Photovoltaic power station power prediction method, device, equipment, storage medium and product

Abstract

The invention relates to the technical field of power prediction, and discloses a photovoltaic power station power prediction method, device and equipment, a storage medium and a product, and the method comprises the steps: integrating the meteorological data of at least two meteorological data sources, and obtaining the integrated multi-source meteorological data; predicting the solar irradiance of the photovoltaic power station in the prediction day according to the multi-source meteorological data; and predicting the output power data of the photovoltaic power station on the prediction day according to the solar irradiance on the prediction day and the real-time power station environment parameters by using a deep learning algorithm. According to the method, various influence factors can be fully considered, the power prediction result is updated in real time, the latest meteorological change is reflected in time, and accurate and reliable power generation prediction is provided for the photovoltaic power station.

Description

Technical Field

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

[0002] As the global demand for renewable energy continues to increase, solar energy has received widespread attention as a clean and sustainable energy source. In recent years, the advancement of solar energy technology and the reduction of costs have led to its rapid promotion around the world. Especially in areas with abundant sunlight resources, solar power generation has become one of the main energy production methods. However, due to the influence of various factors such as climate and geography, the efficiency and stability of solar power generation still face many challenges. Solving these challenges is of great significance to improving the efficiency of solar energy resource utilization and reducing energy costs.

[0003] High altitude areas usually have abundant solar energy resources, especially in summer, with long sunshine hours and high solar irradiance, which provide good natural conditions for solar power generation. However, the meteorological conditions in these areas are complex and changeable, such as drastic temperature changes and frequent wind speed changes, which put higher requirements on the stability and power prediction of solar power generation equipment. In addition, the thin air in high altitude areas also poses challenges to the prediction accuracy of solar irradiance. Therefore, it is particularly important to develop efficient solar energy prediction technology in view of the special climatic conditions in high altitude areas.

[0004] Most of the current photovoltaic power station power prediction technologies rely on a single meteorological data source, and the prediction models are mainly based on simple physical or statistical methods, which to a certain extent limits the accuracy and practicality of the prediction. Especially under complex climate conditions, a single data source is difficult to fully capture meteorological changes, resulting in large prediction errors. In addition, when predicting the power of photovoltaic power stations, only meteorological factors are considered without considering factors such as power station environmental parameters such as photovoltaic panel temperature, which further increases the prediction error. Summary of the invention

[0005] In view of this, the present invention provides a photovoltaic power station power prediction method, device, equipment, storage medium and product to solve the technical problem of low prediction accuracy of existing photovoltaic power station power prediction methods.

[0006] In a first aspect, the present invention provides a method for predicting power of a photovoltaic power station, comprising: integrating meteorological data from at least two meteorological data sources to obtain integrated multi-source meteorological data; predicting the predicted daily solar irradiance of the photovoltaic power station based on the multi-source meteorological data; and using a deep learning algorithm to predict the predicted daily output power data of the photovoltaic power station based on the predicted daily solar irradiance and real-time power station environmental parameters.

[0007] A photovoltaic power station power prediction method of the present invention reduces the prediction error caused by a single data source by integrating meteorological data from at least two meteorological data sources, improves the comprehensiveness and accuracy of meteorological information, predicts the predicted daily solar irradiance of the photovoltaic power station based on the integrated multi-source meteorological data, and predicts the predicted daily output power data of the photovoltaic power station by predicting the daily solar irradiance and real-time power station environmental parameters. It can fully consider multiple influencing factors, update the power prediction results in real time, and promptly reflect the latest meteorological changes, provide accurate and reliable power generation prediction for photovoltaic power stations, improve the utilization efficiency of solar energy resources, optimize power station operation and management, and significantly improve economic benefits and power supply stability.

[0008] Optionally, predicting the predicted daily solar irradiance of the photovoltaic power station based on the multi-source meteorological data includes: inputting the multi-source meteorological data into a pre-built solar irradiance prediction model, wherein the solar irradiance prediction model is a prediction model that combines a physical model and a machine learning model; and predicting the predicted daily solar irradiance of the photovoltaic power station through the solar irradiance prediction model.

