Photovoltaic power generation power prediction method, device, equipment, medium and product

Abstract

The invention relates to the technical field of power grids, and discloses a power prediction method and device for photovoltaic power generation, equipment, a medium and a product. The method comprises the steps of obtaining a multi-modal historical data set corresponding to a photovoltaic station, and performing modal decomposition on the multi-modal historical data set by adopting a multivariate variational modal decomposition method to obtain a plurality of first modal components; obtaining a modal prediction result corresponding to each first modal component according to the plurality of first modal components through a kernel extreme learning machine prediction model optimized by a multi-target white shark optimization algorithm; and searching a prediction weight value corresponding to each first modal component, and obtaining a generation power prediction result corresponding to the photovoltaic station according to the modal prediction result and the prediction weight value corresponding to each first modal component. According to the scheme of the embodiment, multi-modal data feature mining and efficient prediction of the photovoltaic station can be realized, and the accuracy of power prediction of photovoltaic power generation can be improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a power prediction method, device, equipment, medium and product for photovoltaic power generation. Background Art

[0002] In recent years, the photovoltaic industry has maintained a high-speed development trend, and the large-scale and high-proportion access of new energy to the power grid has greatly increased the operation and absorption pressure of the system. Compared with traditional energy, photovoltaic power generation is greatly affected by geographical location, environmental climate and hardware configuration, and has the characteristics of volatility and intermittence, which brings instability problems to the large-scale access of photovoltaic power generation to the power grid.

[0003] Accurate photovoltaic power prediction is the key to solving the above problems. At present, the existing photovoltaic power prediction methods are usually based on meteorological data given by simple weather forecasts for model training and prediction, and fail to consider the influence of different modal components in the prediction process. This causes the prediction results to be unable to accurately reflect the volatility of photovoltaic power, resulting in poor accuracy of photovoltaic power prediction. Summary of the invention

[0004] The present invention provides a photovoltaic power generation power prediction method, device, equipment, medium and product, which can improve the accuracy of photovoltaic power generation power prediction.

[0005] According to one aspect of the present invention, a method for predicting power of photovoltaic power generation is provided, comprising:

[0006] Acquire a multimodal historical data set corresponding to the photovoltaic station, and perform modal decomposition on the multimodal historical data set using a multivariate variational modal decomposition method to obtain a plurality of first modal components;

[0007] Obtaining a modal prediction result corresponding to each of the first modal components according to the multiple first modal components using a kernel extreme learning machine prediction model optimized by a multi-objective white shark optimization algorithm;

[0008] The prediction weight value corresponding to each of the first modal components is searched, and the power generation prediction result corresponding to the photovoltaic station is obtained according to the modal prediction result and the prediction weight value corresponding to each of the first modal components.

[0009] According to another aspect of the present invention, there is provided a photovoltaic power generation power prediction device, comprising:

[0010] A modal decomposition module is used to obtain a multimodal historical data set corresponding to a photovoltaic station, and perform modal decomposition on the multimodal historical data set using a multivariate variational modal decomposition method to obtain a plurality of first modal components;

[0011] A modal prediction result acquisition module, configured to acquire a modal prediction result corresponding to each of the first modal components according to the plurality of first modal components using a kernel extreme learning machine prediction model optimized by a multi-objective white shark optimization algorithm;

