NL2033884B1 - A Photovoltaic Power Prediction Method, System and Medium - Google Patents

A Photovoltaic Power Prediction Method, System and Medium Download PDF

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NL2033884B1
NL2033884B1 NL2033884A NL2033884A NL2033884B1 NL 2033884 B1 NL2033884 B1 NL 2033884B1 NL 2033884 A NL2033884 A NL 2033884A NL 2033884 A NL2033884 A NL 2033884A NL 2033884 B1 NL2033884 B1 NL 2033884B1
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data
prediction
model
photovoltaic power
feature
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NL2033884A
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NL2033884A (en
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Sun Jian
Han Zifen
Sun Jianhua
Zhang Xu
He Chang'an
Wang Dingmei
Feng Yan
Zhang Yuanfeng
Fu Jiayu
Wang Lu
Zhang Peng
Guo Taibei
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Gansu Tongxing Intelligent Tech Development Co Ltd
State Grid Gansu Electric Power Co
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks

Abstract

The invention belongs to the technical field of photovoltaic power generation prediction and discloses a photovoltaic power prediction method, system and medium. The driving numerical forecast model is determined based on atmospheric reanalysis meteorological data and global weather prediction field. After preprocessing meteorological data, feature selection is carried out. The prediction method based on LSTM-CNN and the regression prediction method based on Lasso are combined with the field observation data to train the model and finally output the combined prediction results. The present invention takes the weather prediction result of ERA5 driven numerical weather prediction model as the input of model learning for model training. When the weather prediction results of ECMWF driven numerical weather prediction model are input into a trained model and verified, the results are more satisfactory than those of a single model. The invention combines the advantages of linear and nonlinear prediction models, improves the generalization ability of prediction models, and improves the accuracy and stability of short-term power prediction of photovoltaic power generation.

Description

State Grid Gansu Electric Power Company, and
Gansu Tongxing Intelligent Technology Development CO., LTD. 22/112 PDNL
A Photovoltaic Power Prediction Method, System and Medium
Technical Field
The invention belongs to the technical field of photovoltaic power generation prediction, in particular to a photovoltaic power prediction method, system and medium.
Background Technology
As solar energy has the characteristics of large reserves of resources, clean, sustainable and direct access, photovoltaic power generation is an effective way to realize the transformation of sustainable energy structure and reduce carbon emissions in the process of power generation. The actual solar radiation received by photovoltaic system has the characteristics of large fluctuation range and strong uncertainty, which leads to the obvious random fluctuation and intermittent characteristics of its output power. With the rapid development of photovoltaic power generation, its high permeability also brings many new challenges to the operation of the existing power grid system. Improving the accuracy of photovoltaic power prediction is an effective solution to overcome these challenges.
Short-term PV power prediction algorithms mostly take Numerical Weather Prediction (NWP) as the input of meteorological data, and build the mapping model between NWP and
PV output by multiple regression or traditional machine learning methods. However, because of the uncertainty of NWP, The prediction error has a direct influence on the accuracy of power prediction. The model established by this kind of method can not accurately describe the relationship between weather and power.
Through the above analysis, the existing problems and defects of the existing technology are as follows: (1) The accuracy of photovoltaic power prediction model based on traditional methods is affected by too many features. (2) The traditional photovoltaic power prediction method only considers the mapping relationship between numerical weather prediction data and photovoltaic output data, and the simple time analysis ignores the non-stationality of spatio-temporal data. (3) Classical machine learning methods tend to fall into local optimal and overfitting states in the process of model training, so it is difficult for traditional photovoltaic power prediction methods to improve the prediction accuracy of photovoltaic power.
Description of the Invention
Aiming at the problems existing in the prior art, the invention provides a photovoltaic power prediction method, system and medium, in particular a photovoltaic power prediction method, system and medium based on atmospheric reanalysis data.
The invention relates to a photovoltaic power prediction method. The photovoltaic power prediction method includes: determining the driving numerical prediction model according to atmospheric reanalysis meteorological data and global weather prediction field; After preprocessing meteorological data, feature selection is carried out. The prediction method based on LSTM-CNN and the regression prediction method based on Lasso are combined with the field observation data to train the model and finally output the combined prediction results.
Furthermore, the photovoltaic power prediction method includes the following steps:
Step 1: Download the atmospheric reanalysis meteorological data and global numerical weather forecast data of the target area, covering the historical operation time of the photovoltaic power station;
Step 2: Atmospheric reanalysis meteorological data and global numerical weather forecast data are used to drive the mesoscale numerical weather forecast model to conduct downscale forecast respectively.
