Solar power generation power prediction method and device, electronic equipment and storage medium

Abstract

The invention provides a solar power generation power prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of power prediction.The method comprises the steps that based on physical parameters of a target area, first power generation power of solar power generation equipment is predicted through a physical model; the physical model is a physical parameter and power generation power mapping model constructed based on a solar celestial body operation rule and a photovoltaic power generation principle; based on the meteorological data of the target area and the historical power generation data of the solar power generation equipment, utilizing a deep learning model to predict second power generation power of the solar power generation equipment; and fusing the first power generation power and the second power generation power based on the weather type of the target area to obtain third power generation power of the solar power generation equipment. According to the solar power generation power prediction method and device, the electronic equipment and the storage medium provided by the invention, the reliability of solar power generation power prediction can be improved, and accurate power grid dispatching and energy management are facilitated.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of power prediction, and more specifically, relates to a method and device for predicting solar power generation, electronic equipment, and storage medium. Background Art

[0002] As a clean and renewable energy source, solar energy can reduce the consumption of fossil energy such as coal and oil, thereby reducing environmental pollution, but its intermittent and volatile nature brings challenges to grid dispatch and energy management. Accurate solar power forecasting is crucial to the stable operation of the grid, economic dispatch, ancillary service configuration and participation in the electricity market.

[0003] In recent years, artificial intelligence prediction methods based on machine learning and deep learning have been widely used in the field of solar power prediction. Such methods can handle high-dimensional nonlinear relationships, have strong adaptive learning capabilities, can integrate multi-source heterogeneous data, and have high prediction accuracy under big data conditions. However, artificial intelligence methods have the "black box" characteristics that lead to poor interpretability, and training requires a large amount of high-quality data; secondly, artificial intelligence models are not adaptable enough when facing new scenarios, abnormal weather or changes in equipment status, resulting in low reliability of prediction results. Summary of the invention

[0004] The purpose of the present disclosure is to provide a solar power generation power prediction method and device, electronic equipment, and storage medium to improve the reliability of solar power generation power prediction and facilitate accurate grid scheduling and energy management.

[0005] A first aspect of the embodiments of the present disclosure provides a method for predicting solar power generation, comprising: Based on the physical parameters of the target area, a physical model is used to predict the first power generation power of the solar power generation equipment; the physical model is a mapping model of physical parameters and power generation power constructed based on the movement law of the solar celestial body and the mechanics of photovoltaic power generation, and the target area is the area where the solar power generation equipment is located; Based on the meteorological data of the target area and the historical power generation data of the solar power generation equipment, using a deep learning model to predict the second power generation power of the solar power generation equipment; The first power generation power and the second power generation power are merged based on the weather type of the target area to obtain the third power generation power of the solar power generation equipment.

[0006] A second aspect of the embodiments of the present disclosure provides a solar power generation power prediction device, comprising: A first prediction module is used to predict a first power generation power of the solar power generation device using a physical model based on physical parameters of the target area; the physical model is a mapping model of physical parameters and power generation power constructed based on the movement law of the solar celestial body and the mechanics of photovoltaic power generation, and the target area is the area where the solar power generation device is located; A second prediction module is used to predict the second power generation power of the solar power generation equipment by using a deep learning model based on the meteorological data of the target area and the historical power generation data of the solar power generation equipment; The data fusion module is used to fuse the first power generation and the second power generation based on the weather type of the target area to obtain the third power generation of the solar power generation equipment.

[0007] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned solar power generation power prediction method when executing the computer program.

[0008] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned solar power generation power prediction method are implemented.

[0009] The beneficial effects of the solar power generation power prediction method and device, electronic device, and storage medium provided by the embodiments of the present disclosure are: First, the embodiment of the present disclosure introduces a physical model to predict the power generation, so that the power generation prediction of the solar power generation equipment has a physical basis and is more explainable; Secondly, the disclosed embodiment performs two predictions, namely: the first power generation prediction based on the physical model and the second power generation prediction based on the deep learning model, on which the prediction results of the two predictions are integrated. In this way, the prediction process based on the physical model can effectively make up for the defect of "the lack of adaptability of the deep learning model when facing new scenes, abnormal weather or changes in equipment status", and can also make up for the defect of "the low prediction accuracy of the deep learning model when lacking high-quality data", thereby effectively solving the problems of the prior art.

