Optical power prediction system and method suitable for photovoltaic power station, and electronic equipment

Abstract

The invention relates to an optical power prediction system and method suitable for a photovoltaic power station and electronic equipment, and belongs to the technical field of photovoltaic power generation. The method comprises the following steps: acquiring current climate environment data of a photovoltaic power station in real time, wherein the climate environment data comprises altitude data, gradient data, time data, temperature data, humidity data, wind speed data and illumination intensity data; grouping the environment data according to slope climate environment data and flat climate environment data; data preprocessing is conducted on the sloping field climate environment data and the flat field climate environment data, a first data set and a second data set which are aligned according to time are obtained, and the first data set comprises sloping field environment data; the data group II comprises flat ground environment data; and performing optical power prediction of the photovoltaic power station based on the data set I and the data set II. The power generation units of the photovoltaic power station are finely divided, grouped and trained, and the prediction precision of the generated power is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a light power prediction system, method and electronic equipment applicable to a photovoltaic power station. Background Art

[0002] The existing optical power prediction system predicts the power generation of photovoltaic power stations based on factors such as ambient temperature, light radiation, air pressure, relative humidity and meteorological data. The power prediction of photovoltaic power stations requires a large amount of data and includes numerical weather forecast data, real-time meteorological data, real-time power data, operating status and planned maintenance information, but ignores the regional differences in meteorological data.

[0003] In addition, the existing photovoltaic power prediction system focuses on the correlation between the output power of the photovoltaic power station and the solar radiation intensity, while considering the influence of temperature, relative humidity and cloud cover, and combines the correlation analysis of the relationship between various meteorological factors and photovoltaic output to determine the actual solar radiation intensity, temperature, relative humidity of the historical measured meteorological conditions and the active power generation power in the historical output data of the photovoltaic power station as the input layer sample data for training to obtain the predicted photovoltaic power generation power.

[0004] However, the solar radiation intensity, temperature and relative humidity in the historical meteorological data do not take into account the regional differences in the distribution of photovoltaic panels. The solar radiation intensity, temperature and relative humidity in the historical meteorological data of the same region are used as standard data for training photovoltaic power stations. Since the meteorological data corresponding to the actual operation of multiple photovoltaic panels in distributed photovoltaic power stations deviate from the standard data, there is a deviation in the correlation analysis of the relationship between various meteorological factors and photovoltaic output, resulting in inaccurate photovoltaic power prediction values.

[0005] In addition, the existing photovoltaic power prediction system does not take into account that the multiple photovoltaic panels of the photovoltaic power station are actually arranged in a hillside environment. When the multiple photovoltaic panels of the actual photovoltaic power station are in operation, the wind forces on different slopes are different. For example, the photovoltaic panels on the left slope are in a windy environment and the photovoltaic panels on the right slope are in a windless environment. On the one hand, the wind on the left slope will take away part of the heat on the photovoltaic panels, thereby making the operating temperature of the photovoltaic panels on the left slope lower than the operating temperature of the photovoltaic panels on the right slope; on the other hand, the wind on the left slope will take away part of the dust on the photovoltaic panels, thereby the surface cleanliness of the photovoltaic panels on the left slope is higher than the surface cleanliness of the photovoltaic panels on the right slope; the difference in operating temperature and surface cleanliness of the photovoltaic panels on the left and right slopes makes the power generation of different photovoltaic panels different, and there will be deviations in prediction using a unified training model, resulting in a decrease in the accuracy of the prediction value.

[0006] Public Content

[0007] The purpose of the present disclosure is to provide a light power prediction system, method and electronic device suitable for photovoltaic power stations, which solves the problem of inaccurate prediction values ​​caused by deviations caused by predictions using a unified training model. The method of the present application finely divides and groups the power generation units of the photovoltaic power station based on slopes, flat land and different wind speeds, using photovoltaic strings as units, and trains each group separately, balancing the effects of differences in photovoltaic panel heating, cleanliness, heat dissipation and light radiation intensity on the power generation, thereby improving the prediction accuracy of the power generation.

[0008] A method for predicting light power for a photovoltaic power station, which is applied in the field of green electricity hydrogen production, comprises:

[0009] Acquire climate and environmental data of the photovoltaic power station in real time, wherein the climate and environmental data includes altitude data, slope data, time data, temperature data, humidity data, wind speed data and light intensity data;

[0010] The climate and environment data are grouped according to the climate and environment data of slope land and the climate and environment data of flat land;

[0011] Preprocess the slope climate environment data and the flat climate environment data to obtain a data group 1 and a data group 2 aligned in time, wherein the data group 1 includes the slope environment data and the data group 2 includes the flat environment data;

[0012] The power generation capacity is predicted based on the data set one and the data set two.

[0013] The method further comprises:

[0014] The data cleaning and data alignment of the slope climate environment data and the flat climate environment data includes:

[0015] Data cleaning, processing of missing values ​​and outliers;

[0016] Data standardization: standardize or normalize the cleaned data;

[0017] Feature extraction, extracting target feature data from standardized or normalized environmental data, the target is Shanghai 2024-11487 (M240487CNI)

[0018] The characteristic data include time characteristics, meteorological characteristics and geographical characteristics;

[0019] Data alignment: align the target feature data of the slope and the target feature data of the flat land in time.

[0020] The method further comprises:

[0021] The performing of optical power prediction of the photovoltaic power station based on the data group 1 and the data group 2 comprises:

[0022] Inputting the slope optical power prediction model and the flat optical power prediction model corresponding to the data group 1 and the data group 2 respectively obtains the slope optical power prediction value and the flat optical power prediction value;

[0023] Based on the slope optical power prediction value and the flat land optical power prediction value, a total optical power theoretical prediction value is calculated.

[0024] The method further comprises:

[0025] The input of the slope optical power prediction model also includes the optical power prediction value of the flat land optical power prediction model, and the output of the slope optical power prediction model also includes a first deviation value, which is the difference between the slope optical power prediction value and the flat land optical power prediction value;

[0026] The input parameter of the flat land optical power prediction model also includes the optical power prediction value of the slope land optical power prediction model, and the output parameter of the flat land optical power prediction model also includes a second deviation value, which is the difference between the flat land optical power prediction value and the optical power prediction value of the slope land optical power prediction model;

[0027] The predicted optical power value of the photovoltaic power station obtained based on the predicted optical power value of the slope and the predicted optical power value of the flat land also includes:

[0028] Correct the slope optical power prediction value according to the first deviation value to obtain a first corrected slope optical power prediction value;

[0029] Correct the predicted value of the flat-ground optical power according to the second deviation value to obtain a first corrected value of the predicted value of the flat-ground optical power;

[0030] The first corrected value of the predicted light power of the photovoltaic power station is calculated based on the first corrected value of the predicted light power of the slope and the first corrected value of the predicted light power of the flat land.