[0012] The power generation prediction module is used to find the prediction weight value corresponding to each of the first modal components, and obtain the power generation prediction result corresponding to the photovoltaic station according to the modal prediction result and the prediction weight value corresponding to each of the first modal components.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the photovoltaic power generation power prediction method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program is used to enable a processor to implement the photovoltaic power generation power prediction method described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method for predicting photovoltaic power generation according to any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention obtains a multimodal historical data set corresponding to a photovoltaic station, and uses a multivariate variational modal decomposition method to perform modal decomposition on the multimodal historical data set to obtain multiple first modal components; obtains modal prediction results corresponding to each first modal component according to multiple first modal components through a kernel extreme learning machine prediction model optimized by a multi-objective white shark optimization algorithm; searches for prediction weight values ​​corresponding to each first modal component, and obtains a power generation prediction result corresponding to the photovoltaic station according to the modal prediction results and prediction weight values ​​corresponding to each first modal component; optimizes the kernel extreme learning machine prediction model by using a multi-objective white shark optimization algorithm to predict different modal components, and obtains a final power generation prediction result according to the modal prediction results and prediction weight values ​​corresponding to different modal components, thereby realizing multimodal data feature mining and efficient prediction of photovoltaic stations, and improving the accuracy of power prediction of photovoltaic power generation.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a photovoltaic power generation power prediction method provided according to the first embodiment of the present invention;

[0023] Figure 2 is a schematic diagram of a model optimization process provided according to Embodiment 1 of the present invention;

[0024] Figure 3 is a schematic structural diagram of a photovoltaic power generation power prediction device provided according to Embodiment 2 of the present invention;

[0025] Figure 4 It is a schematic diagram of the structure of an electronic device for implementing the photovoltaic power generation power prediction method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 A flowchart of a photovoltaic power generation power prediction method is provided for the first embodiment of the present invention. This embodiment is applicable to the case of predicting the power generation of a photovoltaic station. The method can be executed by a photovoltaic power generation power prediction device. The photovoltaic power generation power prediction device can be implemented in the form of hardware and / or software. Typically, the photovoltaic power generation power prediction device can be configured in an electronic device, such as a computer device or a server. Figure 1 As shown, the method includes:

[0030] S110, obtaining a multimodal historical data set corresponding to a photovoltaic station, and performing modal decomposition on the multimodal historical data set using a multivariate variational modal decomposition method to obtain a plurality of first modal components.

[0031] The multimodal historical data set may include historical power generation data, historical ledger data, and historical weather data. The historical weather data may be meteorological data given by the weather forecast at a historical moment, for example, it may include temperature, wind speed, humidity, and cloud cover. In this embodiment, various historical data of distributed photovoltaic stations may be collected, and different photovoltaic stations may be used as labels to construct a multimodal historical data set.

[0032] Optionally, after collecting various historical data of the photovoltaic station, the collected data can be preprocessed. Typically, the preprocessing may include eliminating invalid and abnormal data, supplementing missing data, and standardizing the data. Then, a multimodal historical data set can be generated based on the preprocessed collected data.

[0033] In this embodiment, after obtaining the multimodal historical data set, the multivariate variational mode decomposition (MVMD) method can be used to perform modal decomposition on the multidimensional multimodal historical data set to obtain k modal components IMF1, IMF2, ..., IMF k Among them, MVMD is a signal decomposition method used to extract modal components from multivariate time series data.

[0034] S120. Obtaining a modal prediction result corresponding to each of the first modal components according to the multiple first modal components using a kernel extreme learning machine prediction model optimized by a multi-objective white shark optimization algorithm.

[0035] In this embodiment, a kernelized extreme learning machine (KELM) prediction model can be pre-established, and a multi-objective white shark optimization (MOWSO) algorithm is used to take the model parameters of the prediction model as optimization targets to obtain the optimal model parameters, thereby obtaining a MOWSO-optimized KELM prediction model. For example, the model parameters may include a regularization coefficient, a kernel function parameter, etc., and each model parameter may be used as an optimization target.

[0036] Among them, the MOWSO algorithm combines the multi-objective optimization algorithm and the great white shark optimization algorithm. It can set multiple optimization goals. By simulating the predation behavior of the great white shark, it continuously updates the position of the great white shark to track the position of the prey, thereby achieving target optimization.

[0037] In this embodiment, the input of the KELM prediction model optimized for MOWSO can be the modal components obtained by decomposing the multimodal historical data using the MVMD method, and the output can be the prediction result corresponding to each modal component. When the MOWSO algorithm is used to optimize the KELM prediction model, each shark individual can be used as a set of model parameters, and then the optimal model parameters can be obtained by finding the optimal individual.