Step 3: Obtain power generation data of photovoltaic power station and remove outliers as the target value of model training;
Step 4: Extract the elements of PV station meteorological prediction from the downscaling meteorological data of reanalysis as the training set of PV power prediction model;
Step 5: Extract the same meteorological prediction elements of photovoltaic power station from downscaling meteorological data forecast, and use them as the test set of photovoltaic power prediction model.
Step 6: Extract the features and add them to the feature sets of the training set and the test set respectively, The feature information of PV power prediction was screened from the feature set of training set by random forest.
Step 7: The feature information set of the selected training set is used as the input of the LSTM-CNN and Lasso models respectively to conduct model training and cross- validation, and obtain the well-trained LSTM-CNN and Lasso models.
Step 8: The feature information of the test set is matched according to the filtered training set set. The trained LSTM-CNN model and Lasso model are called on the test set to obtain two groups of initial prediction results, respectively. The Stacking fusion strategy is used to fuse the two prediction results and obtain the final prediction results.
Furthermore, in Step 2, the same parameterization scheme and spatial resolution of model time were adopted for the numerical model to obtain two sets of refined target range meteorological forecast data, namely reanalysis downscaling meteorological data and downscaling meteorological forecast data respectively.
Furthermore, the meteorological prediction factors of photovoltaic power station in Step 4 include total solar level radiation, direct solar radiation, solar scattered radiation, low, middle and high cloud cover, wind speed, wind direction, temperature, humidity and pressure.
Furthermore, feature extraction in Step 6 includes: (1) Convert the direct and scattered radiation of the ground to the total solar radiation of the inclined plane according to the latitude and longitude coordinates of the photovoltaic power station and the atmospheric radiation theory; (2) The wind direction of surface wind and upper wind is characterized by wind direction cosine and wind direction sine; (3) Add labels for month, week, day and hour; (4) According to the dip angle, longitude and latitude information of the photovoltaic square, add the sine of the solar altitude angle and the cosine of the solar incidence angle feature labels.
Furthermore, in Step 6, the set of meteorological feature information was input into the random forest model as a training set to calculate the degree of influence of different feature information on the prediction results, and the optimal feature subset was selected.
When there is nee in the random forest, according to the bootstrap resampling technology of random forest, 1/3 data of each sub-decision tree is not extracted in the process of training sample extraction, which is called out-of-pocket data. The out-of-pocket data of the j'th decision tree is predicted, and the error of prediction results is calculated.
Where, the error of the prediction result is the out-of-pocket data error OOBerror; of the jth decision tree:
OOBerror; = a Zia Oe — Yip)”:
Wherein, N; is the total number of data samples outside the bag, y;- is the actual value of data samples outside the bag, and y is the predicted value of data samples outside the bag.
The out-of-pocket data error of the entire random forest is:
OOBerror = Lyre OOBerror; ;
Ntree J=1 J
The importance measure of the feature information is calculated by using the data outside the bag, so as to provide reference for selecting the feature information. For out-of- pocket data error OOBerror; recording of the j'th decision tree, change one of the feature variables x;(i=1,2,3,...,M) in the feature set X = {}, M is the total number of features, other feature variables remain unchanged, the out-of-pocket data of the feature variable is randomly scrambled, and the error OOBerrorj of the changed out-of-pocket data is recalculated. The difference of the error of the out-of-pocket data before and after the changed feature variable is taken as the importance measurement of the feature x;:
MDA = 3.tree(00Berror; — OOBerror{);
Input factors filtered by random forest algorithm include: (1) Using bootstrap sampling method, K sample sets were randomly generated in the original training data set, and the features with the most classification ability were selected from the m features for node splitting. Multiple decision trees were generated to form a random forest, and the new data were classified by tree classifier voting method. (2) The importance MDA of each characteristic variable was calculated and sorted in descending order; (3) Determine the proportion to be deleted, and delete the unimportant indicators in the corresponding proportion from the current feature variables, so as to obtain a new feature set; (4) Establish a new random forest with a new feature set, calculate the importance of each feature in the feature set, and rank; (5) Repeat Step (2) to Step (4) until m features remain; (6) According to each obtained feature set and random forest, the corresponding out- of-pocket error OOBerror is calculated, and the feature set with the lowest out-of-pocket error is taken as the final selected feature set Q.
Another purpose of the invention is to provide a photovoltaic power prediction system using the photovoltaic power prediction method, including:
Driver numerical forecast module, which is used to drive the numerical forecast model based on atmospheric reanalysis of meteorological data and global weather forecast field respectively;
Data preprocessing module, which is used for meteorological data preprocessing and then feature selection;
Model training module, which is used for the prediction method based on LSTM-CNN and the regression prediction method based on Lasso, and combines the electric field observation data to train the model, and finally outputs the combined prediction results.