[0031] The method further comprises:

[0032] The step of correcting the slope optical power prediction value according to the first deviation value to obtain a first corrected slope optical power prediction value includes: the first corrected slope optical power prediction value = the slope optical power prediction value + K1 * Shanghai 2024-11487 (M240487CNI)

[0033] The first deviation value, K1 the first correction coefficient;

[0034] The method of correcting the predicted value of the flat ground optical power according to the second deviation value to obtain a first corrected value of the predicted value of the flat ground optical power includes: the first corrected value of the predicted value of the flat ground optical power = the predicted value of the flat ground optical power + K2*

[0035] The second deviation value, K2 the second correction coefficient;

[0036] The first corrected value of the predicted light power of the photovoltaic power station is calculated based on the first corrected value of the predicted light power of the slope and the first corrected value of the predicted light power of the flat land, including: the first corrected value of the predicted light power of the slope and the first corrected value of the predicted light power of the flat land are summed to obtain the first corrected value of the predicted light power of the photovoltaic power station.

[0037] The method further comprises:

[0038] Obtaining historical prediction data of flat land optical power, historical prediction data of sloping land optical power, and historical prediction data of photovoltaic power station optical power having the same time characteristics, meteorological characteristics, and geographical characteristics as the data group one and the data group two, wherein the historical prediction data of flat land optical power, historical prediction data of sloping land optical power, and historical prediction data of photovoltaic power station optical power having the same time characteristics, meteorological characteristics, and geographical characteristics refer to deviations within a preset range, and the historical prediction data of optical power refer to theoretical prediction value data of optical power whose deviations from the theoretical prediction value of optical power and the actual value of optical power are within a preset range;

[0039] The first correction value of the slope land optical power prediction value of the photovoltaic power station is compared with the slope land optical power historical prediction data to obtain a third deviation value, and the first correction value of the flat land optical power prediction value of the photovoltaic power station is compared with the flat land optical power historical prediction data to obtain a fourth deviation value;

[0040] Determine a second correction value of the slope optical power prediction value according to the third deviation value, and determine a second correction value of the flat optical power prediction value according to the fourth deviation value,

[0041] The second correction value of the predicted optical power value is calculated based on the second correction value of the predicted optical power value on the slope and the second correction value of the predicted optical power value on the flat land;

[0042] Comparing the second correction value of the optical power prediction value with the historical optical power prediction data of the photovoltaic power station to obtain a fifth deviation value;

[0043] When the fifth deviation value is less than or equal to the set deviation value, outputting a theoretical predicted value of the total optical power of the photovoltaic power station;

[0044] If the fifth deviation value is greater than the set deviation value, the second correction value of the optical power prediction value is Shanghai 2024-11487 (M240487CNI)

[0045] The third corrected value of the light power prediction value is corrected to obtain the third corrected value of the light power prediction value, and the third corrected value of the light power prediction value is output as the theoretical predicted value of the total light power of the photovoltaic power station.

[0046] The method further comprises:

[0047] Obtain actual optical power data during the forecast period;

[0048] The actual optical power data is compared with the theoretical predicted value of the total optical power of the photovoltaic power station to obtain a sixth deviation value. If the sixth deviation value is less than or equal to the set deviation value, the third correction value of the optical power prediction value is stored as historical data; if the sixth deviation value is greater than the set deviation value, the slope optical power prediction model is optimized according to the first deviation value and the third deviation value; and the flat optical power prediction model is optimized according to the second deviation value and the fourth deviation value.

[0049] Optionally, the photovoltaic power station is provided with a plurality of photovoltaic strings, each of the photovoltaic strings comprises a plurality of photovoltaic panels arranged in series, and the plurality of photovoltaic panels are connected in parallel to a combiner box;

[0050] The obtaining of the current climate and environmental data of the photovoltaic power station includes obtaining environmental data of each photovoltaic string;

[0051] Grouping the environmental data according to the climatic environmental data of the slope and the climatic environmental data of the flat land includes:

[0052] The photovoltaic strings with wind speed less than or equal to the first preset value in the photovoltaic power station are divided into a low-wind group on the slope within the station;

[0053] The photovoltaic strings in the photovoltaic power station with the first preset value < wind speed ≦ the second preset value are divided into the slope moderate wind group within the station;

[0054] The photovoltaic strings with wind speeds greater than the second preset value in the photovoltaic power station are divided into the high wind group on the slope within the station;

[0055] The data of each photovoltaic string in the in-station slope low wind group, the in-station slope medium wind group and the in-station slope high wind group include photovoltaic data and environmental data;

[0056] Grouping the photovoltaic data and environmental data according to slope data and flat land data includes:

[0057] The photovoltaic strings with wind speed ≤ the fourth preset value in the photovoltaic power station are divided into the flat ground slow wind group in the station;

[0058] The photovoltaic strings with wind speed greater than the third preset value in the photovoltaic power station are divided into the flat ground and wind groups within the station.

[0059] The slope optical power prediction model and the flat optical power prediction model respectively include a plurality of parallel DNN neural network modules, and the data of the slope low wind group, the slope medium wind group and the slope high wind group of each photovoltaic string within the statistical time are respectively input into the independent DNN neural network module of the slope optical power prediction model, and the data of the flat slow wind group and the flat and wind group within the station are respectively input into the independent DNN neural network module of the flat optical power prediction model, and the slope optical power prediction model and the flat optical power prediction model are respectively connected.

[0060] The output power generation of each DNN neural network module of the power prediction model is accumulated to obtain the slope optical power prediction value and the flat land optical power prediction value.

[0061] A light power prediction system suitable for a photovoltaic power station comprises a photovoltaic power station centralized control room controller and a plurality of photovoltaic strings. The photovoltaic power station centralized control room controller communicates with each photovoltaic string in the photovoltaic power station and exchanges data.

[0062] An electronic device stores a computer program, which, when executed by a processor, implements a light power prediction method applicable to a photovoltaic power station.

[0063] Beneficial technical effects:

[0064] By finely dividing and grouping the power generation units of the photovoltaic power station with photovoltaic strings as units according to slopes, flat land and different wind speeds, and training each group separately, the effects of differences in photovoltaic panel heating, cleanliness, heat dissipation and light radiation intensity on the power generation are balanced, thereby improving the prediction accuracy of the power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present disclosure. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0066] Figure 1 The diagram is a schematic diagram of the arrangement of photovoltaic panels of a photovoltaic power station on a slope according to an embodiment of the present disclosure.

[0067] Figure 2 It is a schematic diagram of the photovoltaic panel structure of an embodiment of the present disclosure.

[0068] Figure 3 Schematic diagram of a photovoltaic string and a combiner box according to an embodiment of the present disclosure.