[0038] Specifically, all first modal components may be input into the KELM prediction model optimized by MOWSO, and the modal prediction results corresponding to each first modal component output by the prediction model may be obtained. The modal prediction results may be the power generation prediction results of different modal components.

[0039] S130, searching for prediction weight values ​​corresponding to each of the first modal components, and obtaining a power generation prediction result corresponding to the photovoltaic station according to the modal prediction results and prediction weight values ​​corresponding to each of the first modal components.

[0040] In this embodiment, the prediction weight values ​​corresponding to different modal components can be pre-set and stored in a local database. When needed, the prediction weight value corresponding to each first modal component to be used can be found from the local database. Then, the prediction weight value corresponding to each first modal component can be used to perform weighted summation on the corresponding modal prediction results, and the sum can be used as the final power generation prediction result. Alternatively, the prediction weight value corresponding to each first modal component can be used to perform weighted averaging on the corresponding modal prediction results, and the obtained average value can be used as the final power generation prediction result.

[0041] Optionally, obtaining the power generation prediction result corresponding to the photovoltaic station according to the modal prediction results and prediction weight values ​​corresponding to each of the first modal components may include:

[0042] Calculating the product of each of the modal prediction results multiplied by the corresponding prediction weight value, and calculating the sum of each product;

[0043] The quotient of the sum of the products divided by the sum of the prediction weight values ​​is calculated as the prediction result of the generated power corresponding to the photovoltaic station.

[0044] In this embodiment, the corresponding modal prediction results can be weighted and summed according to the prediction weight values, and the sum of the weighted sums is divided by the sum of all prediction weight values ​​to calculate a quotient value, and the quotient value is used as the power generation prediction result corresponding to the photovoltaic station.

[0045] The technical solution of the embodiment of the present invention obtains a multimodal historical data set corresponding to a photovoltaic station, and uses a multivariate variational modal decomposition method to perform modal decomposition on the multimodal historical data set to obtain multiple first modal components; obtains modal prediction results corresponding to each first modal component according to multiple first modal components through a kernel extreme learning machine prediction model optimized by a multi-objective white shark optimization algorithm; searches for prediction weight values ​​corresponding to each first modal component, and obtains a power generation prediction result corresponding to the photovoltaic station according to the modal prediction results and prediction weight values ​​corresponding to each first modal component; optimizes the kernel extreme learning machine prediction model by using a multi-objective white shark optimization algorithm to predict different modal components, and obtains a final power generation prediction result according to the modal prediction results and prediction weight values ​​corresponding to different modal components, thereby realizing multimodal data feature mining and efficient prediction of photovoltaic stations, and improving the accuracy of power prediction of photovoltaic power generation.

[0046] In an optional implementation of this embodiment, before the kernel extreme learning machine prediction model optimized by the multi-objective white shark optimization algorithm obtains the modal prediction results corresponding to each of the first modal components according to the multiple first modal components, the following may also be included:

[0047] Generate a photovoltaic station sample data set according to the multimodal historical data sets corresponding to different photovoltaic stations, and divide the photovoltaic station sample data set according to a preset ratio to obtain a training sample set and a test sample set;

[0048] An initial nuclear extreme learning machine prediction model is established, and a multi-objective white shark optimization algorithm is used to optimize the initial nuclear extreme learning machine prediction model according to the training sample set and the test sample set to obtain a nuclear extreme learning machine prediction model optimized by the multi-objective white shark optimization algorithm.

[0049] In this embodiment, the historical power generation data, historical ledger data and historical weather data of the distributed photovoltaic station can be collected, and the collected data can be preprocessed, and then the photovoltaic station sample data set can be generated according to the preprocessed collected data. Then, the photovoltaic station sample data set can be divided into a training sample set and a test sample set based on a preset ratio, so as to be used for model training and model testing, respectively. For example, the training sample set and the test sample set can be divided according to a ratio of 7:3.