Another purpose of the invention is to provide a computer device, which comprises a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the photovoltaic power prediction method. 5 Another purpose of the invention is to provide a computer-readable storage medium containing a computer program which, when executed by a processor, enables the processor to perform the steps of the photovoltaic power prediction method.
Another purpose of the invention is to provide an information data processing terminal for realizing the photovoltaic power prediction system.
Combined with the above technical scheme and solved technical problems, the advantages and positive effects of the technical scheme to be protected by the invention are as follows:
Firstly, in view of the technical problems existing in the above-mentioned prior art and the difficulty of solving the problem, closely combined with the technical scheme to be protected by the invention and the results and data in the process of research and development, detailed and profound analysis of the technical problem solved by the technical scheme of the invention, and some creative technical effects after solving the problem. Specific description is as follows:
Reanalysis of meteorological data is a kind of meteorological data obtained after quality control and fusion of numerical forecast products and atmospheric observation data (including ground observation, satellite, radar, sounding, etc.) through data assimilation method. It has the characteristics of long time series and wide spatial distribution, and is more accurate than numerical forecast. Taking it as the background driving field can improve the simulation quality of meteorological model well. The noise and uncertainty of model data can be greatly reduced by downscaling the regional numerical weather prediction model again, and the prediction accuracy of the model can be effectively improved in mining the mapping relationship between meteorological prediction factors and power fluctuations.
The reanalysis meteorological data and meteorological forecast data based on the same model have consistency in model error. Therefore, the invention can take the meteorological prediction results of ERAS driven numerical weather forecast model as the input of model learning for model training, and input the meteorological prediction results of
ECMWF driven numerical weather forecast model into the trained model. The prediction verification can effectively improve the prediction accuracy of the model.
Secondly, taking the technical scheme as a whole or from the perspective of product, the technical effect and advantages of the technical scheme to be protected by the invention are described as follows:
The invention extracts the time characteristic information of historical data through
LSTM neural network, takes the output of LSTM neural network as the input of CNN neural network, extracts the spatial characteristic information of data, and can well deal with the timing problem of photovoltaic power itself and the nonlinear relationship between various influencing factors and photovoltaic power. It has a good effect on the power prediction of photovoltaic power generation system. Then, by combining the prediction results of Lasso linear regression model and LSTM-CNN nonlinear neural network, more satisfactory results can be obtained than that of a single model. The advantages of linear and nonlinear prediction models are integrated to further improve the generalization ability of the prediction model, and improve the accuracy and stability of short-term photovoltaic power prediction.
Thirdly, as creative auxiliary evidence of the claims of the invention, it is also embodied in the following important aspects:
The expected income and commercial value after the conversion of the technical scheme of the invention are:
For the first time, the invention applies the historical reanalysis meteorological data to the short-term forecast of photovoltaic power by using the downscaling technology of meteorological model, and reduces the meteorological error. At the same time, it gives full play to the unique advantages of deep learning neural network in adapting to the processing of meteorological data and time series, and obtains the characteristic quantity from the massive input information of multiple hidden layers. After training a large amount of data to complete the model training and learning, and finally use the training and learning model to complete the photovoltaic power prediction, which has great advantages compared with other methods.
Brief Description of the Drawings
In order to more clearly explain the technical scheme of the implementation method of the invention, the following is a brief introduction of the attached drawings required in the implementation method of the invention. It is obvious that the attached drawings described below are only some implementation methods of the invention. For ordinary technical personnel in the field, other attached drawings can be obtained according to these drawings without creative labor.
Figure 1 is a flow chart of the photovoltaic power prediction method provided by the implementation method of the invention;
Figure 2 is a schematic diagram of the photovoltaic power prediction method provided by the implementation method of the invention.
Specific Implementation Methods
In order to make the purpose, technical scheme and advantages of the invention more clear, the invention is further explained in detail in combination with implementation methods. It should be understood that the specific implementation methods described herein are intended only to explain the invention and are not intended to qualify it.
Aiming at the problems existing in the prior art, the invention provides a photovoltaic power prediction method, system and medium. The invention is described in detail in combination with the attached drawings below.
Atmospheric forecast data: The Medium-Range Weather Forecasts products of the
European Centre for Medium-Range Weather Forecasts (ECMWF).
As shown in Drawing 1, the photovoltaic power prediction method provided by the implementation method of the invention includes the following steps:
S101. The numerical prediction model is driven by atmospheric reanalysis meteorological data and global weather prediction field respectively;
S102. Preprocess meteorological data and then proceed the feature selection;
S103. The prediction method based on LSTM-CNN and the regression prediction method based on Lasso, combined with the field observation data to train the model, and finally output the combined prediction results.