[0069] Figure 4 The diagram is a schematic diagram of the data measurement arrangement of the photovoltaic string and the combiner box according to the embodiment of the present disclosure.

[0070] Figure 5 Schematic diagram of parallel connection of method models of embodiments of the present disclosure. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not used to limit the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work belong to this public Shanghai 2024-11487 (M240487CNI)

[0072] Open the protection range.

[0073] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0074] Green Hydrogen via Electrolysis refers to the use of renewable energy sources such as solar energy, wind energy, etc. to drive the water electrolysis process to produce hydrogen. In this process, it is crucial to accurately predict the power generation of renewable energy, because this directly affects the working efficiency of the electrolyzer and the hydrogen production. The concept of the present disclosure is that, unlike the traditional photovoltaic station data-training-prediction method, this application subdivides and groups the photovoltaic station data, thereby characterizing smaller and more accurate dimensions that affect power generation, and then trains and accumulates the results to obtain the predicted value. For a photovoltaic power station system based on solar energy, a light power prediction model is needed to estimate the intensity of solar radiation in the future, and predict the output power of the photovoltaic power generation system based on this.

[0075] Embodiment 1

[0076] Photovoltaic power generation is a green and clean energy. The use of photovoltaic green energy to produce hydrogen not only improves the utilization rate of green energy but also improves the local balance of photovoltaic power generation, thereby avoiding the loss of long-distance transportation. The use of photovoltaic green energy to produce hydrogen involves the prediction of photovoltaic power generation. In the existing photovoltaic power prediction system, the output power of the photovoltaic power station is focused on being related to the solar radiation intensity, while considering the influence of temperature, relative humidity and cloud layer. Combined with the correlation analysis of the relationship between various meteorological factors and photovoltaic output, the actual solar radiation intensity, temperature, relative humidity of the historical measured meteorology and the active power generation power in the historical output data of the photovoltaic power station are determined as the input layer sample data for training to obtain the predicted photovoltaic power generation power.

[0077] However, the solar radiation intensity, temperature and relative humidity in the historical meteorological data do not take into account the regional differences in the distribution of photovoltaic panels. The solar radiation intensity, temperature and relative humidity in the historical meteorological data of the same region are used as the standard data for training photovoltaic power stations. Since the meteorological data corresponding to the actual operation of multiple photovoltaic panels in distributed photovoltaic power stations deviate from the standard data, the relationship between various meteorological factors and photovoltaic output is not well understood. Shanghai 2024-11487 (M240487CNI)

[0078] There is a deviation in the correlation analysis, resulting in inaccurate photovoltaic power prediction values.

[0079] like Figure 1 As shown, in the existing photovoltaic power prediction system, it is not considered that the multiple photovoltaic panels of the photovoltaic power station are actually arranged in a hillside environment. When the multiple photovoltaic panels of the actual photovoltaic power station are in operation, the wind forces on different slopes are different. For example, the photovoltaic panels on the left slope are in a windy environment and the photovoltaic panels on the right slope are in a windless environment. On the one hand, the wind on the left slope will take away part of the heat on the photovoltaic panels, so that the operating temperature of the photovoltaic panels on the left slope is lower than that of the photovoltaic panels on the right slope; on the other hand, the wind on the left slope will take away part of the dust on the photovoltaic panels, so that the surface cleanliness of the photovoltaic panels on the left slope is higher than that of the photovoltaic panels on the right slope; the difference in operating temperature and surface cleanliness of the photovoltaic panels on the left and right slopes makes the power generation of different photovoltaic panels different, and there will be deviations in prediction using a unified training model, resulting in a decrease in the accuracy of the prediction value.

[0080] like Figure 2As shown, each photovoltaic panel of a photovoltaic power station contains multiple photoelectric conversion units. For example, each photovoltaic panel includes 60 photoelectric conversion units. A photovoltaic power station has 1000 photovoltaic panels, then a photovoltaic power station contains 60000 photoelectric conversion units. If the temperature of each photoelectric conversion unit is to be detected, a photovoltaic power station will be equipped with 60000 temperature detection modules. On the one hand, the amount of temperature detection is large; on the other hand, the cost of temperature detection modules and wiring is high; on the third hand, the sampling data of 60000 temperature detection modules are used for training, resulting in excessively large parameters, difficult model convergence, and large amount of calculation, resulting in a geometric increase in computational cost.

[0081] The present disclosure proposes a method for predicting light power for a photovoltaic power station, which is applied to the field of green electricity hydrogen production. The method comprises:

[0082] Acquire the current climate and environmental data of the photovoltaic power station in real time, wherein the climate and environmental data includes altitude data, slope data, time data, temperature data, humidity data, wind speed data, and light intensity data;

[0083] The environmental data are grouped according to the climate and environmental data of the slope land and the climate and environmental data of the flat land;

[0084] Preprocessing the slope climate environment data and the flat climate environment data to obtain a data set 1 and a data set 2 aligned in time, wherein the data set 1 includes the slope environment data and the data set 2 includes the flat environment data;

[0085] The optical power of the photovoltaic power station is predicted based on the data set one and the data set two.

[0086] Since the heat dissipation and cleanliness of photovoltaic power stations on slopes and flat land are different, the slope and flat land data are Shanghai 2024-11487 (M240487CNI)

[0087] The photovoltaic power is predicted by grouping and performing light power prediction respectively, which improves the prediction accuracy of photovoltaic stations.

[0088] Specifically, in the embodiment of the present application, by distinguishing the climate and environmental data of slopes and flat lands, and performing data preprocessing and power generation prediction respectively, the power generation performance of photovoltaic power stations under different terrain conditions can be more accurately reflected. This is because the differences in terrain and landforms between slopes and flat lands will lead to different climate conditions such as light and wind speed, thereby affecting the power generation efficiency of photovoltaic panels. Through group processing, the power generation under different terrains can be analyzed and predicted more finely, improving the accuracy of the prediction.

[0089] In addition, in the embodiments of the present application, by acquiring climate and environmental data in real time, grouping and preprocessing it, the prediction model can adapt to different environmental conditions. This adaptability is crucial for the photovoltaic power station to maintain stable operation in a changing climate environment, and helps to improve the overall power generation efficiency and economic benefits of the power station.

[0090] It is worth mentioning that accurate light power forecasting can help power station operators better plan and dispatch resources. For example, the maintenance and upgrade of photovoltaic power stations can be reasonably arranged according to the forecast results to reduce losses caused by unexpected power outages or insufficient power generation.