[0010] In addition, it should be pointed out that the embodiment of the present disclosure does not simply merge the results of the two predictions, but dynamically merges them based on the weather type, thereby further improving the applicability of power generation prediction to actual application scenarios, effectively ensuring the accuracy and reliability of solar power generation prediction, and facilitating accurate grid scheduling and energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of a process of predicting solar power generation provided by an embodiment of the present disclosure; Figure 2 A schematic diagram of a physical model structure provided in one embodiment of the present disclosure; Figure 3 A schematic diagram of a linear fusion layer structure provided in one embodiment of the present disclosure; Figure 4 A schematic diagram of a deep learning model structure provided in one embodiment of the present disclosure; Figure 5 A structural block diagram of a solar power generation power prediction device provided by an embodiment of the present disclosure; Figure 6 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0014] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0015] Please refer to Figure 1 , Figure 1 A schematic diagram of a process of predicting solar power generation provided by an embodiment of the present disclosure, the method comprising: S101: Based on the physical parameters of the target area, a physical model is used to predict the first power generation power of the solar power generation equipment; the physical model is a mapping model of physical parameters and power generation power constructed based on the movement law of the solar celestial body and the mechanics of photovoltaic power generation, and the target area is the area where the solar power generation equipment is located.

[0016] In this embodiment, the physical parameters of the target area may include the geographical location (latitude and longitude), altitude, atmospheric conditions (such as atmospheric transparency, cloud cover, etc.) of the target area, and characteristic parameters of the solar power generation equipment itself (such as the type, area, conversion efficiency, etc. of photovoltaic cells). The above physical parameters will directly or indirectly affect the amount of solar radiation received by the solar power generation equipment, and the ability to convert solar radiation into electrical energy, thereby affecting the power generation of the solar power generation equipment.

[0017] In this embodiment, the solar celestial body movement law refers to the astronomical law of the earth's revolution and rotation around the sun. Based on the solar celestial body movement law, the change of the sun's position in the sky over time and geographical location can be calculated. In this embodiment, the solar celestial body movement law can be modeled to obtain a solar geometric model. Based on the solar geometric model, parameters such as the solar altitude angle and azimuth angle at a specific time and target area can be accurately calculated, and then the angle between the sun's rays and the ground and the surface of the solar power generation equipment can be determined, and then the incident amount of solar radiation on the solar power generation equipment can be calculated.

[0018] In this embodiment, the photovoltaic power generation mechanism (also known as the photovoltaic effect) is the basic principle of solar power generation. When sunlight shines on a photovoltaic cell made of semiconductor materials (such as silicon), the photon energy is transferred to the electrons in the semiconductor, causing the electrons to transition into free electrons, thereby generating current. Based on this mechanism, a physical model can be established to describe the relationship between solar radiation energy and the current and voltage generated by the photovoltaic cell, thereby deriving the power generation.

[0019] In this embodiment, the power generation power of the solar power generation equipment is predicted based on the physical model, which can reflect the theoretical value of the solar power generation power more scientifically based on the physical essence, and the prediction result is more interpretable.

[0020] S102: Based on the meteorological data of the target area and the historical power generation data of the solar power generation equipment, a deep learning model is used to predict the second power generation power of the solar power generation equipment.

[0021] In this embodiment, complex power generation patterns can be learned directly from historical power generation data and meteorological data based on the deep learning model, thereby predicting the power generation power of solar power generation equipment.

[0022] Specifically, the deep learning model may include a feature extraction network, a time series modeling network, and a fully connected layer. On this basis, predicting the second power generation of the target area based on the deep learning model may include: Extracting features from historical power generation data and meteorological data of the target area based on a feature extraction network to obtain first feature data; Inputting the first characteristic data into the time series modeling network to obtain second characteristic data; The second feature data is input into the fully connected layer to obtain the second generated power of the target area.

[0023] In this embodiment, the potential patterns and regularities in meteorological data and historical power generation data are mined based on the deep learning model, which can capture nonlinear relationships and random factors that are difficult to accurately describe with the physical model, so that the second power generation power of the solar power generation equipment can be predicted with higher accuracy.

[0024] S103: The first power generation power and the second power generation power are integrated based on the weather type of the target area to obtain a third power generation power of the solar power generation equipment.

[0025] In this embodiment, the third power generation is the predicted result of the solar power generation in the target area. The third power generation is obtained by fusing the first power generation and the second power generation. The respective advantages of the physical model and the deep learning model can be utilized to improve the accuracy of the solar power generation prediction.

[0026] Specifically, the first power generation and the second power generation can be fused in a weighted summation manner. At the same time, considering that the prediction effects of the physical model and the deep learning model are different under different weather types, different weights can be selected according to the weather type when performing weighted summation on the physical model and the deep learning model. For example, in clear weather, the prediction results of the physical model are closer to the actual value, and the corresponding weight of the physical model can be increased; while in cloudy, overcast days or when there are special meteorological phenomena (such as dust and rainstorms), the deep learning model can better handle complex situations and the weight of its prediction results can be increased. This embodiment adaptively fuses the first power generation and the second power generation based on the weather type, which can give full play to the advantages of the two methods, comprehensively consider the physical principles and actual operating conditions, and obtain a more accurate and reliable power generation prediction value.