[0050] Specifically, an initial KELM prediction model can be established based on the initial model parameters, and the parameters of the MOWSO algorithm can be initialized; then, the initialized MOWSO algorithm can be used to optimize the model parameters of the initial KELM prediction model based on the divided training sample set, and after the optimization is completed, the MOWSO-optimized KELM prediction model can be obtained. Secondly, for the MOWSO-optimized KELM prediction model, a test sample set can also be used to evaluate its prediction effect. Typically, the evaluation indicators can include mean square error, root mean square error, etc.

[0051] Optionally, using a multi-objective White Shark optimization algorithm to optimize the initial kernel extreme learning machine prediction model according to the training sample set and the test sample set to obtain a kernel extreme learning machine prediction model optimized by the multi-objective White Shark optimization algorithm may include:

[0052] Generate an initial population according to the initial kernel extreme learning machine prediction model, wherein the initial population includes a plurality of individuals, each of which corresponds to a set of model parameters;

[0053] A multi-objective white shark optimization algorithm is used to update the initial population according to the training sample set and the test sample set until it is detected that the preset optimization conditions are met, and a kernel extreme learning machine prediction model optimized by the multi-objective white shark optimization algorithm is obtained according to the current population.

[0054] In this embodiment, when performing model optimization, first, an initial population is generated according to a preset number of individuals, individual positions, and training samples. Then, the fitness of the population is evaluated, and the non-dominated sorting and crowding distance of the population are calculated. The non-dominated sorting is used to classify the population, and the crowding distance is used to sort individuals of the same classification. The larger the crowding distance, the better the individual is and the easier it is to be retained. Further, a random search is performed on the individuals in the population so that all individuals track the optimal solution, and new individuals are generated according to the optimal solution, and then the new individuals are merged into the initial population to update the initial population. Finally, it can be determined whether the current population meets the preset optimization conditions, for example, reaching the set maximum number of iterations, meeting the set accuracy requirements, etc. If it is determined that it is not satisfied, the model optimization continues, and if it is determined that it is satisfied, a non-dominated set is generated according to the current population, and a KELM prediction model optimized by MOWSO is established according to the non-dominated set.

[0055] Optionally, after dividing the photovoltaic station sample data set according to a preset ratio to obtain a training sample set and a test sample set, the method may further include:

[0056] Performing modal decomposition on the training sample set using a multivariate variational modal decomposition method to obtain a plurality of second modal components;

[0057] The permutation entropy corresponding to each of the second modal components is calculated, and the prediction weight value corresponding to each of the second modal components is obtained according to the permutation entropy.

[0058] In this embodiment, after the training sample set is obtained, the MVMD method can be used to perform modal decomposition on the training sample set in advance to obtain multiple modal components IMF1, IMF2, ..., IMF k Then, the permutation entropy of different modal components can be calculated. Permutation entropy is an indicator to measure the complexity of time series. The more regular the time series, the smaller the corresponding permutation entropy, and the more complex the time series, the larger the corresponding permutation entropy. Furthermore, the modal components can be sorted in descending order of corresponding permutation entropy, and the prediction weight values ​​corresponding to different modal components can be obtained according to the sorting results and the preset relationship between the preset sorting and weight value.

[0059] In a specific implementation of this embodiment, the process of model optimization can be as follows: Figure 2 First, the multi-dimensional historical data of the photovoltaic station is collected, including historical power generation data, historical ledger data and historical weather data, and an initial multimodal data set is generated based on the collected multi-dimensional historical data, and then the initial multimodal data set is screened and standardized to obtain a valid sample set; further, the valid sample set is split into a training sample set and a test sample set according to a set ratio.