[0091] For example, if the forecast shows that the photovoltaic panels in the slope area may generate less power due to high wind speed on a certain day, while the light conditions in the flat area are good, the operation and maintenance personnel can adjust the angle of the photovoltaic panels or clean the photovoltaic panels in advance to improve the power generation efficiency. At the same time, the power station can communicate with the power grid company in advance based on the forecast results to reasonably arrange the power generation plan and ensure the stability of the power supply. In this way, the power station can make more effective use of limited resources and improve power generation efficiency and economic benefits.

[0092] Further, in some possible implementations, the data preprocessing of the slope climate environment data and the flat climate environment data includes:

[0093] Data cleaning, processing of missing values ​​and outliers;

[0094] Data standardization: standardize or normalize the cleaned data;

[0095] Feature extraction, extracting target feature data from the standardized or normalized photovoltaic data and environmental data, wherein the target feature data includes time features, meteorological features, and geographical features;

[0096] Data alignment: align the target feature data of the slope and the target feature data of the flat land in time.

[0097] Process the data to facilitate data operations.

[0098] Specifically, in the embodiment of the present application, missing values ​​and outliers can be eliminated by data cleaning.

[0099] Errors and inconsistencies in the data set can be eliminated to improve the accuracy and reliability of the data. This is crucial for subsequent data analysis and model training, as low-quality data can lead to deviations in analysis results and degradation of model performance.

[0100] Specifically, standardizing or normalizing the data can ensure the consistency of the scale of different features. Standardized data can increase the convergence speed of the algorithm and improve the predictive ability of the model.

[0101] Specifically, feature extraction is the process of converting raw data into a form that is more useful to the model. By extracting temporal features, meteorological features, and geographic features, the key factors affecting photovoltaic power generation can be better captured, thereby improving the accuracy and generalization ability of the prediction model.

[0102] More specifically, aligning the target feature data of slopes and flat lands in time can ensure the consistency of data in different terrains in time series, which is very important for time series analysis and prediction models because it can ensure that the model can obtain consistent training and prediction results under different terrain conditions.

[0103] For example, suppose a photovoltaic power station is located in a mountainous area, and its data set contains climate and environmental data for the past year, including altitude, slope, time, temperature, humidity, wind speed, and light intensity. Before the above method is used, there may be missing values, outliers, and inconsistencies in the time of different terrain data in the data set, resulting in poor training results for the power generation prediction model.

[0104] After adopting the above method, the data set can be cleaned first, and missing values ​​and outliers can be processed, such as using mean filling or statistical-based methods to identify and process outliers. Then, the cleaned data can be standardized or normalized to make the scales of different features consistent, such as using minimum and maximum scaling or Z-score standardization. Next, the target feature data is extracted from the standardized or normalized data, including time features (such as hours of the day), meteorological features (such as temperature, humidity, wind speed), and geographic features (such as altitude, slope). Finally, the target feature data of slopes and flat lands are aligned in time to ensure the consistency of data in different terrains in the time series. Through this series of data preprocessing steps, the accuracy and reliability of photovoltaic power station power generation prediction can be significantly improved.

[0105] Further, in some possible implementations, the performing of optical power prediction of a photovoltaic power station based on the data set 1 and the data set 2 includes:

[0106] Input the slope optical power prediction model and the flat optical power prediction model corresponding to the data group 1 and the data group 2 respectively to obtain the slope optical power prediction value and the flat optical power prediction value;

[0107] Shanghai 2024-11487(M240487CNI)

[0108] The theoretical predicted value of the total optical power of the photovoltaic power station is calculated based on the predicted value of the optical power on the slope and the predicted value of the optical power on the flat land.

[0109] That is, different training models are used to predict the grouped data, which improves the prediction accuracy of input parameters for slopes and flat lands.

[0110] Specifically, by establishing light power prediction models for slopes and flat areas respectively, the impact of different terrains on photovoltaic power generation can be captured more accurately. This method takes into account the impact of terrain changes on climate conditions such as light and temperature, thereby improving the accuracy of prediction.

[0111] Different terrains have different effects on the power generation efficiency of photovoltaic panels. Therefore, by predicting slopes and flat lands separately, the model can better adapt to photovoltaic power plants under different terrain conditions, improving the generalization ability of the prediction model. Accurate light power prediction helps power plant operators plan and dispatch resources more effectively. For example, the maintenance and upgrade of photovoltaic power plants can be reasonably arranged according to the prediction results to reduce losses caused by unexpected power outages or insufficient power generation.

[0112] It is worth noting that photovoltaic power generation is intermittent and volatile, and large-scale photovoltaic power generation connected to the power grid will affect the stable operation of the power grid. In the embodiment of the present application, accurate power prediction can be used to reasonably arrange the operation mode and response measures of the power grid, thereby improving the safety and reliability of the power grid.

[0113] For example, the power station operation and maintenance personnel can establish optical power prediction models for slopes and flat areas respectively. First, they collect climate and environmental data for slopes and flat areas, including altitude, slope, time, temperature, humidity, wind speed, and light intensity. Then, these data are input into the corresponding prediction models to obtain the optical power prediction values ​​for slopes and flat areas. Finally, the theoretical prediction value of total optical power is calculated based on these two prediction values.

[0114] Furthermore, in some possible implementations, the method further includes:

[0115] Inputting the optical power prediction value of the flat land optical power prediction model into the slope land optical power prediction model, and based on the input optical power prediction value of the flat land optical power prediction model, the slope land optical power prediction model outputs a first deviation value, wherein the first deviation value is the difference between the slope land optical power prediction value and the flat land optical power prediction value;

[0116] The optical power prediction value of the slope optical power prediction model is input into the flat land optical power prediction model. Based on the input optical power prediction value of the slope optical power prediction model, the flat land optical power prediction model is

[0117] The prediction model outputs a second deviation value, where the second deviation value is the difference between the flat land optical power prediction value and the optical power prediction value of the slope optical power prediction model;

[0118] Correct the slope optical power prediction value according to the first deviation value to obtain a first corrected slope optical power prediction value;

[0119] Correct the predicted value of the flat-ground optical power according to the second deviation value to obtain a first corrected value of the predicted value of the flat-ground optical power;

[0120] The first corrected value of the predicted light power of the photovoltaic power station is calculated based on the first corrected value of the predicted light power of the slope and the first corrected value of the predicted light power of the flat land.

[0121] Specifically, in the embodiment of the present application, by inputting the flat land optical power prediction value into the slope land optical power prediction model, and inputting the slope land optical power prediction value into the flat land optical power prediction model, the deviation value between the two can be calculated. This two-way correction method can more accurately capture the difference in power generation between slope land and flat land, thereby improving the overall prediction accuracy.

[0122] By calculating the first deviation value and the second deviation value and correcting the optical power prediction values ​​of slopes and flat lands accordingly, the prediction model can be better adapted to different terrain conditions. This method helps to improve the robustness of the model in the face of complex terrain, so that it can provide reliable prediction results under different environmental conditions.