As a preferred implementation method, the photovoltaic power prediction method provided by the implementation method of the invention, as shown in Drawing 2, specifically includes the following steps: 1. Download the atmospheric reanalysis meteorological data and global meteorological forecast data of the target area, covering the historical operation time of the photovoltaic power station; 2. The ERAS atmospheric reanalysis data and the ECMWF atmospheric prediction data are respectively used to drive the mesoscale numerical weather prediction model to conduct downscale prediction. The numerical model adopts the same parameterization scheme and spatial resolution of the model time, and two sets of target range refined weather forecast data can be obtained. Are ERA5 downscaling meteorological data and ECMWF downscaling meteorological data, respectively. 3. Obtain power generation data of photovoltaic power station and remove outliers as the target value of model training; 4. Extract the meteorological prediction elements of photovoltaic power station from the downscaling meteorological data of ERAS, such as total solar horizontal radiation, direct solar radiation, solar scattered radiation, low, middle and high cloud cover, wind speed, wind direction, temperature, humidity, pressure, etc., as the training set of photovoltaic power prediction model; 5. In the same way as the above steps, the same meteorological prediction elements at the photovoltaic power station were extracted from the downscaling meteorological data of
ECMWF, and used as the test set of the photovoltaic power prediction model; 6. Since photovoltaic power generation is related to time, local latitude and longitude, solar radiation amount, cloud cover, wind speed and wind direction on the square array, extraction and representation of key features are very important for discovering the periodicity rule between data and improving the convergence rate of model training.
Features are extracted in the following ways and added into the feature set of training set and test set respectively: (1) The direct and scattered radiation of the ground is converted into the total solar radiation of the inclined plane according to the atmospheric radiation theory; (2) The wind direction of surface wind and upper wind is characterized by wind direction cosine and wind direction sine; (3) Add labels for month, week, day and hour; (4) According to the dip angle, longitude and latitude information of the photovoltaic square, add the sine of the solar altitude angle and the cosine of the solar incidence angle feature labels; 7. Random forest was used to screen out the feature information of photovoltaic power prediction from the feature set of the above training set. First, the above meteorological feature information set was input into the random forest model as the training set, and the influence degree of different feature information on the prediction results was calculated, and the optimal feature subset was selected based on this.
When there is ntree in the random forest, according to the bootstrap resampling technology of random forest, 1/3 data of each sub-decision tree is not extracted in the process of training sample extraction, which is called out-of-pocket data. The out-of-pocket data of the j'th decision tree is predicted, and the error of prediction results is calculated.
Where, the error of the prediction result is the out-of-pocket data error OOBerror; of the j'th decision tree;
Wherein, N; is the total number of data samples outside the bag, y;- is the actual value of data samples outside the bag, and y; is the predicted value of data samples outside the bag.
The out-of-pocket data error of the entire random forest is:
OOBerror = Lyle OOBerror; ;
Niree J=1 J
The importance measure of the feature information is calculated by using the data outside the bag, so as to provide reference for selecting the feature information. For out-of- pocket data error OOBerror; recording of the j'th decision tree, change one of the feature variables x;(i=1,2,3,...,M) in the feature set X = {}, M is the total number of features, other feature variables remain unchanged, the out-of-pocket data of the feature variable is randomly scrambled, and the error OOBerrorj of the changed out-of-pocket data is recalculated. The difference of the error of the out-of-pocket data before and after the changed feature variable is taken as the importance measurement of the feature x;:
MDA = 3.tree(00Berror; — OOBerror{);
It can be seen that if the value of a certain feature is added to the random noise data, the larger the error of the out-of-pocket data is, the more important the feature is.
Steps of input factor screening by random forest algorithm: (1) Using bootstrap sampling method, K sample sets were randomly generated in the original training data set, and a feature with the most classification ability was selected among the m features for node splitting. Multiple decision trees were generated to form a random forest, and the new data were classified by tree classifier voting method. (2) Calculate the importance MDA of each characteristic variable and sort it in descending order. (3) Determine the proportion to be deleted, and delete the unimportant indicators in the corresponding proportion from the current feature variables, so as to obtain a new feature set. (4) Establish a new random forest with a new feature set, calculate the importance of each feature in the feature set, and sort it. (5) Repeat Step (2) to Step (4) until m features remain. (6) According to each obtained feature set and random forest, the corresponding out- of-pocket error OOBerror is calculated, and the feature set with the lowest out-of-pocket error is taken as the final selected feature set Q. 8. The feature information set of the selected training set was used as the input of the
LSTM-CNN and Lasso models respectively to conduct model training and cross-validation, and the trained LSTM-CNN model and Lasso model were obtained; 9. The feature information of the test set is matched according to the filtered training set set, and the trained LSTM-CNN model and Lasso model are called on the test set to obtain two groups of initial prediction results, respectively. The fusion strategy is used to fuse the two prediction results and obtain the final prediction results.