[0027] It can be concluded from the above that this embodiment firstly introduces a physical model to predict the power generation, so that the power generation prediction of the solar power generation equipment has a physical basis and is more explainable; Secondly, the disclosed embodiment performs two predictions, namely: the first power generation prediction based on the physical model and the second power generation prediction based on the deep learning model, on which the prediction results of the two predictions are integrated. In this way, the prediction process based on the physical model can effectively make up for the defect of "the lack of adaptability of the deep learning model when facing new scenes, abnormal weather or changes in equipment status", and can also make up for the defect of "the low prediction accuracy of the deep learning model when lacking high-quality data", thereby effectively solving the problems of the prior art.

[0028] In addition, it should be pointed out that the embodiment of the present disclosure does not simply merge the results of the two predictions, but dynamically merges them based on the weather type, thereby further improving the applicability of power generation prediction to actual application scenarios, effectively ensuring the accuracy and reliability of solar power generation prediction, and facilitating accurate grid scheduling and energy management.

[0029] Please refer to Figure 2 In one embodiment of the present disclosure, the physical model is used to determine the irradiance of the target area based on the spatiotemporal parameters and the atmospheric optical characteristic parameters, and to determine the first power generation power based on the irradiance and the photoelectric conversion parameters; Before predicting the first power generation power of the solar power generation device using the physical model based on the physical parameters of the target area, the method further includes: Based on the error between the first generated power predicted by the physical model on the first historical date and the corresponding actual generated power, the atmospheric optical characteristic parameters and the photoelectric conversion parameters in the physical model are optimized.

[0030] In this embodiment, the spatiotemporal parameters may include timestamp, longitude information and latitude information, and the atmospheric optical characteristic parameters may be atmospheric transmittance. The position of the sun at a specific moment may be accurately calculated based on the timestamp information, the longitude information may reflect the time of solar radiation, the latitude information may reflect the radiation characteristics of the sun, and the atmospheric transmittance may reflect the proportion or degree of solar radiation passing through the atmosphere and reaching the ground. Therefore, this embodiment may obtain the irradiance of the target area based on the timestamp, longitude information, latitude information and atmospheric optical characteristic parameters using the movement laws of the solar celestial body.

[0031] In this embodiment, on the basis of obtaining the irradiance of the target area, the photoelectric conversion parameter can characterize the conversion relationship between the irradiance and the generated power. Therefore, the corresponding first generated power can be obtained based on the irradiance and the photoelectric conversion parameter.

[0032] This embodiment first calculates the irradiance of the target area based on the timestamp information, longitude information, latitude information and atmospheric transmittance of the target area, and then obtains the first power generation power based on the irradiance and the photoelectric conversion parameters. The above calculation process does not require the input of a large amount of meteorological data (such as cloud cover, temperature, humidity, etc.), the calculation amount is small, and the timestamp information, longitude information and latitude information are relatively fixed, and the calculation process is not easily affected by external factors.

[0033] At the same time, considering the influence of factors such as seasonal changes and aging of power generation equipment (such as aging of photovoltaic modules and dust accumulation), the prediction result of the first power generation may be inaccurate.

[0034] To solve the above problem, this embodiment can regularly obtain historical data of the physical model, such as the first power generation predicted by the physical model on the first historical date and the corresponding actual power generation, and adjust the atmospheric optical characteristic parameters and the photoelectric conversion parameters to achieve the optimal value with the goal of minimizing the error between the first power generation and the corresponding actual power generation. The first power generation is predicted based on the optimized atmospheric optical characteristic parameters and photoelectric conversion parameters, which can improve the accuracy of the prediction of the first power generation. Specifically, the calculation process of the irradiance and the first power generation is shown in the following first formula:

[0035]

[0036] in, represents the solar constant (1361 W / m²), represents the zenith angle, represents the atmospheric transmittance (a value between 0 and 1), represents the photoelectric conversion parameter, represents irradiance, represents the predicted value of the first power generation, ” represents scalar multiplication.

[0037] According to the first formula, the predicted value of the first power generation can be obtained by using the physical parameters such as the zenith angle, atmospheric transmittance and irradiance of the first historical date. On this basis, an error function can be constructed based on the predicted value of the first power generation and the corresponding actual value. With the goal of minimizing the error function, the error function is adjusted. and , finally determined and The optimal value of , the error function can adopt the root mean square error function, as shown in the second formula below: .

[0038] in, represents the actual value of the first generated power on the i-th date, It represents the predicted value of the first generated power on the i-th date.