[0123] It is worth noting that accurate light power prediction is crucial for the operation of photovoltaic power plants and the dispatching of power grids. Through the above method, the power generation plan of the power station can be planned more effectively, the operation mode of the power grid can be reasonably arranged, and the safety and reliability of the power grid can be improved.

[0124] For example: The power station operation and maintenance personnel can establish optical power prediction models for slopes and flat lands respectively. Then, they input the predicted value of the flat land into the slope model and calculate the first deviation value, which reflects the prediction error of the slope model when considering the flat land conditions. Similarly, they also input the predicted value of the slope into the flat land model and calculate the second deviation value, which reflects the prediction error of the flat land model when considering the slope conditions. Next, the operation and maintenance personnel correct the predicted values ​​of the slope and flat land respectively according to these two deviation values ​​to obtain the corrected prediction values. Finally, they calculate the total optical power prediction value of the entire power station based on these corrected prediction values. For example, if the first deviation value shows that the actual power generation power of the slope is generally higher than that predicted by the flat land model, then they can adjust the predicted value of the slope accordingly to reflect this difference. In this way, the power station can more accurately predict the power generation power under different terrain conditions, so as to be more effective Shanghai 2024-11487 (M240487CNI)

[0125] Local planning of power generation and grid dispatching.

[0126] Further, in some possible implementations, the slope optical power prediction value is corrected according to the first deviation value to obtain a first corrected value of the slope optical power prediction value, including: the first corrected value of the slope optical power prediction value = the slope optical power prediction value + K1*first deviation value, K1 is the first correction coefficient; optionally, K1 is a value between 0.1 and 0.2, that is, K1 is a positive value, and the heat dissipation condition of the slope is better than that of the flat land, so consider making a positive correction to its predicted value.

[0127] The method of correcting the flat ground optical power prediction value according to the second deviation value to obtain the first corrected value of the flat ground optical power prediction value includes: the first corrected value of the flat ground optical power prediction value = the flat ground optical power prediction value + K2*the second deviation value, K2 the second correction coefficient; optionally, K2 is a value between -0.1 and -0.2, that is, K2 is a negative value, and the heat dissipation condition of the flat ground is worse than that of the slope, so it is considered to make a negative correction to its prediction value.

[0128] The first corrected value of the predicted light power of the photovoltaic power station is calculated based on the first corrected value of the predicted light power of the slope and the first corrected value of the predicted light power of the flat land, including: the first corrected value of the predicted light power of the slope and the first corrected value of the predicted light power of the flat land are summed to obtain the first corrected value of the predicted light power of the photovoltaic power station.

[0129] In the embodiment of the present application, by introducing correction coefficients K1 and K2 to correct the predicted optical power values ​​of slopes and flat lands, the actual power generation can be more accurately reflected. This method allows the model to adjust the predicted value according to historical data and actual conditions, thereby improving the accuracy of the prediction. At the same time, the correction coefficients K1 and K2 can be adjusted according to different time, climate conditions and terrain characteristics, so that the prediction model can adapt to various environmental changes and improve the generalization ability of the model.

[0130] For example: The power station operation and maintenance personnel established optical power prediction models for sloped land and flat land respectively. Then, they input the predicted value of flat land into the sloped land model and calculated the first deviation value, which reflects the prediction error of the sloped land model when considering the flat land conditions. Similarly, they also input the predicted value of sloped land into the flat land model and calculated the second deviation value, which reflects the prediction error of the flat land model when considering the sloped land conditions.

[0131] The method further comprises:

[0132] Obtain the historical forecast data of flat land optical power, historical forecast data of slope optical power, and photovoltaic power station optical power Shanghai 2024-11487 (M240487CNI) with the same time characteristics, meteorological characteristics and geographical characteristics as the data group 1 and the data group 2

[0133] Historical prediction data, wherein the same means that the deviation is within a preset range, and the optical power historical prediction data is the optical power theoretical prediction value data whose deviation between the optical power theoretical prediction value and the optical power actual value is within a preset range;

[0134] The first correction value of the slope land optical power prediction value of the photovoltaic power station is compared with the slope land optical power historical prediction data to obtain a third deviation value, and the first correction value of the flat land optical power prediction value of the photovoltaic power station is compared with the flat land optical power historical prediction data to obtain a fourth deviation value;

[0135] Determine a second correction value of the slope optical power prediction value according to the third deviation value, and determine a second correction value of the flat optical power prediction value according to the fourth deviation value,

[0136] The second corrected value of the predicted light power of the photovoltaic power station is calculated according to the second corrected value of the predicted light power of the slope and the second corrected value of the predicted light power of the flat land;

[0137] The fifth deviation value is obtained by comparing the second correction value of the light power prediction value of the photovoltaic power station with the historical light power prediction data of the photovoltaic power station;

[0138] When the fifth deviation value is less than or equal to the set deviation value, the theoretical predicted value of the total optical power of the photovoltaic power station is output;

[0139] If the fifth deviation value is greater than the set deviation value, the second correction value of the optical power prediction value is corrected to obtain a third correction value of the optical power prediction value, and the third correction value of the optical power prediction value is output as the theoretical prediction value of the total optical power of the photovoltaic power station.

[0140] In the embodiment of the present application, by introducing historical prediction data to calculate the deviation value, the performance of the current prediction model can be more accurately evaluated. This method utilizes the statistical characteristics of historical data and helps to improve the accuracy of the prediction. By calculating the third deviation value and the fourth deviation value, and correcting the optical power prediction values ​​of the slope and flat land accordingly, the prediction model can be better adapted to the changing trend of historical data and improve the self-correction ability of the model.

[0141] For example: Power station operation and maintenance personnel can establish optical power prediction models for slopes and flat lands respectively. Then, they collect historical prediction data with the same time characteristics, meteorological characteristics, and geographical characteristics as these data groups, and ensure that the deviation of these historical data is within the preset range. Next, they compare the current slope optical power prediction value with the historical slope optical power prediction data to obtain the third deviation value, and correct the slope optical power prediction value accordingly. Similarly, they also correct the flat optical power prediction value. Finally, they calculate the total optical power prediction value of the entire power station based on the corrected slope and flat optical power prediction values, and compare it with the historical power station optical power prediction data to obtain the fifth deviation value. If the fifth deviation value is within the preset Shanghai 2024-11487 (M240487CNI)

[0142] If the deviation value is within the preset range, they will output the predicted value as the theoretical predicted value of the total optical power of the power station. If the deviation value exceeds the preset range, they will further correct the predicted value until a satisfactory accuracy is achieved.