The photovoltaic power prediction system provided by the implementation method of the invention includes:
Driver numerical forecast module, which is used to determine the driving numerical forecast model based on ERAS atmospheric reanalysis meteorological data and global weather forecast field.
Data preprocessing module, which is used for meteorological data preprocessing and then feature selection;
Model training module, which is used for the prediction method based on LSTM-CNN and the regression prediction method based on Lasso, and combines the electric field observation data to train the model, and finally outputs the combined prediction results.
In order to prove the creativity and technical value of the technical scheme of the invention, this part is an application implementation method of the technical scheme of the claim on a specific product or related technology.
In order to verify the effect of the proposed method, a photovoltaic power station in
Shanxi Province and Gansu Province were selected for testing. The installed capacity of
Shanxi power station is 60MW and that of Gansu power station is 59MW. The grid- connected data of two centralized photovoltaic power plants were obtained, and the ERAS historical reanalysis data and ECMWF global weather forecast data were used. The downscale numerical weather prediction was carried out, the training set and the test set were divided, the deep learning neural network LSTM-CNN and Lasso models were used to build the fusion prediction results, and the method was compared with the traditional prediction methods. The method provided in this patent can improve the average accuracy of the two electric fields in September 2022 by about 0.9%.
The calculation formula of power prediction accuracy is as follows:
CR = £ - cie ie y ì x100%
Vea GJ
Where, Pui is the actual generation power at i'th moment, Ppiis the predicted generation power at i'th moment, Ci is the startup capacity at i'th moment, n is the number of actual generation power collection times every 15 minutes during the daytime, i=1,2... n.
The implementation method of the invention has obtained some positive effects in the process of research and development or use, and indeed has great advantages compared with the current technology.
Traditional NWP-power | cNN.LSTM | Lasso | Fusion forecasting method model of model of | result of (Based on random forest . . . . . . : invention invention | invention algorithm)
A power station in 88.39% 89.15% 89.08% 89.30%
Shanxi
A power station in 88.84% 89.63% 89.37% 89.73%
Gansu 88.62% 89.39% 89.23% 89.52% value
What should be awared is that implementation methods of the invention may be realized by hardware, software or a combination of software and hardware. The hardware part can be realized by using special logic; The software part can be stored in memory and executed by the appropriate instruction system. Such as a microprocessor or specially designed hardware to perform. The general technical person in the field can understand that the above devices and methods can be implemented using computer executable instructions and/or contained in processor control code. For example, such code is provided on a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device and its module of the invention may be realized by the hardware circuit of a programmable hardware device such as a very large scale integrated circuit or gate array, a semiconductor such as a logic chip or a transistor, or a field programmable gate array or a programmable logic device. It can also be implemented by software executed by various types of processors, or by a combination of the above hardware circuits and software, such as firmwaree.
The above mentioned is only the specific mode of implementation of the invention, but the scope of protection of the invention is not limited to this, and any modification, equivalent substitution and improvement made by any technical person familiar with the technical field within the scope of the technology disclosed by the invention and within the spirit and principles of the invention shall be covered by the scope of protection of the invention.