[0039] It can be concluded from the above that this embodiment constructs an error function based on the predicted value of the first power generation power and the corresponding actual value, and optimizes the atmospheric optical characteristic parameters and photoelectric conversion parameters with the goal of minimizing the error function, so that the physical model can more accurately reflect the mapping relationship between the physical parameters of the target area and the solar power generation power, thereby improving the accuracy and reliability of the prediction.

[0040] In one embodiment of the present disclosure, the solar power generation power prediction method further includes: Based on the correlation between the irradiance and the actual power generation value corresponding to the multiple historical dates, the multiple historical dates are screened to obtain a first historical date; the irradiance corresponding to the multiple historical dates is the irradiance of the target area on the multiple historical dates obtained based on the physical model, and the actual power generation value is the actual power generation value of the solar power generation equipment in the target area on the multiple historical dates; Multiple historical dates are the N dates before the predicted date.

[0041] In this embodiment, a rolling training strategy can be adopted to optimize the atmospheric optical characteristic parameters and the photoelectric conversion parameters based on the historical data of the N dates before the prediction date (that is, the most recent historical data). Since the most recent historical data can more accurately reflect the current atmospheric environmental characteristics and the latest performance status of the photoelectric equipment, the accuracy of the first power generation prediction can be improved by optimizing the atmospheric optical characteristic parameters and the photoelectric conversion parameters based on the historical data of the N dates before the prediction date.

[0042] Furthermore, this embodiment selects a date with a higher correlation from the first N dates of the predicted date based on the correlation between the irradiance and the actual value of the generated power corresponding to multiple historical dates, that is, the first historical date. The correlation corresponding to the first historical date is higher, indicating that the irradiance obtained based on the physical model on these dates can be better mapped to the actual generated power output. Therefore, the first historical date can be called a high-quality day.

[0043] Among them, the correlation between the irradiance and the actual value of power generation corresponding to multiple historical dates can be obtained by calculating the correlation coefficient between the two. Taking the Pearson correlation coefficient as an example, the specific calculation formula of the correlation is:

[0044] in, represents the Pearson correlation coefficient, n represents the n time points of any historical date, represents the irradiance of the target area at the i-th time point, represents the average irradiance of the target area on any historical date. represents the actual value of the power generated by the solar power generation equipment at the i-th time point, It represents the average value of the power generated by the solar power generation equipment on any historical date.

[0045] In this embodiment, a first threshold may be preset, and a date with a correlation greater than the first threshold is determined as the first historical date. For example, the first threshold may be set to 0.95, 0.98, 0.99, etc.

[0046] On the basis of obtaining the first historical date, the physical model is retrained based on the physical parameters of the first historical date, the historical predicted values ​​and the corresponding actual values, so that the atmospheric optical characteristic parameters and the photoelectric conversion parameters of the physical model can be dynamically optimized.

[0047] It can be concluded from the above that this embodiment adopts a rolling training strategy and dynamically optimizes the atmospheric optical characteristic parameters and photoelectric conversion parameters of the physical model, which can enhance the model's adaptability to seasonal changes and equipment aging, and improve the stability and reliability of the prediction. At the same time, by selecting the first historical date with high relevance, the model can effectively learn from a small amount of high-quality data, further reducing the amount of calculation in the dynamic optimization process and improving the calculation accuracy.

[0048] In one embodiment of the present disclosure, determining the irradiance of a target area based on timestamp information, longitude information, latitude information, and atmospheric transmittance includes: Determine the solar declination angle and solar hour angle of the target area based on the timestamp information, longitude information and latitude information of the target area; Determine the zenith angle of the target area based on the latitude information of the target area, the solar declination angle and the solar hour angle; The irradiance of the target area is determined based on the zenith angle and atmospheric transmittance of the target area.

[0049] In this embodiment, the calculation process of the target area irradiance is: (1) Solar time calculation: Convert standard time to solar time, taking into account the longitude correction and the time difference equation:

[0050] The standard time is the time obtained according to the timestamp information, specifically to the hour, such as 12 o'clock, 1 o'clock, etc. The standard longitude is a preset constant. is the time difference equation, calculated by:

[0051]

[0052] The day number refers to the number of days in a year starting from January 1. For example, the day number of January 1 is 1, and the day number of January 2 is 2.

[0053] Based on the solar time, the solar hour angle can be obtained based on the solar time:

[0054] in, is the solar hour angle, Indicates solar time in hours.

[0055] (2) Zenith angle calculation: The zenith angle represents the angle between the sun's rays and the ground normal, and is a key parameter for irradiance calculation:

[0056] in, is the latitude information, is the solar declination angle, is the solar hour angle. The solar declination angle is calculated by the following formula:

[0057] (3) Irradiance calculation: The theoretical irradiance is calculated based on the zenith angle and atmospheric transmittance:

[0058] in, Represents irradiance, specifically theoretical irradiance, is the solar constant (1361 W / m²), is the atmospheric transmittance (a value between 0-1).