[0009] By combining the physical model and the machine learning model, the solar irradiance prediction model can be used to predict the solar irradiance based on a variety of meteorological data, making the prediction results more accurate.

[0010] Optionally, the deep learning algorithm is a long short-term memory algorithm; correspondingly, the deep learning algorithm is used to predict the predicted daily output power data of the photovoltaic power station according to the predicted daily solar irradiance and the real-time power station environmental parameters, including: training the long short-term memory network according to historical solar irradiance, historical power output data and historical power station environmental parameters; inputting the predicted daily solar irradiance and the real-time power station environmental parameters into the trained long short-term memory network; and predicting the predicted daily output power data of the photovoltaic power station through the long short-term memory algorithm.

[0011] By using the long short-term memory algorithm to predict power, it is possible to comprehensively consider historical data and current meteorological conditions to provide highly accurate prediction results.

[0012] Optionally, after obtaining the integrated multi-source meteorological data, the method further includes: cleaning, aligning and denoising the multi-source meteorological data.

[0013] By cleaning, aligning and denoising the multi-source meteorological data, redundancy and noise in the data are removed, thereby improving data quality.

[0014] Optionally, meteorological data from at least two meteorological data sources are integrated to obtain integrated multi-source meteorological data, including: determining an optimal weight combination for the meteorological data from each meteorological data source; integrating the meteorological data from each meteorological data source through the optimal weight combination to obtain integrated multi-source meteorological data.

[0015] By integrating the data from various meteorological data sources by determining the optimal weight combination, the advantages and disadvantages of different data sources can be balanced and the accuracy of the overall forecast can be improved.

[0016] Optionally, the optimal weight combination is determined for the meteorological data of each meteorological data source, including: determining an objective function, the objective function is used to measure the power prediction accuracy of different weight combinations; initializing the prior distribution of the weight combination; extracting a set of weight combinations from the prior distribution, and integrating the meteorological data of each meteorological data source using the extracted weight combination, and obtaining a power prediction result based on the integrated multi-source meteorological data; calculating the power prediction accuracy under the current weight combination according to the objective function; calculating the posterior distribution using the Bayesian formula based on the power prediction accuracy under the current weight combination and the current prior distribution; using the posterior distribution as a new prior distribution, and re-extracting a set of weight combinations from the new prior distribution, and iteratively updating the prior distribution; after the iteration conditions are met, selecting the weight combination with the highest prediction accuracy in the iteration process as the optimal weight combination.

[0017] Combining the Bayesian optimization method to determine the optimal weight combination can improve the optimization efficiency of the optimal weight combination.

[0018] In the second aspect, the present invention provides a photovoltaic power station power prediction device, including: a multi-source data integration module, used to integrate meteorological data from at least two meteorological data sources to obtain integrated multi-source meteorological data; an irradiance prediction module, used to predict the predicted daily solar irradiance of the photovoltaic power station based on the multi-source meteorological data; a power prediction module, used to use a deep learning algorithm to predict the predicted daily output power data of the photovoltaic power station based on the predicted daily solar irradiance and real-time power station environmental parameters.

[0019] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the photovoltaic power station power prediction method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the photovoltaic power station power prediction method of the first aspect or any corresponding embodiment thereof.

[0021] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, wherein the computer instructions are used to enable a computer to execute the photovoltaic power station power prediction method as described in the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 is a schematic diagram of a process of a photovoltaic power station power prediction method according to an embodiment of the present invention;

[0024] Figure 2 is a structural block diagram of a photovoltaic power station power prediction device according to an embodiment of the present invention;

[0025] Figure 3 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] In the related art, the photovoltaic power station power prediction method has the following problems:

[0028] 1. Limitations of a single data source:

[0029] Most of the existing PV power generation relies on a single meteorological data source, such as using only data from the European Centre for Medium-Range Weather Forecasts (ECMWF). This single data source approach has obvious limitations, because each meteorological data source has its own unique advantages and disadvantages, and a single data source is difficult to fully and accurately reflect complex meteorological changes. Especially in high-altitude areas, where meteorological conditions are more variable, the prediction accuracy of a single data source is greatly reduced, affecting the prediction accuracy of solar irradiance, and thus affecting the prediction accuracy of PV power generation.