[0143] Furthermore, in some possible implementations, the above method further includes:

[0144] Obtain actual optical power data during the forecast period;

[0145] The actual optical power data is compared with the theoretical predicted value of the total optical power of the photovoltaic power station to obtain a sixth deviation value. If the sixth deviation value is less than or equal to the set deviation value, the third correction value of the optical power prediction value is stored as historical data; if the sixth deviation value is greater than the set deviation value, the slope optical power prediction model is optimized according to the first deviation value and the third deviation value; and the flat optical power prediction model is optimized according to the second deviation value and the fourth deviation value.

[0146] By coupling and correcting the slope prediction value and the flat land prediction value, the mutual influence between photovoltaic power generation power at different locations is taken into account, and the overall prediction accuracy of the photovoltaic power station is improved.

[0147] In the embodiment of the present application, by obtaining actual optical power data and comparing it with the theoretical prediction value, the accuracy of the prediction model can be quantified. If the sixth deviation value is within the preset range, it means that the prediction accuracy of the model is acceptable, and it can be stored as historical data for calibration and improvement of future models. If the sixth deviation value is greater than the set deviation value, it means that the prediction accuracy of the current model is insufficient and needs to be further optimized. By comparing the first deviation value with the third deviation value, and the second deviation value with the fourth deviation value, the deficiencies of the model in the prediction of slopes and flat areas can be identified, thereby optimizing the optical power prediction models of slopes and flat areas in a targeted manner.

[0148] It is worth noting that the optimization process of the model takes into account the actual power generation data, so that the model can better adapt to changes in actual environmental conditions, improve the adaptability and robustness of the model, and by continuously optimizing the prediction model, the accuracy of photovoltaic power station power generation prediction can be improved, thereby helping power grid companies to more effectively dispatch and allocate resources, and improve the safety and reliability of the power grid.

[0149] For example: The power station operation and maintenance personnel first establish the optical power prediction model for slope and flat land respectively, and make predictions. Then, they collect the actual optical power data and compare it with the theoretical prediction value to obtain the sixth deviation value. If this deviation value is within the preset range, it means that the prediction accuracy of the model is acceptable, and these prediction values ​​can be stored as historical data. If the deviation value exceeds the preset range, they will optimize the slope optical power prediction model based on the first deviation value and the third deviation value, and optimize the slope optical power prediction model based on the second deviation value and the third deviation value.

[0150] Four deviation values ​​optimize the flat-land optical power prediction model. For example, if the analysis finds that the slope model has low prediction accuracy in cloudy weather, they may introduce more meteorological factors, such as cloud coverage and wind speed, to improve the model. In this way, the power station can continuously improve the accuracy of power generation prediction, thereby more effectively planning power generation and grid scheduling.

[0151] Furthermore, in some possible implementations, the photovoltaic power station is provided with a plurality of photovoltaic strings, each of the photovoltaic strings comprises a plurality of photovoltaic panels arranged in series, and the plurality of photovoltaic panels are connected in parallel to a combiner box;

[0152] The obtaining of the current climate and environmental data of the photovoltaic power station includes obtaining environmental data of each photovoltaic string;

[0153] Grouping the environmental data according to the climatic environmental data of the slope and the climatic environmental data of the flat land includes:

[0154] The photovoltaic strings with wind speed ≤ the first preset value in the photovoltaic power station are divided into the slope low wind group within the station;

[0155] The photovoltaic strings in the photovoltaic power station with the first preset value < wind speed ≦ the second preset value are divided into the slope moderate wind group within the station;

[0156] The photovoltaic strings with wind speeds greater than the second preset value in the photovoltaic power station are divided into the high wind group on the slope within the station;

[0157] The data of each photovoltaic string in the in-station slope low wind group, the in-station slope medium wind group and the in-station slope high wind group include photovoltaic data and environmental data;

[0158] Grouping the photovoltaic data and environmental data according to slope data and flat land data includes:

[0159] The photovoltaic strings with wind speed ≤ the fourth preset value in the photovoltaic power station are divided into the flat ground slow wind group in the station;

[0160] The photovoltaic strings with wind speed greater than the third preset value in the photovoltaic power station are divided into the flat ground and wind groups within the station.

[0161] In the embodiment of the present application, by dividing the photovoltaic strings of the photovoltaic power station into different groups according to the wind speed, more refined environmental data management and power generation prediction can be performed on the photovoltaic strings of each group. This grouping method helps to identify and analyze the performance of photovoltaic panels under different wind speed conditions, thereby improving the management efficiency and power generation efficiency of the power station. By grouping the photovoltaic strings under different wind speed conditions, the power generation under specific wind speed conditions can be more accurately predicted. This is because wind speed is one of the important factors affecting the efficiency of photovoltaic power generation, especially under different terrain conditions of slopes and flat lands, the impact of wind speed on photovoltaic panels may be different. Based on this, through grouping and refined management, power station operators can allocate resources more specifically, such as taking different maintenance and optimization measures for slope areas with higher wind speeds to improve power generation efficiency.

[0162] For example: If the forecast shows that in a slope area with high wind speed, the power generation efficiency of the photovoltaic string may be Shanghai 2024-11487 (M240487CNI)

[0163] If the wind speed is affected and reduced, the operation and maintenance personnel can take measures in advance, such as adjusting the angle of the photovoltaic panels or adding support structures, to reduce the impact of wind speed on power generation efficiency. At the same time, the power station can communicate with the power grid company in advance based on the forecast results, reasonably arrange the power generation plan, and ensure the stability of power supply. In this way, the power station can make more effective use of limited resources and improve power generation efficiency and economic benefits.

[0164] Furthermore, in some possible implementations, the slope optical power prediction model and the flat land optical power prediction model respectively include multiple parallel DNN neural network modules, and the data of the slope low wind group, the slope medium wind group and the slope high wind group of each photovoltaic string within the statistical time are respectively input into the independent DNN neural network module of the slope optical power prediction model, and the data of the flat land slow wind group and the flat land and wind group within the station are respectively input into the independent DNN neural network module of the flat land optical power prediction model, and the output power generation power of each DNN neural network module of the slope optical power prediction model and the flat land optical power prediction model are respectively accumulated to obtain the slope optical power prediction value and the flat land optical power prediction value.

[0165] In the embodiments of the present application, by using a deep neural network (DNN) model, the complex nonlinear relationship in the climate and environmental data of the photovoltaic power station can be captured, thereby improving the accuracy of the optical power prediction. The DNN model can learn high-level features in the data due to its deep structure, which is particularly important for predicting time series data such as photovoltaic power generation that is affected by multiple factors. Multiple parallel DNN neural network modules can independently learn and extract data features of different photovoltaic strings, and then output the power generation by accumulating. Such a structure helps to improve the model's generalization ability for photovoltaic power generation behavior under different conditions.