Claims (8)

ConclusiesConclusions 1. Voorspellingsmethode voor fotovoltaisch vermogen, met het kenmerk, dat de methode omvat: het bepalen van het sturende numerieke voorspellingsmodel volgens de atmos- ferische heranalyse van meteorologische gegevens en het globale weersvoorspellingsveld, waarbij na voorbewerking van de meteorologische gegevens de selectie van functies wordt uitgevoerd, waarbij de voorspellingsmethode op basis van LSTM-CNN en de Lasso- regressievoorspellingsmethode worden gecombineerd met de veldobservatiegegevens om het model te trainen en uiteindelijk de gecombineerde voorspellingsresultaten uit te voeren.1. Prediction method for photovoltaic power, characterized in that the method includes: determining the driving numerical prediction model according to the atmospheric reanalysis of meteorological data and the global weather forecast field, whereby the selection of functions is carried out after pre-processing of the meteorological data , which combines the prediction method based on LSTM-CNN and the Lasso regression prediction method with the field observation data to train the model and finally output the combined prediction results. 2. Voorspellingsmethode voor fotovoltaisch vermogen volgens conclusie 1, met het kenmerk, dat de methode de volgende stappen omvat: Stap 1: Download de ERAS atmosferische heranalyse van meteorologische gegevens en ECMWF atmosferische voorspellingsgegevens in het doelgebied, waarbij het tijdsbereik de historische bedrijfsuren van de fotovoltaïsche energiecentrale omvat. Stap 2: Gebruik ERAS atmosferische heranalyse van meteorologische gegevens en ECMWF atmosferische voorspellingsgegevens om het numerieke weersvoorspellingsmodel op mesoschaal aan te sturen voor downscaling voorspelling. Stap 3: Verkrijg de stroomopwekkingsgegevens van de fotovoltaïsche energie- centrale, verwijder uitschieters en gebruik deze als doelwaarde voor modeltraining; Stap 4: Extraheer de meteorologische voorspellingselementen op de locatie van de fotovoltaïsche energiecentrale uit de ERA5-downscaling van meteorologische gegevens als trainingsset van het fotovoltaïsche energievoorspellingsmodel; Stap 5: Extraheer dezelfde meteorologische voorspellingselementen op de locatie van de fotovoltaïsche energiecentrale uit de ECMWF-downscaling van meteorologische gegevens en gebruik deze als testset van het fotovoltaïsche vermogensvoorspellingsmodel. Stap 6: Extraheer de functies en voeg deze toe aan de functiesets van respectievelijk de trainingsset en de testset, waarbij de functie-informatie van fotovoltaïsche vermogens- voorspelling uit de functieset van de trainingsset werd gefilterd door middel van willekeurig bos (random forest). Stap 7: Gebruik de functie-informatieset van de gefilterde trainingsset als de invoer van respectievelijk de LSTM-CNN- en LASSO-modellen om rasterzoekopdrachten uit te voeren en de parameters aan te passen om de modellen te trainen en de getrainde LSTM- CNN- en LASSO-modellen te verkrijgen. Stap 8: Koppel de functie-informatie van de testset aan de gefilterde trainingsset, roep het getrainde LSTM-CNN-model en het Lasso-model op de testset aan om respectievelijk twee groepen initiële voorspellingsresultaten te verkrijgen en gebruik de Stacking-fusie-2. Photovoltaic power forecasting method according to claim 1, characterized in that the method comprises the following steps: Step 1: Download the ERAS atmospheric reanalysis of meteorological data and ECMWF atmospheric forecast data in the target area, where the time range is the historical operating hours of the photovoltaic power plant. Step 2: Use ERAS atmospheric reanalysis meteorological data and ECMWF atmospheric forecast data to drive the mesoscale numerical weather prediction model for downscaling forecast. Step 3: Obtain the power generation data from the photovoltaic power plant, remove outliers and use it as a target value for model training; Step 4: Extract the meteorological forecast elements at the photovoltaic power plant site from the ERA5 downscaling of meteorological data as a training set of the photovoltaic energy forecast model; Step 5: Extract the same meteorological forecast elements at the photovoltaic power plant site from the ECMWF downscaling of meteorological data and use them as the test set of the photovoltaic power forecast model. Step 6: Extract the features and add them to the feature sets of the training set and the test set, respectively, where the feature information of photovoltaic power prediction was filtered from the feature set of the training set by random forest. Step 7: Use the feature information set of the filtered training set as the input of the LSTM-CNN and LASSO models, respectively, to perform raster searches and adjust the parameters to train the models and the trained LSTM-CNN and LASSO models available. Step 8: Match the feature information of the test set to the filtered training set, call the trained LSTM-CNN model and Lasso model on the test set to obtain two groups of initial prediction results respectively, and use the Stacking fusion strategie om de twee voorspellingsresultaten samen te voegen en het uiteindelijke voorspellingsresultaat te verkrijgen.strategy to merge the two prediction results and obtain the final prediction result. 