[0059] In one embodiment of the present disclosure, the solar power generation power prediction method further includes: The weather type of the target area is determined based on the cloud cover and radiation intensity of the target area.

[0060] In this embodiment, the current weather type can be identified through meteorological data. Specifically, according to the difference in cloud cover and solar radiation intensity, the weather type can be divided into three categories: the first weather type can be sunny, the second weather type can be cloudy, and the third weather type can be overcast. The model is expressed as:

[0061] in, Indicates the weather type. is the cloud cover index, is the radiation intensity.

[0062] Specifically, the process of determining the weather type can be described as follows: Determine the weather with cloud cover less than the first cloud cover and radiation intensity greater than the first radiation intensity as the first weather type; Determine the weather where the cloud amount is between the first cloud amount and the second cloud amount and the radiation intensity is between the first radiation intensity and the second radiation intensity as the second weather type; The weather in which the cloud amount is greater than the second cloud amount and the radiation intensity is less than the second radiation intensity is determined as the third weather type.

[0063] Among them, the first cloud amount, the second cloud amount, the first radiation intensity, and the second radiation intensity are all preset constants, and those skilled in the art can design their specific values ​​according to actual needs.

[0064] Please refer to Figure 3 In one embodiment of the present disclosure, the first power generation and the second power generation are integrated based on the weather type of the target area, including: Determine the corresponding fusion parameters based on the weather type of the target area; fusing the first generated power and the second generated power based on the fusion parameter; Among them, under the first weather type, the fusion parameter of the first power generation is greater than the fusion parameter of the second power generation; the first weather type refers to a weather type in which the cloud cover is less than the first cloud cover and the radiation intensity is greater than the first radiation intensity.

[0065] In this embodiment, an independent linear fusion layer (which can be implemented using PyTorch's Linear layer) can be configured for each weather type to form an expert system architecture:

[0066] in, represents the final predicted value of generated power, i.e., the third generated power; It means sunny day. Indicates cloudy. Indicates cloudy days. , , are the weight matrices corresponding to the weather types, , , is the corresponding bias term, represents the prediction result of the physical model, represents the prediction result of the deep learning model, Indicates that the prediction results of the physical model and the deep learning model are concatenated into a vector. ” represents matrix multiplication.

[0067] By setting the corresponding weight matrix and bias term, we can achieve: (1) Under sunny conditions, the linear layer can learn to give higher weights to the physical model; (2) Under cloudy and overcast conditions, the linear layer can learn the optimal fusion ratio.

[0068] Among them, the linear fusion layer parameters of each weather type are obtained by training the historical data of the corresponding weather type:

[0069] in, is the weight matrix corresponding to the weather type, is the corresponding bias term, is the number of samples corresponding to the weather type, represents the final predicted value, that is, the third power generated by combining the first power generation and the second power generation. Indicates the actual value of the generated power.

[0070] In this embodiment, in the prediction stage, the current weather type can be identified, and then the corresponding linear fusion layer model is selected, and the prediction results of the physical model and the deep learning model are input into the linear fusion layer model to obtain the final prediction value.

[0071] This multi-model fusion method based on weather classification has the following advantages: (1) Advantages of expert system: A specially optimized fusion model is used for each weather type; (2) Strong model adaptability: Automatically select the optimal fusion strategy under different weather conditions; (3) High prediction stability: avoids the performance degradation of a single model under unsuitable conditions; (4) Simple and efficient implementation: The linear layer is implemented based on PyTorch with low computational overhead.

[0072] like Figure 4 As shown, in one embodiment of the present disclosure, the deep learning model receives historical power generation data and meteorological data as input, and finally generates a deep learning prediction value through three main links: feature extraction, time series modeling, and prediction output. The specific implementation process of each link is described in detail below: (1) Input feature fusion The input of a deep learning model consists of two types of data: (1.1) Historical power generation data: The historical power generation sequence containing time pattern information is expressed as:

[0073] (1.2) Meteorological data: including key meteorological factors that affect power generation, such as temperature, humidity, and cloud cover. These heterogeneous data are integrated through the feature fusion layer to form a unified input tensor:

[0074] in, Is the length The historical power generation series, The present and the future Weather forecast data for each time step.

[0075] (2) Feature extraction network Feature extraction uses a one-dimensional convolutional neural network with the following structure: (2.1) One-dimensional convolutional layer: extract features from the input sequence and map the local pattern of the time dimension to the feature space:

[0076] in, represents the convolution operation, and are the convolution kernel and bias.