[0166] For example: the power station operation and maintenance personnel can first obtain the environmental data of each photovoltaic string, including wind speed, temperature, humidity, etc. Then, according to the different wind speeds, the photovoltaic strings are divided into the low wind group on the slope within the station, the medium wind group on the slope within the station, the high wind group on the slope within the station, the slow wind group on the flat land within the station, and the moderate wind group on the flat land within the station. The photovoltaic string data of each group includes photovoltaic data and environmental data. Then, these data are input into the corresponding DNN neural network modules respectively. For example, the data of the low wind group on the slope within the station is input into a DNN module of the slope optical power prediction model, and the data of the slow wind group on the flat land within the station is input into a DNN module of the flat optical power prediction model. In this way, each DNN module can specifically process data under specific wind speed conditions, so as to more accurately predict the power generation under such conditions. Finally, the outputs of all DNN modules are accumulated to obtain the predicted value of the optical power of the entire photovoltaic power station. This method significantly improves the prediction of the power generation of photovoltaic power stations by utilizing the powerful learning ability and multi-level feature extraction ability of DNN. Shanghai 2024-11487 (M240487CNI)

[0167] Accuracy and reliability.

[0168] Furthermore, an embodiment of the present application also provides an optical power prediction system suitable for a photovoltaic power station, including a photovoltaic power station control room controller and multiple photovoltaic strings. The photovoltaic power station control room controller and each photovoltaic string in the photovoltaic power station establish a data interaction link through wired or wireless communication methods.

[0169] like Figure 3 As shown, multiple photovoltaic panels of a photovoltaic power station are connected in series in sequence to form a photovoltaic string, and multiple photovoltaic strings are connected in parallel to a junction box. For example, 20 photovoltaic panels are connected in series to form a photovoltaic string. A photovoltaic power station has 1,000 photovoltaic panels, and a photovoltaic power station has 50 photovoltaic strings.

[0170] Optionally, a photovoltaic power station has one or more combiner boxes.

[0171] like Figure 4 and Figure 5 As shown, a method for predicting optical power of a photovoltaic power station includes the following steps:

[0172] S1. The controller in the photovoltaic power station control room obtains data of multiple photovoltaic strings in each photovoltaic power station;

[0173] It can be understood that a plurality of photovoltaic panels in a photovoltaic power station are connected in series in sequence to form a photovoltaic string; and a plurality of photovoltaic strings are connected in parallel to the combiner box 4 .

[0174] For slopes:

[0175] Preferably, a wind speed measuring instrument 1 is provided on the head photovoltaic panel of at least one photovoltaic string, and a thermometer 2 and a light radiation intensity meter 3 are provided on the tail photovoltaic panel;

[0176] Preferably, a thermometer 2 and a light radiation intensity meter 3 are provided on the head photovoltaic panel of at least one photovoltaic string, and a wind speed meter 1 is provided on the tail photovoltaic panel;

[0177] Preferably, a wind speed measuring instrument 1, a thermometer 2 and a light radiation intensity meter 3 are provided on the head end photovoltaic panel of at least one photovoltaic string;

[0178] Preferably, at least one photovoltaic string has a tail photovoltaic panel provided with a wind speed meter 1, a thermometer 2 and a light radiation intensity meter 3;

[0179] Preferably, an anemometer 1 is provided on an intermediate photovoltaic panel of at least one photovoltaic string, and a thermometer 2 and a light radiation intensity meter 3 are provided on another intermediate photovoltaic panel; that is, anemometer 1, thermometer 2 and light radiation intensity meter 3 are provided on adjacent or non-adjacent intermediate photovoltaic panels of the photovoltaic string, respectively, that is, anemometer 1 is provided on one intermediate photovoltaic panel, and thermometer 2 and light radiation intensity meter 3 are provided on another intermediate photovoltaic panel at the same time;

[0180] Shanghai 2024-11487(M240487CNI)

[0181] The slope data includes the wind speed measured by the anemometer 1 of each photovoltaic string, the temperature measured by the thermometer 2, the light radiation intensity measured by the light radiation intensity meter 3, and the photovoltaic string power generation;

[0182] For slopes, the wind speed, solar radiation and heat dissipation at different altitudes and slopes are different, and the temperature difference is large, so a unified meteorological temperature is not used. Instead, each photovoltaic string independently measures the wind speed, temperature and solar radiation intensity. In addition, for slopes, by collecting wind speed and temperature for each photovoltaic string, different wind speeds have already comprehensively considered the cleanliness and heat dissipation of the photovoltaic panels, so there is no need to measure the cleanliness of the photovoltaic panels or photovoltaic strings on the slope.

[0183] For flat land:

[0184] Preferably, a wind speed measuring instrument 1 is provided on the head photovoltaic panel of at least one photovoltaic string, and a light radiation intensity meter 3 is provided on the tail photovoltaic panel;

[0185] Preferably, a light radiation intensity meter 3 is provided on the head photovoltaic panel of at least one photovoltaic string, and a wind speed meter 1 is provided on the tail photovoltaic panel;

[0186] Preferably, a wind speed meter 1 and a light radiation intensity meter 3 are provided on the head end photovoltaic panel of at least one photovoltaic string;

[0187] Preferably, a wind speed meter 1 and a light radiation intensity meter 3 are provided on the tail photovoltaic panel of at least one photovoltaic string;

[0188] Preferably, a wind speed measuring instrument 1 is provided on one middle photovoltaic panel of at least one photovoltaic string, and a light radiation intensity meter 3 is provided on another middle photovoltaic panel; that is, a wind speed measuring instrument 1 and a light radiation intensity meter 3 are provided on adjacent or non-adjacent middle photovoltaic panels of the photovoltaic string, respectively, that is, the wind speed measuring instrument 1 is provided on one middle photovoltaic panel, and the light radiation intensity meter 3 is provided on another middle photovoltaic panel;

[0189] Optionally, a weighing unit 5 is provided on the top of the combiner box 4 at the flat ground, and the weighing unit 5 is used to measure the weight of dust and impurities to centrally map the cleanliness of the photovoltaic panels at the flat ground;

[0190] The mapping is:

[0191] If the weight measured by weighing unit 5 is less than 10 grams, the cleanliness factor of the photovoltaic panel on flat ground is in the range of 0.9-1;

[0192] The weight measured by the weighing unit 5 is between 10 and 30 grams, and the cleanliness factor of the photovoltaic panel on the flat ground is between 0.7 and 0.9;

[0193] Shanghai 2024-11487(M240487CNI)

[0194] The weight measured by the weighing unit 5 is between 30 and 50 grams, and the cleanliness factor of the photovoltaic panel on the flat ground is between 0.6 and 0.7;

[0195] If the weight measured by weighing unit 5 is greater than 50 grams, the cleanliness factor of the photovoltaic panel on flat ground is in the range of 0.4-0.6;

[0196] Optionally, the cleanliness coefficient obtained by mapping the weighing unit 5 is used as the cleanliness coefficient of each photovoltaic string connected to the combiner box 4 on the flat ground, and the cleanliness is only measured at the combiner box 4 on the flat ground, avoiding the equipment complexity and data complexity of multiple measurements.