3. Voorspellingsmethode voor fotovoltaisch vermogen volgens conclusie 2, met het kenmerk, dat in stap 2 het numerieke model hetzelfde parameterschema en dezelfde tijd- ruimteresolutie van het model gebruikt om twee sets verfijnde meteorologische voorspel- lingsgegevens in het doelbereik te verkrijgen, namelijk ERA5-downscaling van meteoro- logische gegevens en ECMWF-downscaling van meteorologische gegevens.Photovoltaic power forecasting method according to claim 2, characterized in that in step 2 the numerical model uses the same parameter scheme and the same spatiotemporal resolution of the model to obtain two sets of refined meteorological forecast data in the target range, namely ERA5 downscaling of meteorological data and ECMWF downscaling of meteorological data. 4. Voorspellingsmethode voor fotovoltaisch vermogen volgens conclusie 2, met het kenmerk, dat de meteorologische voorspellingselementen van de fotovoltaïsche energie- centrale in stap 4 omvatten: totale zonnestraling, directe zonnestraling, verstrooide zonne- straling, lage, gemiddelde en hoge bewolking, windsnelheid, windrichting, temperatuur, vochtigheid en druk.Photovoltaic power prediction method according to claim 2, characterized in that the meteorological prediction elements of the photovoltaic power plant in step 4 include: total solar radiation, direct solar radiation, scattered solar radiation, low, medium and high cloud cover, wind speed, wind direction , temperature, humidity and pressure. 5. Voorspellingsmethode voor fotovoltaisch vermogen volgens conclusie 2, met het kenmerk, dat de functie-extractie in stap 6 omvat: (1) Omzetting van de directe en verstrooide straling op de grond in totale zonnestraling van op het schuine oppervlak volgens de atmosferische stralingstheorie; (2) De windrichting van oppervlaktewind en wind op hoog niveau wordt gekenmerkt door windrichting cosinus en windrichting sinus; (3) Extraheer relevante informatie op basis van tijd- en geografische kenmerken, zet de tijdstempelinformatie van meteorologische gegevens om in discrete tijdinformatie door middel van verwerking en voeg labels van maand, week, dag en uur toe aan alle gegevens; (4) Voeg volgens de hellingshoek, lengtegraad- en breedtegraadinformatie van de fotovoltaïsche array, de kenmerklabels toe van de sinus van de zonnehoogte en de cosinus van de invalshoek van de zon. A photovoltaic power prediction method according to claim 2, characterized in that the feature extraction in step 6 comprises: (1) Conversion of the direct and scattered radiation on the ground into total solar radiation from the inclined surface according to the atmospheric radiation theory; (2) The wind direction of surface wind and high-level wind is characterized by wind direction cosine and wind direction sinus; (3) Extract relevant information based on time and geographical features, convert the timestamp information of meteorological data into discrete time information through processing, and add labels of month, week, day and hour to all data; (4) According to the inclination angle, longitude and latitude information of the photovoltaic array, add the feature labels of the sine of the sun's altitude and the cosine of the sun's incidence angle. 6 Voorspellingsmethode voor fotovoltaïsch vermogen volgens conclusie 2, waarbij in stap 6 de meteorologische functie-informatieset als trainingsset wordt ingevoerd in het willekeurige bos model om de mate van invloed van verschillende functie-informatie op de voorspellingsresultaten te berekenen en de optimale functie-subset te selecteren; Wanneer zich een boom in het willekeurige bos bevindt, volgens de bootstrap resampling-technologie, worden 1/3 van de gegevens van elke sub-beslissingsboom niet geëxtraheerd in het proces van trainingsmonster-extractie, dat out-of-pocket data wordt genoemd. De out-of-pocket data van de jde beslissingsboom worden voorspeld en de fout van de voorspellingsresultaten wordt berekend, waarbij de de fout van het voorspellings-6 Photovoltaic power prediction method according to claim 2, wherein in step 6 the meteorological function information set is input as a training set into the random forest model to calculate the degree of influence of different function information on the prediction results and select the optimal function subset ; When a tree is in the random forest, according to the bootstrap resampling technology, 1/3 of the data of each sub-decision tree is not extracted in the process of training sample extraction, which is called out-of-pocket data. The out-of-pocket data of the jth decision tree is predicted and the error of the prediction results is calculated, where the error of the prediction resultaat de out-of-pocket-gegevensfout van de jde beslissingsboom is: OOBerror; = 2 Zia Oe — Vip); waarin N; het totale aantal out-of-pocket gegevensmonsters is, y;,. de werkelijke waarde van de out-of-pocket gegevensmonsters is, en y;, de voorspelde waarde van de out-of-pocket gegevensmonsters is; De out-of-pocket datafout van het gehele willekeurige bos is: OOBerror = — yhhree OOBerror; ; Niree J=1 J De mate van belangrijkheid van de functie-informatie wordt berekend uit de out-of-bag gegevens om een referentie te bieden voor het selecteren van functie-informatie.result the out-of-pocket data error of the jth decision tree is: OOBerror; = 2 Zia Oe — Vip); where N; the total number of out-of-pocket data samples is,y;,. is the actual value of the out-of-pocket data samples, and y;, is the predicted value of the out-of-pocket data samples; The out-of-pocket data error of the entire random forest is: OOBerror = — yhhree OOBerror; ; Niree J=1 J