[0030] 2. Lack of data fusion and processing methods:

[0031] The current solar irradiance prediction system has deficiencies in data fusion and processing. Although some systems attempt to use multi-source data, data preprocessing, alignment, and fusion technologies are not mature enough, resulting in serious data redundancy and noise problems, affecting the accuracy and reliability of the prediction model. The existing technology lacks effective multimodal data fusion methods, making it difficult to fully utilize information from different meteorological data sources, resulting in inconsistent and unstable prediction results.

[0032] 3. The accuracy and real-time performance of the power prediction model are insufficient:

[0033] Most of the existing photovoltaic power station power prediction models are based on simple physical models or statistical models. They fail to fully consider a variety of influencing factors such as temperature, wind speed, cloud cover, snow cover, etc., and also fail to consider power station environmental parameters such as photovoltaic panel temperature and other factors, resulting in low prediction accuracy. In addition, these models are updated infrequently, have poor real-time performance, and are difficult to cope with rapidly changing meteorological conditions, especially in high-altitude areas. In this case, the prediction results cannot reflect the latest meteorological changes in a timely manner, affecting the operation and management efficiency of photovoltaic power stations.

[0034] Therefore, it is urgent to develop a new generation of multi-source data fusion and high-precision prediction models to improve the efficiency and reliability of solar power generation. The photovoltaic power station power prediction method of the embodiment of the present invention aims to improve the prediction accuracy and real-time performance of solar power generation in high-altitude areas, by integrating multi-source meteorological data, adopting advanced data processing and fusion technology, and constructing a high-precision solar irradiance and power prediction model, comprehensively optimizing the utilization efficiency of solar energy resources, improving the operation and management level of photovoltaic power stations, and achieving higher economic benefits and power supply stability.

[0035] According to an embodiment of the present invention, an embodiment of a photovoltaic power station power prediction method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] In this embodiment, a photovoltaic power station power prediction method is provided, which can be used in mobile terminals such as computers, mobile phones, etc. Figure 1 As shown, the process includes the following steps:

[0037] Step S101, integrating meteorological data from at least two meteorological data sources to obtain integrated multi-source meteorological data.

[0038] Specifically, the integrated meteorological data comes from multiple different meteorological data sources, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Meteorological Service, the US Environmental Forecast Center (GFS), etc. The meteorological data from different sources are integrated through multi-source data fusion technology, combining the advantages of each data source, overcoming the limitations of a single data source, and providing comprehensive and accurate meteorological information. It can then more comprehensively capture the complex and changeable meteorological conditions in high-altitude areas, laying the foundation for subsequent solar irradiance and power forecasts.

[0039] Step S102: predicting the predicted daily solar irradiance of the photovoltaic power station based on multi-source meteorological data.

[0040] Specifically, the multi-source meteorological data includes at least two meteorological factors that affect solar irradiance, such as temperature, cloud cover, wind speed, humidity, etc.

[0041] Through the pre-trained solar irradiance prediction model, the solar irradiance can be accurately predicted based on various meteorological factors such as temperature, cloud cover, wind speed, and humidity.

[0042] Step S103, using a deep learning algorithm, predicting the predicted daily output power data of the photovoltaic power station according to the predicted daily solar irradiance and the real-time power station environmental parameters.

[0043] Specifically, the real-time power station environmental parameters are real-time operating parameters that affect the power generation of the power station, such as the temperature of the photovoltaic panel.

[0044] Since photovoltaic power stations use solar energy to generate electricity, solar irradiance and power generation are closely related. Based on solar irradiance, the power generation power can be predicted more accurately. In addition, the impact of real-time power station environmental parameters on power generation power is also considered, making the power prediction results more accurate.