[0060] Then, an initial KELM prediction model is established according to the initial model parameters, and the training sample set is input into the initial KELM prediction model to obtain the initial population; secondly, the fitness of the population is evaluated, and the non-dominated sorting and crowding distance are determined, and then a random search is performed so that the individuals in the population track the optimal solution and generate new individuals based on the optimal solution; further, the new individuals are merged to update the population, and it is determined whether the current population meets the optimization conditions. If not, the model optimization is continued. If so, the MOWSO optimized KELM prediction model is obtained.

[0061] While optimizing the model, the MVMD method can be used to perform modal decomposition on the training sample set to obtain multiple modal components IMF1, IMF2, ..., IMF k , and the permutation entropy of different modal components can be calculated, and then the weights of different modal components can be allocated according to the permutation entropy to obtain the prediction weight values ​​W1, W2, ..., W corresponding to each modal component. k , and the prediction weight matrix can be composed of each prediction weight value.

[0062] Finally, the optimization model test is performed based on the test sample set. Specifically, the MVMD method is used to decompose the test sample set to obtain multiple modal components, and each modal component is input into the KELM prediction model optimized by MOWSO to obtain the modal prediction results P1, P2, ..., P corresponding to each modal component. k ; Then, the prediction results of each mode can be weighted averaged according to the prediction weight matrix to obtain the power generation prediction result corresponding to the test sample. By comparing the power generation prediction result with the historical power generation data, the power generation prediction effect of the KELM prediction model optimized by MOWSO can be verified.

[0063] Embodiment 2

[0064] Figure 3 This is a schematic diagram of the structure of a photovoltaic power generation power prediction device provided in the second embodiment of the present invention. Figure 3 As shown, the device includes: a modal decomposition module 210, a modal prediction result acquisition module 220 and a power generation prediction module 230; wherein,

[0065] A modal decomposition module 210 is used to obtain a multimodal historical data set corresponding to the photovoltaic station, and perform modal decomposition on the multimodal historical data set using a multivariate variational modal decomposition method to obtain a plurality of first modal components;

[0066] A modal prediction result acquisition module 220, configured to acquire a modal prediction result corresponding to each of the first modal components according to the plurality of first modal components using a kernel extreme learning machine prediction model optimized by a multi-objective white shark optimization algorithm;

[0067] The power generation prediction module 230 is used to find the prediction weight value corresponding to each of the first modal components, and obtain the power generation prediction result corresponding to the photovoltaic station according to the modal prediction result and the prediction weight value corresponding to each of the first modal components.

[0068] The technical solution of the embodiment of the present invention obtains a multimodal historical data set corresponding to a photovoltaic station, and uses a multivariate variational modal decomposition method to perform modal decomposition on the multimodal historical data set to obtain multiple first modal components; obtains modal prediction results corresponding to each first modal component according to multiple first modal components through a kernel extreme learning machine prediction model optimized by a multi-objective white shark optimization algorithm; searches for prediction weight values ​​corresponding to each first modal component, and obtains a power generation prediction result corresponding to the photovoltaic station according to the modal prediction results and prediction weight values ​​corresponding to each first modal component; optimizes the kernel extreme learning machine prediction model by using a multi-objective white shark optimization algorithm to predict different modal components, and obtains a final power generation prediction result according to the modal prediction results and prediction weight values ​​corresponding to different modal components, thereby realizing multimodal data feature mining and efficient prediction of photovoltaic stations, and improving the accuracy of power prediction of photovoltaic power generation.

[0069] Optionally, the photovoltaic power generation power prediction device further includes:

[0070] A sample set generation module is used to generate a photovoltaic station sample data set according to the multimodal historical data sets corresponding to different photovoltaic stations, and divide the photovoltaic station sample data set according to a preset ratio to obtain a training sample set and a test sample set;

[0071] The model optimization module is used to establish an initial nuclear extreme learning machine prediction model, and adopt a multi-objective white shark optimization algorithm to optimize the initial nuclear extreme learning machine prediction model according to the training sample set and the test sample set to obtain a nuclear extreme learning machine prediction model optimized by the multi-objective white shark optimization algorithm.