The importance level of the job information is calculated from the out-of-bag data to provide a reference for selecting job information. Verander voor de out-of-pocket gegevensfout OOBerror; van de jde beslissingsboom één van functievariabelen x,(i=1,2,3,...,M) in de functieset X = {}, waarbij M het totale aantal functies is, andere functievariabelen blijven onveranderd, verstoor willekeurig de out-of-bag data van de functievariabelen en herbereken de gewijzigde out-of-bag data error OOBerror;. Het verschil van de out-of-bag datafout voor en na de veranderde functie wordt gebruikt als maatstaf voor het belang van de functie x;:For the out-of-pocket data error, change OOBerror; of the jth decision tree one of function variables x,(i=1,2,3,...,M) in the function set of-bag data of the function variables and recalculate the changed out-of-bag data error OOBerror;. The difference of the out-of-bag data error before and after the changed function is used as a measure of the importance of the function x;: MDA = — 3. tree(00Berror; — 00Berror)); Willekeurige inputfactoren voor screening van bosalgoritmen zijn onder meer: (1) Gebruik de bootstrap-samplingmethode om willekeurig K-samplesets te genereren in de originele trainingsgegevensset, selecteer de meest classificeerbare functie van de m-MDA = — 3. tree(00Berror, — 00Berror)); Random input factors for forest algorithm screening include: (1) Use the bootstrap sampling method to randomly generate K sample sets in the original training data set, select the most classifiable feature of the m functies voor het splitsen van knooppunten, genereer meerdere beslissingsbomen, vorm een willekeurig bos en gebruik een boomclassificatie stemmethode om nieuwe gegevens te classificeren;functions for splitting nodes, generate multiple decision trees, form a random forest and use a tree classification voting method to classify new data; (2) Bereken het belang MDA van elke functievariabele en sorteer deze in aflopende volgorde;(2) Calculate the importance MDA of each function variable and sort them in descending order; (3) Bepaal het te verwijderen deel en verwijder de onbelangrijke indicatoren in het overeenkomstige deel van de huidige functievariabele, om zo een nieuwe functieset te verkrijgen;(3) Determine the part to be removed and remove the unimportant indicators in the corresponding part of the current function variable, so as to obtain a new function set; (4) Creëer een nieuw willekeurig bos met de nieuwe functieset, bereken het belang van elke functie in de functieset en sorteer;(4) Create a new random forest with the new feature set, calculate the importance of each feature in the feature set and sort; (5) Herhaal stappen (2) tot (4) totdat er nog m functies over zijn;(5) Repeat steps (2) to (4) until there are m functions left; (6) Bereken op basis van elke verkregen functieset en willekeurig bos de overeenkomstige out-of-bag fout OOBerror, en gebruik de functieset met de laagste out-of- bag fout als de uiteindelijk geselecteerde feature set Q.(6) Based on each obtained feature set and random forest, calculate the corresponding out-of-bag error OOBerror, and use the feature set with the lowest out-of-bag error as the final selected feature set Q. 7. Voorspellingssysteem voor fotovoltaisch vermogen, onder toepassing van de voor- spellingsmethode voor fotovoltaïsch vermogen zoals beschreven in één van de conclusies 1-6, met het kenmerk, dat het voorspellingssysteem omvat: - Besturingsmodule voor numerieke voorspelling, die wordt gebruikt om het numerieke voorspellingsmodel aan te sturen op basis van respectievelijk atmosferische heranalyse van meteorologische gegevens en het globale weersvoorspellingsveld; - Voorbewerkingsmodule van gegevens, die wordt gebruikt voor de voorbewerking van meteorologische gegevens en vervolgens voor de functieselectie; - Model trainingsmodule, die wordt gebruikt voor de voorspellingsmethode op basis van LSTM-CNN en de LASSO-regressievoorspellingsmethode, en de elektrische veld- observatiegegevens combineert om het model te trainen en uiteindelijk het gecombineerde voorspellingsresultaat uit te voeren.7. Photovoltaic power prediction system, using the photovoltaic power prediction method as described in any one of claims 1 to 6, characterized in that the prediction system comprises: - Numerical prediction control module used to control the numerical prediction model to be controlled on the basis of atmospheric reanalysis of meteorological data and the global weather forecast field respectively; - Data preprocessing module, which is used for preprocessing meteorological data and then for feature selection; - Model training module, which is used for the prediction method based on LSTM-CNN and the LASSO regression prediction method, and combines the electric field observation data to train the model and finally output the combined prediction result. 8. Computer leesbaar opslagmedium dat computerprogramma's opslaat, met het kenmerk, dat wanneer het computerprogramma wordt uitgevoerd door de processor, de processor de stappen uitvoert van de fotovoltaïsche vermogensvoorspellingsmethode, zoals beschreven in conclusie 1.A computer readable storage medium storing computer programs, characterized in that when the computer program is executed by the processor, the processor executes the steps of the photovoltaic power prediction method as described in claim 1.
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