[0045] A photovoltaic power station power prediction method according to an embodiment of the present invention reduces the prediction error caused by a single data source by integrating meteorological data from at least two meteorological data sources, improves the comprehensiveness and accuracy of meteorological information, predicts the predicted daily solar irradiance of the photovoltaic power station based on the integrated multi-source meteorological data, and predicts the predicted daily output power data of the photovoltaic power station by predicting the daily solar irradiance and real-time power station environmental parameters. It can fully consider various influencing factors, update the power prediction results in real time, and promptly reflect the latest meteorological changes, provide accurate and reliable power generation prediction for photovoltaic power stations, improve the utilization efficiency of solar energy resources, optimize power station operation and management, and significantly improve economic benefits and power supply stability.

[0046] In some embodiments, step S102, predicting the predicted daily solar irradiance of the photovoltaic power station according to multi-source meteorological data, includes:

[0047] Step S1021, inputting multi-source meteorological data into a pre-built solar irradiance prediction model, wherein the solar irradiance prediction model is a prediction model that combines a physical model and a machine learning model.

[0048] Step S1022, predicting the predicted daily solar irradiance of the photovoltaic power station using a solar irradiance prediction model.

[0049] Specifically, the embodiment of the present invention combines the physical model and the machine learning model to construct a high-precision solar irradiance prediction model, which can take into account a variety of factors affecting solar irradiance, such as temperature, cloud cover, wind speed, humidity, etc. Combined with real-time updated data and the Bayesian method for iterative optimization, the solar irradiance prediction model can quickly respond to changes in meteorological conditions, provide accurate solar irradiance prediction results, and provide a reliable basis for solar power generation.

[0050] The embodiment of the present invention uses a solar irradiance prediction model that combines a physical model and a machine learning model to predict solar irradiance based on a variety of meteorological data, making the prediction result more accurate.

[0051] In some embodiments, the deep learning algorithm is a Long Short-Term Memory (LSTM) algorithm.

[0052] Correspondingly, step S102, using a deep learning algorithm, predicts the predicted daily output power data of the photovoltaic power station according to the predicted daily solar irradiance and the real-time power station environmental parameters, including:

[0053] Step S1021, training the long short-term memory network according to historical solar irradiance, historical power output data and historical power station environmental parameters.

[0054] Step S1022, input the predicted daily solar irradiance and real-time power station environmental parameters into the trained long short-term memory network.

[0055] Step S1023, predicting the predicted daily output power data of the photovoltaic power station by using the long short-term memory algorithm.

[0056] Specifically, to achieve high-precision real-time power prediction and optimization, the embodiment of the present invention adopts an advanced deep learning algorithm, the long short-term memory algorithm. LSTM is a special recursive neural network that can effectively process and predict time series data, and is particularly suitable for capturing complex time dependencies and nonlinear characteristics in the solar power generation process.

[0057] LSTM, through its unique memory unit and gating mechanism, can remember long-term time dependencies and ignore irrelevant information. This enables LSTM to comprehensively consider historical data and current meteorological data when predicting changes between solar irradiance and power, providing highly accurate prediction results.

[0058] It should be understood that the historical solar irradiance, historical power output data and historical power station environmental parameters are the solar irradiance data, power data and environmental parameter data before the prediction day, which are obtained through the historical records of the power station. The real-time power station environmental parameters are obtained by real-time monitoring of the power station operating parameters.

[0059] In one example, historical solar irradiance and predicted daily solar irradiance constitute irradiance time series data, historical power station environmental parameters and real-time power station environmental parameters constitute environmental parameter time series, and historical power output data is used as power time series. The LSTM network of the long short-term memory algorithm is used to learn these time series and output predicted daily output power data.