[0072] Optionally, a model optimization module is specifically used to generate an initial population according to the initial kernel extreme learning machine prediction model, wherein the initial population includes a plurality of individuals, each of which corresponds to a set of model parameters;

[0073] A multi-objective white shark optimization algorithm is used to update the initial population according to the training sample set and the test sample set until it is detected that the preset optimization conditions are met, and a kernel extreme learning machine prediction model optimized by the multi-objective white shark optimization algorithm is obtained according to the current population.

[0074] Optionally, the photovoltaic power generation power prediction device further includes:

[0075] A sample decomposition module, used to perform modal decomposition on the training sample set using a multivariate variational modal decomposition method to obtain a plurality of second modal components;

[0076] The weight acquisition module is used to calculate the permutation entropy corresponding to each of the second modal components, and obtain the prediction weight value corresponding to each of the second modal components based on the permutation entropy.

[0077] Optionally, the power generation prediction module 230 is specifically used to calculate the product of each of the modal prediction results multiplied by the corresponding prediction weight value, and calculate the sum of each product;

[0078] The quotient of the sum of the products divided by the sum of the prediction weight values ​​is calculated as the prediction result of the generated power corresponding to the photovoltaic station.

[0079] Optionally, the multimodal historical data set includes historical power generation data, historical ledger data, and historical weather data.

[0080] The photovoltaic power generation power prediction device provided in the embodiment of the present invention can execute the photovoltaic power generation power prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0081] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0082] Embodiment 3

[0083] Figure 4 A schematic diagram of the structure of an electronic device 30 that can be used to implement an embodiment of the present invention is shown. The electronic device 30 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 30 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0084] like Figure 4As shown, the electronic device 30 includes at least one processor 31, and a memory connected to the at least one processor 31, such as a read-only memory (ROM) 32, a random access memory (RAM) 33, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 31 can perform various appropriate actions and processes according to the computer program stored in the read-only memory 32 or the computer program loaded from the storage unit 38 to the random access memory 33. In the RAM 33, various programs and data required for the operation of the electronic device 30 can also be stored. The processor 31, the ROM 32 and the RAM 33 are connected to each other through a bus 34. The input / output (I / O) interface 35 is also connected to the bus 34.

[0085] A number of components in the electronic device 30 are connected to the I / O interface 35, including: an input unit 36, such as a keyboard, a mouse, etc.; an output unit 37, such as various types of displays, speakers, etc.; a storage unit 38, such as a disk, an optical disk, etc.; and a communication unit 39, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 39 allows the electronic device 30 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0086] The processor 31 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 31 include, but are not limited to, a central processing unit, a graphics processing unit, various special artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any appropriate processors, controllers, microcontrollers, etc. The processor 31 executes the various methods and processes described above, such as a power prediction method for photovoltaic power generation.

[0087] In some embodiments, the power prediction method for photovoltaic power generation can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 38. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 30 via the ROM 32 and / or the communication unit 39. When the computer program is loaded into the RAM 33 and executed by the processor 31, one or more steps of the power prediction method for photovoltaic power generation described above can be performed. Alternatively, in other embodiments, the processor 31 can be configured to execute the power prediction method for photovoltaic power generation in any other appropriate manner (for example, by means of firmware).

[0088] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays, application specific integrated circuits, application specific standard products, systems on a chip, load programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0089] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0090] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0091] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device 30 having: a display device (e.g., a cathode ray tube or a liquid crystal display) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device 30. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0092] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area networks, wide area networks, blockchain networks, and the Internet.

[0093] A computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other. The server may be a cloud server.

[0094] This embodiment may also include a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the photovoltaic power generation power prediction method provided by any embodiment of the present invention.