[0077] (2.2) ReLU activation function: introduces nonlinear transformation to enhance the model's expressiveness:

[0078] (2.3) Maximum pooling layer: Reduces the dimension and extracts the most significant features, reducing the amount of computation for subsequent processing:

[0079] The advantage of convolutional networks is that they can automatically extract local temporal features and capture short-term patterns and trends in power generation series.

[0080] (3) Time Series Modeling Network Time series modeling uses LSTM network combined with attention mechanism: (3.1) LSTM layer: Capturing long-term dependencies of time series:

[0081] in, is the hidden state, are unit states that together encode the temporal information of the sequence.

[0082] (3.2) Attention mechanism: Dynamically weight the importance of different time steps:

[0083]

[0084]

[0085] in, It is The attention weights for time steps, is the context vector, which represents the weighted sequence representation.

[0086] The advantage of the LSTM network is that it can capture long-term dependencies, while the attention mechanism enhances the model's perception of key time steps, making the prediction more accurate.

[0087] (3) Prediction output network The prediction output network consists of fully connected layers: (3.1) Fully connected layer: maps the context vector to the prediction space:

[0088] in, It’s the future The power generation forecast value for each time step.

[0089] (4) Model training and optimization The present invention adopts an end-to-end training method and uses a back-propagation algorithm to optimize model parameters: Loss function: Mean square error (MSE) is used as the main loss function:

[0090] Optimizer: Use Adam optimizer to adaptively adjust the learning rate:

[0091] in, are model parameters, is the learning rate, and are the first and second moment estimates.

[0092] Regularization: Implicit regularization is achieved through soft weight assignment of the attention mechanism to reduce the risk of overfitting.

[0093] The predicted values ​​output by the deep learning model will be combined with the predicted values ​​of the physical model through a linear fusion layer to form the final solar power prediction result.

[0094] In summary, the solar power generation prediction method proposed in this embodiment achieves technical breakthroughs and application values ​​in many aspects by integrating physical models and deep learning models, and has the following significant beneficial effects: (1) Improved prediction accuracy This invention adopts a hybrid architecture of "physical model + deep learning", which significantly improves the prediction accuracy compared with the traditional single method: Reduced root mean square error (RMSE): The physical model and deep learning network make independent predictions, and the two prediction results are intelligently combined through the linear fusion layer to make the overall prediction closer to the actual power generation curve.

[0095] Mean Absolute Percent Error (MAPE) reduction: Especially during transition periods such as sunrise and sunset, the prediction accuracy is significantly better than traditional methods.

[0096] Various evaluation indicators have been improved: the accuracy of a certain energy bureau, the southern region, and the national standard CR and QR indicators have all improved.

[0097] (2) Enhanced prediction stability and reliability The present invention significantly enhances the stability and reliability of prediction through physical constraints and high-quality day identification mechanism: Abnormal predictions are greatly reduced: The celestial motion constraints provided by the physical model effectively prevent overfitting and abnormal predictions of deep learning models.

[0098] Reduced forecast volatility: Compared with pure data-driven methods, the volatility and randomness of the forecast results are significantly reduced, which is more in line with the actual power generation laws.

[0099] Enhanced adaptability to extreme weather: Under extreme weather conditions such as drastic changes in cloud cover and abnormal temperatures, the prediction performance is better than traditional methods.

[0100] (3) Computing efficiency and resource consumption optimization The present invention optimizes the calculation process and improves the prediction efficiency: The calculation time is significantly shortened: the physical model calculation is simple and efficient, the deep learning network and the physical model are calculated in parallel, and the overall prediction process is efficient and coordinated.

[0101] Reduced training data requirements: The high-quality day recognition mechanism enables the model to effectively learn from a small amount of high-quality data, reducing the reliance on large-scale training data.

[0102] Reasonable optimization of model parameters: The deep learning module focuses on learning complex patterns from data, and the physical model processes basic laws. The two have clear division of labor, and the overall system parameters are reasonably optimized.

[0103] (4) Improved explainability and traceability The hybrid architecture of the present invention significantly improves the interpretability of prediction results: Transparency of prediction process: The prediction results can be decomposed into the contribution of the physical model and the contribution of the deep learning model. The weight distribution of the linear fusion layer clearly shows the influence of each model, which facilitates the analysis of the source of prediction deviation.

[0104] The physical meaning of the parameters is clear: the key parameters of the physical model (such as atmospheric transmittance , Photoelectric conversion parameters ) has a clear physical meaning, which is easy for engineers to understand and adjust.

[0105] Enhanced abnormality diagnosis capability: By comparing the deviation pattern between physical predictions and actual values, problems such as PV system failures and sensor abnormalities can be effectively identified.