[0060] In the application of the embodiment of the present invention, the LSTM network is trained by historical solar irradiance, historical power output data and historical power station environmental parameters to learn the patterns and relationships in these data, so that the output power of the solar power station can be predicted in real time in practical applications. This real-time power prediction and optimization algorithm that combines LSTM deep learning and reinforcement learning enables accurate prediction of changes in photovoltaic power station power under complex and changeable meteorological conditions.

[0061] In some embodiments, in step S101, after obtaining the integrated multi-source meteorological data, the step further includes: cleaning, aligning and denoising the multi-source meteorological data.

[0062] Specifically, after data integration, advanced data preprocessing and noise filtering technologies are used to clean, align and denoise multi-source meteorological data. Machine learning algorithms and statistical methods are used to remove redundancy and noise in the data and improve data quality. This step ensures the accuracy and reliability of the data input into the prediction model, thereby improving the accuracy of the prediction results.

[0063] In some embodiments, step S101, integrating meteorological data from at least two meteorological data sources to obtain integrated multi-source meteorological data, includes:

[0064] Step S1011, determining an optimal weight combination for the meteorological data from each meteorological data source.

[0065] Specifically, the Bayesian optimization method is used to utilize the probability model to gradually approximate the optimal weight combination and achieve the optimal weight allocation for the data source, thereby improving the prediction accuracy.

[0066] The specific process of the Bayesian optimization method includes the following steps:

[0067] 1. Define the objective function: First, define an objective function (root mean square error, correlation coefficient, etc.) to measure the prediction accuracy under different weight combinations.

[0068] 2. Initialize the prior distribution: Initialize the prior distribution of the weight combination and select a uniform distribution as the prior distribution to represent the initial uncertainty of the weight combination.

[0069] 3. Sampling: Randomly extract a set of weight combinations from the prior distribution, and then use these weight combinations to fuse multi-source meteorological data to obtain power prediction results.

[0070] 4. Evaluate the objective function: Use historical data for cross-validation and calculate the prediction accuracy under the current weight combination based on the objective function to ensure the reliability of the evaluation results.

[0071] 5. Update the posterior distribution: Based on the prediction accuracy of the current weight combination and the prior distribution, the posterior distribution is calculated using the Bayesian formula.

[0072] 6. Select a new weight combination: Based on the posterior distribution, select the weight combination with the highest prediction accuracy as the starting point for the next iteration.

[0073] 7. Repeat steps 3-6 until the stopping condition is met: the number of iterations (such as 10 times), prediction accuracy threshold or computing resource limit can be set as the stopping condition.

[0074] 8. Output the optimal weight combination: After the stopping condition is met, select the weight combination with the highest prediction accuracy as the final result.

[0075] Step S1012, integrating the meteorological data from various meteorological data sources through optimal weight combination to obtain integrated multi-source meteorological data.

[0076] Specifically, the optimal weight combination determines the weight of each meteorological data, and assigns weights to the meteorological data of each meteorological data source based on the optimal weight combination. For example, taking temperature data as an example, there are two meteorological data sources, 35℃ and 30℃ respectively. The weights obtained according to the optimal weight combination are 0.6 and 0.4 respectively, and the integrated multi-source meteorological data of temperature is 0.6×35℃+0.4×30℃.

[0077] The embodiment of the present invention determines the optimal weight combination by combining the Bayesian optimization method, thereby improving the optimization efficiency of the optimal weight combination, balancing the advantages and disadvantages of different data sources, and improving the accuracy of the overall prediction.

[0078] A photovoltaic power station power prediction method according to an embodiment of the present invention integrates multi-source meteorological data, adopts advanced data processing and fusion technology, constructs a high-precision solar irradiance prediction model, and predicts the predicted daily output power data of the photovoltaic power station by combining the predicted solar irradiance obtained by the long short-term memory algorithm, which can significantly improve the accuracy and real-time performance of solar power generation power prediction. First, multi-source data fusion can integrate the advantages of various meteorological data sources, reduce the prediction error caused by a single data source, and improve the comprehensiveness and accuracy of meteorological information. Secondly, advanced data processing and fusion technology can effectively filter data noise, optimize data quality, and enhance the stability of the prediction model. Finally, the high-precision solar irradiance prediction model can consider a variety of influencing factors, update the prediction results of the predicted solar irradiance on the predicted day in real time, and promptly reflect the latest meteorological changes, providing an accurate and reliable power generation prediction basis for photovoltaic power stations, and combining the real-time power station environmental parameter learning prediction to obtain accurate power generation, optimize power station operation and management, and significantly improve economic benefits and power supply stability.