[0095] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0096] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting power of photovoltaic power generation, characterized in that: include: Acquire a multimodal historical data set corresponding to the photovoltaic station, and perform modal decomposition on the multimodal historical data set using a multivariate variational modal decomposition method to obtain a plurality of first modal components; Obtaining a modal prediction result corresponding to each of the first modal components according to the multiple first modal components using a kernel extreme learning machine prediction model optimized by a multi-objective white shark optimization algorithm; The prediction weight value corresponding to each of the first modal components is searched, and the power generation prediction result corresponding to the photovoltaic station is obtained according to the modal prediction result and the prediction weight value corresponding to each of the first modal components.

2. The method according to claim 1, characterized in that Before obtaining the modal prediction results corresponding to each of the first modal components according to the plurality of first modal components using the kernel extreme learning machine prediction model optimized by the multi-objective white shark optimization algorithm, the method further includes: Generate a photovoltaic station sample data set according to the multimodal historical data sets corresponding to different photovoltaic stations, and divide the photovoltaic station sample data set according to a preset ratio to obtain a training sample set and a test sample set; An initial nuclear extreme learning machine prediction model is established, and a multi-objective white shark optimization algorithm is used to optimize the initial nuclear extreme learning machine prediction model according to the training sample set and the test sample set to obtain a nuclear extreme learning machine prediction model optimized by the multi-objective white shark optimization algorithm.

3. The method according to claim 2, characterized in that The multi-objective White Shark optimization algorithm is used to optimize the initial kernel extreme learning machine prediction model according to the training sample set and the test sample set to obtain the kernel extreme learning machine prediction model optimized by the multi-objective White Shark optimization algorithm, including: Generate an initial population according to the initial kernel extreme learning machine prediction model, wherein the initial population includes a plurality of individuals, each of which corresponds to a set of model parameters; A multi-objective white shark optimization algorithm is used to update the initial population according to the training sample set and the test sample set until it is detected that the preset optimization conditions are met, and a kernel extreme learning machine prediction model optimized by the multi-objective white shark optimization algorithm is obtained according to the current population.

4. The method according to claim 2, characterized in that: After dividing the photovoltaic station sample data set according to a preset ratio to obtain a training sample set and a test sample set, the method further includes: Performing modal decomposition on the training sample set using a multivariate variational modal decomposition method to obtain a plurality of second modal components; The permutation entropy corresponding to each of the second modal components is calculated, and the prediction weight value corresponding to each of the second modal components is obtained according to the permutation entropy.

5. The method according to claim 1, characterized in that According to the modal prediction results and prediction weight values ​​corresponding to each of the first modal components, the power generation prediction results corresponding to the photovoltaic station are obtained, including: Calculating the product of each of the modal prediction results multiplied by the corresponding prediction weight value, and calculating the sum of each product; The quotient of the sum of the products divided by the sum of the prediction weight values ​​is calculated as the prediction result of the generated power corresponding to the photovoltaic station.

6. The method according to any one of claims 1 to 5, characterized in that The multimodal historical data set includes historical power generation data, historical ledger data, and historical weather data.

7. A photovoltaic power generation power prediction device, characterized in that: include: A modal decomposition module is used to obtain a multimodal historical data set corresponding to a photovoltaic station, and perform modal decomposition on the multimodal historical data set using a multivariate variational modal decomposition method to obtain a plurality of first modal components; A modal prediction result acquisition module, configured to acquire a modal prediction result corresponding to each of the first modal components according to the plurality of first modal components using a kernel extreme learning machine prediction model optimized by a multi-objective white shark optimization algorithm; The power generation prediction module is used to find the prediction weight value corresponding to each of the first modal components, and obtain the power generation prediction result corresponding to the photovoltaic station according to the modal prediction result and the prediction weight value corresponding to each of the first modal components.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor, and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the photovoltaic power generation power prediction method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is used to enable a processor to implement the photovoltaic power generation power prediction method according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the photovoltaic power generation power prediction method according to any one of claims 1 to 6.

Images