electronic device (e.g., electronic device (100) of FIG. 1) for predicting solar power generation may receive a plurality of weather forecast data in step S710. For example, the processor may receive a plurality of weather forecast data from at least one external electronic device connected via a communication circuit. For another example, the processor may receive a plurality of weather forecast data from a memory (e.g., memory (110) of FIG. 1). In the present disclosure, the number of weather forecast data used for predicting solar power generation may be at least two (e.g., two, three, etc.), and the types thereof may also vary. For example, the plurality of weather forecast data may include first weather forecast data including weather prediction values ​​for a first time interval within a specified time range and second weather forecast data including weather prediction values ​​for a second time interval longer than the first time interval within the specified time range. According to one embodiment, the first weather forecast data may include forecast data obtained through a local weather forecast model (LDAPS), and the second weather forecast data may include forecast data obtained through a global weather forecast model (GDAPS).

[0099] In step S720, the processor may obtain multiple characteristic data. For example, the processor may obtain multiple characteristic data based on multiple weather forecast data. According to one embodiment, the processor may obtain an embedding vector corresponding to each of the multiple weather forecast data as the multiple characteristic data. The embedding vector corresponding to each of the multiple weather forecast data may include time series information of the corresponding weather forecast data and have a specified length (e.g., dimension). According to one embodiment, the processor may generate the embedding vector using an attention-based LSTM.

[0100] In step S730, the processor may obtain integrated characteristic data that integrates a plurality of characteristic data. For example, the processor may obtain integrated characteristic data that integrates a plurality of characteristic data based on a similarity between the plurality of characteristic data. According to one embodiment, the processor may obtain integrated characteristic data that integrates a plurality of characteristic data using a cross-attention mechanism. For example, the processor may first generate a query matrix, a key matrix, and a value matrix associated with the first characteristic data based on first characteristic data (e.g., a first embedding vector) corresponding to first weather forecast data among the plurality of weather forecast data, and may generate a query matrix, a key matrix, and a value matrix associated with the second characteristic data based on second characteristic data (e.g., a second embedding vector) corresponding to second weather forecast data among the plurality of weather forecast data. Then, the processor can calculate a first attention score based on a key matrix associated with the first feature data and a query matrix associated with the second feature data, and can calculate a second attention score based on the key matrix associated with the second feature data and the query matrix of the first feature data. Then, the processor can calculate a first attention value based on the first attention score and the value matrix associated with the first feature data, and can calculate a second attention value based on the second attention score and the value matrix associated with the second feature data. Then, the processor can concatenate the first attention value and the second attention value to generate integrated feature data.

[0101] In one embodiment, the processor can apply integrated feature data to a transformer model. For example, the processor can extract additional key information (or important information) from the integrated feature data, i.e., the associated attention values, through the operation of an additional transformer.

[0102] At step S740, the processor may generate a predicted value for solar power generation. For example, the processor may generate a predicted value for solar power generation based on integrated characteristic data. In one embodiment, the processor may generate a predicted value for solar power generation using a multilayer perceptron model using the integrated characteristic data. In one embodiment, the processor may use a leaky ReLU function as the activation function of the multilayer perceptron.

[0103] The above flowchart and description are merely examples, and some embodiments may implement the system differently. For example, in some embodiments, the order of each step may be changed, some steps may be repeated, some steps may be omitted, or some steps may be added.

[0104] The above-described method may be provided as a computer program stored on a computer-readable recording medium for execution on a computer. The medium may be one that continuously stores a computer-executable program or one that temporarily stores it for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or multiple hardware combinations, and is not limited to a medium directly connected to a computer system, but may also be distributed over a network. Examples of the medium may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and those configured to store program instructions, including ROM, RAM, and flash memory. In addition, examples of other media may include recording or storage media managed by app stores that distribute applications, sites that supply or distribute various software, servers, etc.

[0105] The methods, operations, or techniques of the present disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will appreciate that the various exemplary logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various exemplary components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software will depend on the particular application and the design requirements imposed on the overall system. Those skilled in the art may implement the described functionality in various ways for each particular application, but such implementations should not be construed as departing from the scope of the present disclosure.