[0079] The embodiment of the present invention also provides a photovoltaic power station power prediction device, such as Figure 2 As shown, including:

[0080] The multi-source data integration module 201 is used to integrate the meteorological data from at least two meteorological data sources to obtain integrated multi-source meteorological data;

[0081] The irradiance prediction module 202 is used to predict the predicted daily solar irradiance of the photovoltaic power station based on multi-source meteorological data;

[0082] The power prediction module 203 is used to predict the predicted daily output power data of the photovoltaic power station according to the predicted daily solar irradiance and real-time power station environmental parameters by using a deep learning algorithm.

[0083] The photovoltaic power station power prediction device of the embodiment of the present invention reduces the prediction error caused by a single data source by integrating meteorological data from at least two meteorological data sources, improves the comprehensiveness and accuracy of meteorological information, predicts the predicted daily solar irradiance of the photovoltaic power station based on the integrated multi-source meteorological data, and predicts the predicted daily output power data of the photovoltaic power station by predicting the daily solar irradiance and the real-time power station environmental parameters. It can fully consider various influencing factors, update the power prediction results in real time, and promptly reflect the latest meteorological changes, provide accurate and reliable power generation predictions for photovoltaic power stations, improve the utilization efficiency of solar energy resources, optimize power station operations and management, and significantly improve economic benefits and power supply stability.

[0084] Furthermore, the irradiance prediction module 202 includes:

[0085] A meteorological data input module, used to input multi-source meteorological data into a pre-built solar irradiance prediction model, wherein the solar irradiance prediction model is a prediction model combining a physical model and a machine learning model;

[0086] The model prediction module predicts the predicted daily solar irradiance of the photovoltaic power station through the solar irradiance prediction model.

[0087] Further, the deep learning algorithm is a long short-term memory algorithm; correspondingly, the power prediction module 203 includes:

[0088] A network training module is used to train the long short-term memory network based on historical solar irradiance, historical power output data, and historical power station environmental parameters;

[0089] Parameter input module, used to input the predicted daily solar irradiance and real-time power station environmental parameters into the trained long short-term memory network;

[0090] The power output module is used to predict the predicted daily output power data of the photovoltaic power station through the long short-term memory algorithm.

[0091] Furthermore, the photovoltaic power station power prediction device also includes:

[0092] The data processing module is used to clean, align and denoise multi-source meteorological data.

[0093] Furthermore, the multi-source data integration module includes:

[0094] A weight optimization module is used to determine the optimal weight combination for the meteorological data from each meteorological data source;

[0095] The weight integration module is used to integrate the meteorological data from various meteorological data sources through the optimal weight combination to obtain integrated multi-source meteorological data.

[0096] Furthermore, the weight optimization module includes:

[0097] A function determination module is used to determine an objective function, and the objective function is used to measure the power prediction accuracy of different weight combinations;

[0098] Prior distribution initialization module, used to initialize the prior distribution of weight combinations;

[0099] A prediction result acquisition module is used to extract a set of weight combinations from the prior distribution, and use the extracted weight combinations to integrate the meteorological data from various meteorological data sources, and obtain power prediction results based on the integrated multi-source meteorological data;

[0100] A prediction accuracy calculation module is used to calculate the power prediction accuracy under the current weight combination according to the objective function;

[0101] A posterior distribution calculation module is used to calculate the posterior distribution using the Bayesian formula according to the power prediction accuracy under the current weight combination and the current prior distribution;

[0102] The iteration module is used to use the posterior distribution as the new prior distribution, and re-extract a set of weight combinations from the new prior distribution to iteratively update the prior distribution;

[0103] The weight output module is used to select the weight combination with the highest prediction accuracy in the iteration process as the optimal weight combination after the iteration conditions are met.