[0106] (5) Enhanced adaptability and generalization capabilities The present invention has excellent adaptability and generalization ability: Strong geographical adaptability: The physical model naturally adapts to the laws of the sun's motion in different geographical locations and can be applied to new locations without retraining.

[0107] Seasonal adaptation: The rolling training strategy enables the model to automatically adapt to changes in power generation characteristics caused by seasonal changes.

[0108] Equipment aging adaptation: By dynamically optimizing mapping parameters, the model can adapt to long-term changing factors such as PV module aging and dust accumulation.

[0109] Strong cross-site generalization capability: In the case of newly built power stations or scenarios with limited data, this method can still provide reliable predictions, and its generalization performance is better than that of pure data-driven methods.

[0110] (6) Improved engineering practicality The present invention has significant engineering practical value: Low deployment threshold: The physical model part only requires basic geographic information (including longitude and latitude information) to run, and the deep learning part can be flexibly configured according to data availability.

[0111] Low maintenance cost: The model structure is clear and the physical meaning of the parameters is clear, which is convenient for engineers to maintain and optimize.

[0112] Strong scalability: The framework is modular in design and can easily integrate new meteorological data sources or prediction algorithms.

[0113] Wide range of application scenarios: It is suitable for photovoltaic power stations of different sizes and types, from distributed rooftop photovoltaics to large ground power stations.

[0114] In summary, the present invention has achieved remarkable breakthroughs in prediction accuracy, stability, computational efficiency, interpretability, adaptability and engineering practicality by integrating the physical constraints of celestial body motion with deep learning technology. It provides an innovative solution for solar power generation prediction technology, which is of great value to promoting the efficient utilization of new energy and the safe and stable operation of power grids.

[0115] Corresponding to the solar power generation power prediction method of the above embodiment, Figure 5 This is a structural block diagram of a solar power generation prediction device provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 5 The solar power generation power prediction device 20 includes: a first prediction module 21, a second prediction module 22 and a data fusion module 23. The first prediction module 21 is used to predict the first power generation of the solar power generation equipment by using a physical model based on the physical parameters of the target area; the physical model is a mapping model of physical parameters and power generation constructed based on the movement law of the solar celestial body and the mechanics of photovoltaic power generation, and the target area is the area where the solar power generation equipment is located; A second prediction module 22 is used to predict the second power generation of the solar power generation equipment by using a deep learning model based on the meteorological data of the target area and the historical power generation data of the solar power generation equipment; The data fusion module 23 is used to fuse the first power generation power and the second power generation power based on the weather type of the target area to obtain the third power generation power of the solar power generation equipment.

[0116] In one embodiment of the present disclosure, the physical parameters of the target area include time and space parameters, atmospheric optical characteristic parameters and photoelectric conversion parameters. The physical model is used to determine the irradiance of the target area based on the spatiotemporal parameters and the atmospheric optical characteristic parameters, and to determine the first power generation based on the irradiance and the photoelectric conversion parameters; Before predicting the first power generation power of the solar power generation equipment using the physical model based on the physical parameters of the target area, the first prediction module 21 is specifically used to: Based on the error between the first generated power predicted by the physical model on the first historical date and the corresponding actual generated power, the atmospheric optical characteristic parameters and the photoelectric conversion parameters in the physical model are optimized.

[0117] In one embodiment of the present disclosure, the first prediction module 21 is further configured to: Based on the correlation between the irradiance and the actual power generation value corresponding to the multiple historical dates, the multiple historical dates are screened to obtain a first historical date; the irradiance corresponding to the multiple historical dates is the irradiance of the target area on the multiple historical dates obtained based on the physical model, and the actual power generation value is the actual power generation value of the solar power generation equipment in the target area on the multiple historical dates; Multiple historical dates are the N dates before the predicted date.

[0118] In one embodiment of the present disclosure, the spatiotemporal parameters include timestamp information, longitude information and latitude information of the target area, the atmospheric optical characteristic parameters include atmospheric transmittance, and the first prediction module 21 is specifically further used for: The irradiance of the target area is determined based on timestamp information, longitude information, latitude information, and atmospheric transmittance.

[0119] In one embodiment of the present disclosure, the first prediction module 21 is further configured to: Determine the solar declination angle and solar hour angle of the target area based on the timestamp information, longitude information and latitude information of the target area; Determine the zenith angle of the target area based on the latitude information of the target area, the solar declination angle and the solar hour angle; The irradiance of the target area is determined based on the zenith angle and atmospheric transmittance of the target area.

[0120] In one embodiment of the present disclosure, the data fusion module 23 is specifically used to: The weather type of the target area is determined based on the cloud cover and radiation intensity of the target area.