[0106] In a hardware implementation, the processing units used to perform the techniques may be implemented within one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, a computer, or a combination thereof.

[0107] Accordingly, the various exemplary logical blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed by any combination of a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or those designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0108] In a firmware and / or software implementation, the techniques may be implemented as instructions stored on a computer-readable medium, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, a compact disc (CD), a magnetic or marking data storage device, etc. The instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described herein.

[0109] When implemented in software, the techniques described above may be stored on or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium.

[0110] For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of media. Disk and disc, as used herein, includes compact discs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically, whereas discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0111] A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. Alternatively, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Alternatively, the processor and the storage medium may reside as discrete components in the user terminal.

[0112] While the embodiments described above have been described as utilizing aspects of the presently disclosed subject matter in one or more standalone computer systems, the present disclosure is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or distributed computing environment. Furthermore, aspects of the present disclosure may be implemented in multiple processing chips or devices, and storage may be similarly affected across multiple devices. Such devices may include personal computers, network servers, and portable devices.

[0113] While the present disclosure has been described in connection with certain embodiments herein, various modifications and variations may be made without departing from the scope of the present disclosure, which would be apparent to those skilled in the art. Furthermore, such modifications and variations are intended to fall within the scope of the claims appended to this specification.

Claims

1. In electronic devices, memory; and a processor operatively connected to said memory; Including, The above processor, Receive multiple weather forecast data, Based on the above multiple weather forecast data, multiple characteristic data are acquired, Based on the similarity between the plurality of characteristic data, integrated characteristic data is obtained by integrating the plurality of characteristic data, An electronic device configured to generate a predicted value of solar power generation based on the above integrated characteristic data.

2. In claim 1, An electronic device wherein the plurality of weather forecast data includes first weather forecast data including weather forecast values ​​for a first time interval within a specified time range and second weather forecast data including weather forecast values ​​for a second time interval longer than the first time interval within the specified time range.

3. In claim 1, The above processor, It is configured to obtain an embedding vector corresponding to each of the plurality of weather forecast data as the plurality of characteristic data, An electronic device wherein the above embedding vector includes time series information of weather forecast data corresponding to the embedding vector among the plurality of weather forecast data and has a specified length.

4. In claim 3, The above processor, An electronic device configured to generate the embedding vector using an attention-based LSTM (Long Short-Term Memory).

5. In claim 3, The above processor, An electronic device configured to generate a query matrix, a key matrix, and a value matrix corresponding to each of the plurality of characteristic data, which are used in an attention model, based on an embedding vector corresponding to each of the plurality of weather forecast data.

6. In claim 5, The above processor, A first attention score indicating the similarity between a query and a key is calculated based on a key matrix associated with a first characteristic data among the plurality of characteristic data and a query matrix associated with a second characteristic data among the plurality of characteristic data, Calculate a first attention value based on the first attention score and the value matrix associated with the first characteristic data, A second attention score indicating the similarity between a query and a key is calculated based on a key matrix associated with the second characteristic data and a query matrix of the first characteristic data, A second attention value is calculated based on the second attention score and the value matrix associated with the second characteristic data, An electronic device configured to generate the integrated characteristic data by concatenating the first attention value and the second attention value.

7. In claim 6, The above processor, An electronic device configured to apply the above integrated characteristic data to a transformer model.

8. In claim 1, The above processor, An electronic device configured to generate a predicted value of the solar power generation amount through a multi-layer perceptron model using the above integrated characteristic data.

9. In claim 8, An electronic device in which the activation function of the above multilayer perceptron includes a leaky ReLU (Rectified Linear Unit) function.

10. A method for predicting solar power generation, performed by at least one processor, A step of receiving multiple weather forecast data; A step of obtaining a plurality of characteristic data based on the plurality of weather forecast data; A step of obtaining integrated characteristic data by integrating the plurality of characteristic data based on the similarity between the plurality of characteristic data; and A step of generating a predicted value of solar power generation based on the above integrated characteristic data. How to include.

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