[0104] The embodiment of the present invention also provides a schematic diagram of the structure of a computer device, such as Figure 3 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0105] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0106] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0107] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0108] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0109] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0110] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0111] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0112] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0113] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope of protection.

Claims

1. A photovoltaic power station power prediction method, characterized in that: include: integrating meteorological data from at least two meteorological data sources to obtain integrated multi-source meteorological data; predicting a predicted daily solar irradiance of a photovoltaic power station based on the multi-source meteorological data; The predicted daily output power data of the photovoltaic power station is predicted according to the predicted daily solar irradiance and the real-time power station environmental parameters by using a deep learning algorithm.

2. The photovoltaic power station power prediction method according to claim 1, characterized in that: Predicting the predicted daily solar irradiance of the photovoltaic power station according to the multi-source meteorological data includes: Inputting the multi-source meteorological data into a pre-built solar irradiance prediction model, wherein the solar irradiance prediction model is a prediction model combining a physical model and a machine learning model; The predicted daily solar irradiance of the photovoltaic power station is predicted by the solar irradiance prediction model.

3. The photovoltaic power station power prediction method according to claim 1, characterized in that: The deep learning algorithm is a long short-term memory algorithm; Correspondingly, using a deep learning algorithm, the predicted daily output power data of the photovoltaic power station is predicted according to the predicted daily solar irradiance and the real-time power station environmental parameters, including: The long short-term memory network is trained based on historical solar irradiance, historical power output data, and historical power station environmental parameters; Inputting the predicted daily solar irradiance and the real-time power station environmental parameters into a trained long short-term memory network; The predicted daily output power data of the photovoltaic power station is predicted by the long short-term memory algorithm.

4. The photovoltaic power station power prediction method according to claim 1, characterized in that: After obtaining the integrated multi-source meteorological data, it also includes: The multi-source meteorological data are cleaned, aligned and denoised.

5. The photovoltaic power station power prediction method according to claim 1, characterized in that: Integrate meteorological data from at least two meteorological data sources to obtain integrated multi-source meteorological data, including: Determine the optimal weight combination for the meteorological data from each meteorological data source; The meteorological data from various meteorological data sources are integrated through the optimal weight combination to obtain integrated multi-source meteorological data.

6. The photovoltaic power station power prediction method according to claim 1, characterized in that: Determine the optimal weight combination for the meteorological data from each meteorological data source, including: Determining an objective function, wherein the objective function is used to measure the power prediction accuracy of different weight combinations; Initialize the prior distribution of weight combinations; Extracting a set of weight combinations from the prior distribution, and integrating the meteorological data from various meteorological data sources using the extracted weight combinations, and obtaining a power prediction result based on the integrated multi-source meteorological data; Calculate the power prediction accuracy under the current weight combination according to the objective function; According to the power prediction accuracy under the current weight combination and the current prior distribution, the posterior distribution is calculated using the Bayesian formula; The posterior distribution is used as the new prior distribution, and a set of weight combinations are re-extracted from the new prior distribution to iteratively update the prior distribution; After the iteration conditions are met, the weight combination with the highest prediction accuracy in the iteration process is selected as the optimal weight combination.

7. A photovoltaic power station power prediction device, characterized in that: include: A multi-source data integration module, used to integrate meteorological data from at least two meteorological data sources to obtain integrated multi-source meteorological data; An irradiance prediction module, used to predict the predicted daily solar irradiance of the photovoltaic power station based on the multi-source meteorological data; The power prediction module is used to use a deep learning algorithm to predict the predicted daily output power data of the photovoltaic power station according to the predicted daily solar irradiance and real-time power station environmental parameters.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the photovoltaic power station power prediction method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the photovoltaic power station power prediction method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the photovoltaic power station power prediction method according to any one of claims 1 to 6.

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