[0121] In one embodiment of the present disclosure, the data fusion module 23 is specifically used to: Determine the corresponding fusion parameters based on the weather type of the target area; fusing the first generated power and the second generated power based on the fusion parameter; Among them, under the first weather type, the fusion parameter of the first power generation is greater than the fusion parameter of the second power generation; the first weather type refers to a weather type in which the cloud cover is less than the first cloud cover and the radiation intensity is greater than the first radiation intensity.

[0122] See also Figure 6 , Figure 6 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 6 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 5 The functions of the first prediction module 21, the second prediction module 22 and the data fusion module 23 are shown.

[0123] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0124] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0125] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0126] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the solar power generation power prediction method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0127] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0128] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0129] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0133] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0134] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for predicting solar power generation, characterized in that: include: Based on the physical parameters of the target area, a physical model is used to predict the first power generation power of the solar power generation equipment; the physical model is a mapping model of physical parameters and power generation power constructed based on the movement law of the solar celestial body and the mechanics of photovoltaic power generation, and the target area is the area where the solar power generation equipment is located; Based on the meteorological data of the target area and the historical power generation data of the solar power generation equipment, using a deep learning model to predict the second power generation power of the solar power generation equipment; The first power generation power and the second power generation power are merged based on the weather type of the target area to obtain the third power generation power of the solar power generation equipment.

2. The solar power generation power prediction method according to claim 1, characterized in that: The physical parameters of the target area include temporal and spatial parameters, atmospheric optical characteristic parameters and photoelectric conversion parameters. The physical model is used to determine the irradiance of the target area based on the spatiotemporal parameters and the atmospheric optical characteristic parameters, and to determine the first power generation based on the irradiance and the photoelectric conversion parameters; Before predicting the first power generation power of the solar power generation equipment by using the physical model based on the physical parameters of the target area, the method further includes: Based on the error between the first generated power predicted by the physical model on the first historical date and the corresponding actual generated power, the atmospheric optical characteristic parameters and the photoelectric conversion parameters in the physical model are optimized.

3. The solar power generation power prediction method according to claim 2, characterized in that: Also includes: Based on the correlation between the irradiance corresponding to the multiple historical dates and the actual value of the generated power, multiple historical dates are screened to obtain the first historical date; the irradiance corresponding to the multiple historical dates is the irradiance of the target area on the multiple historical dates obtained based on the physical model, and the actual value of the generated power is the actual value of the generated power of the solar power generation equipment in the target area on the multiple historical dates; The multiple historical dates are the N dates before the predicted date.

4. The solar power generation power prediction method according to claim 2, characterized in that: The spatiotemporal parameters include timestamp information, longitude information and latitude information of the target area, and the atmospheric optical characteristic parameters include atmospheric transmittance; the physical model is specifically used for: The irradiance of the target area is determined based on the timestamp information, the longitude information, the latitude information, and the atmospheric transmittance.

5. The solar power generation power prediction method according to claim 4, characterized in that: The determining the irradiance of the target area based on the timestamp information, the longitude information, the latitude information, and the atmospheric transmittance includes: Determine the solar declination angle and solar hour angle of the target area based on the timestamp information, longitude information and latitude information of the target area; Determine the zenith angle of the target area based on the latitude information of the target area, the solar declination angle and the solar hour angle; The irradiance of the target area is determined based on the zenith angle and the atmospheric transmittance of the target area.

6. The solar power generation power prediction method according to claim 1, characterized in that: Also includes: The weather type of the target area is determined based on the cloud cover and radiation intensity of the target area.

7. The solar power generation power prediction method according to claim 1, characterized in that: The fusing the first power generation and the second power generation based on the weather type of the target area includes: Determining corresponding fusion parameters based on the weather type of the target area; fusing the first generated power and the second generated power based on the fusion parameter; Among them, under the first weather type, the fusion parameter of the first power generation is greater than the fusion parameter of the second power generation; the first weather type refers to the weather type in which the cloud cover is less than the first cloud cover and the radiation intensity is greater than the first radiation intensity.

8. A solar power generation power prediction device, characterized in that: include: A first prediction module is used to predict a first power generation power of the solar power generation device using a physical model based on physical parameters of the target area; the physical model is a mapping model of physical parameters and power generation power constructed based on the movement law of the solar celestial body and the mechanics of photovoltaic power generation, and the target area is the area where the solar power generation device is located; A second prediction module is used to predict the second power generation power of the solar power generation equipment by using a deep learning model based on the meteorological data of the target area and the historical power generation data of the solar power generation equipment; The data fusion module is used to fuse the first power generation and the second power generation based on the weather type of the target area to obtain the third power generation of the solar power generation equipment.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images