[0197] Optionally, on flat land, the controller in the control room of the photovoltaic power station obtains the meteorological temperature from the meteorological department. Because there is no difference between the windward and leeward sides of the slope on flat land, the heat dissipation and cleanliness conditions on flat land are the same. Therefore, there is no need to measure the temperature of each photovoltaic string independently, but only need to use the same meteorological temperature.

[0198] Optionally, the flat ground data includes the wind speed measured by the anemometer 1 of each photovoltaic string, the meteorological temperature, the light radiation intensity measured by the light radiation intensity meter 3, the cleanliness factor measured by the junction box 4 at the flat ground where the photovoltaic string is connected, and the photovoltaic string power generation.

[0199] It can be understood that slopes are hills, sand dunes or hills, and flat land is plains or flat areas with small elevation differences.

[0200] S2, the controller in the photovoltaic power station control room groups the acquired data;

[0201] For slopes:

[0202] The photovoltaic strings with wind speed ≤ 1 m / s in the photovoltaic power station are divided into the slope low wind group within the station;

[0203] The photovoltaic strings in the photovoltaic power station with wind speed of 1m / s<≦5m / s are divided into the slope wind group within the station;

[0204] The photovoltaic strings with wind speeds > 5 m / s in the photovoltaic power station are divided into the high wind group on the slope within the station;

[0205] The data of each PV string in the low-wind group, medium-wind group and high-wind group on the slopes within the station include wind speed, temperature, light radiation intensity and PV string power generation;

[0206] For flat land:

[0207] The photovoltaic strings with wind speed ≤ 5 m / s in the photovoltaic power station are divided into the flat and slow wind group in the station;

[0208] The photovoltaic strings with wind speed > 5 m / s in the photovoltaic power station are divided into flat ground and wind groups within the station;

[0209] The data of each photovoltaic string in the flat and slow wind group in the station includes wind speed, meteorological temperature, light radiation intensity, cleanliness coefficient and photovoltaic string power generation;

[0210] Shanghai 2024-11487(M240487CNI)

[0211] The data of each PV string in the flat land and wind group in the station include wind speed, meteorological temperature, light radiation intensity and PV string power generation;

[0212] For the flat and slow wind group within the station with a wind speed of ≤5 m / s, the dust and impurity diffusion conditions are poor, and thus the cleanliness of its photovoltaic panels has a greater impact on the power generation of the photovoltaic panels and the photovoltaic strings they belong to. Therefore, the cleanliness coefficient is collected for the flat and slow wind group within the station to consider the impact of dust and impurities on the power generation of the photovoltaic panels and improve the accuracy of the photovoltaic power generation.

[0213] S3, the controller in the photovoltaic power station control room trains the grouped data to obtain the optical power prediction value;

[0214] The controller in the control room of the photovoltaic power station contains multiple parallel DNN neural network modules (which can be DNN neural network modules one, two, three, four and five). The data of the low wind group on the slope within the station, the medium wind group on the slope within the station, the high wind group on the slope within the station, the slow wind group on the flat land within the station, and the flat land and wind group within the station within the statistical time are respectively input into independent DNN neural network modules, and the output power generation power of each DNN neural network module is accumulated to obtain the predicted value of optical power.

[0215] The method of the present application avoids the existing differences in operating temperature and surface cleanliness of photovoltaic panels on sloping and flat land, which results in differences in the power generation of different photovoltaic panels, and the deviation caused by prediction using a unified training model, resulting in inaccurate prediction values. The method of the present application finely divides and groups the power generation units of the photovoltaic power station based on sloping land, flat land and different wind speeds, with photovoltaic strings as units, and trains each group separately, thereby balancing the effects of differences in heating, cleanliness, heat dissipation and light radiation intensity of photovoltaic panels on the power generation, and improving the prediction accuracy of the power generation.

[0216] Optionally, the controller in the centralized control room of the photovoltaic power station is connected to each photovoltaic string in the photovoltaic power station for communication and data exchange, such as network communication or optical communication.

[0217] Optionally, the optical power prediction value may be 10 days or 4 hours.

[0218] Optionally, the above DNN neural network can also be replaced by other machine learning models.

[0219] A light power prediction system suitable for a photovoltaic power station comprises a photovoltaic power station centralized control room controller and a plurality of photovoltaic strings. The photovoltaic power station centralized control room controller communicates with each photovoltaic string in the photovoltaic power station and exchanges data.

[0220] Multiple photovoltaic panels in a photovoltaic power station are connected in series to form a photovoltaic string; multiple photovoltaic strings are connected in parallel to a combiner box.

[0221] For slopes:

[0222] Shanghai 2024-11487(M240487CNI)

[0223] A wind speed measuring instrument 1 is provided on the head photovoltaic panel of at least one photovoltaic string, and a thermometer 2 and a light radiation intensity meter 3 are provided on the tail photovoltaic panel;

[0224] A thermometer 2 and a light radiation intensity meter 3 are provided on the head photovoltaic panel of at least one photovoltaic string, and a wind speed measuring instrument 1 is provided on the tail photovoltaic panel;

[0225] A wind speed measuring instrument 1, a thermometer 2 and a light radiation intensity meter 3 are provided on the head end photovoltaic panel of at least one photovoltaic string;

[0226] At least one photovoltaic string is provided with a wind speed measuring instrument 1, a thermometer 2 and a light radiation intensity meter 3 at the tail end photovoltaic panel;

[0227] A wind speed measuring instrument 1 is provided on an intermediate photovoltaic panel of at least one photovoltaic string, and a thermometer 2 and a light radiation intensity meter 3 are provided on another intermediate photovoltaic panel; that is, the wind speed measuring instrument 1, the thermometer 2 and the light radiation intensity meter 3 are provided on adjacent or non-adjacent intermediate photovoltaic panels of the photovoltaic string, respectively, that is, the wind speed measuring instrument 1 is provided on one intermediate photovoltaic panel, and the thermometer 2 and the light radiation intensity meter 3 are provided on another intermediate photovoltaic panel at the same time;

[0228] The slope data includes the wind speed measured by the anemometer 1 of each photovoltaic string, the temperature measured by the thermometer 2, the light radiation intensity measured by the light radiation intensity meter 3, and the power generation power of the photovoltaic string.

[0229] For flat land:

[0230] A wind speed measuring instrument 1 is provided on the photovoltaic panel at the head end of at least one photovoltaic string, and a light radiation intensity meter 3 is provided on the photovoltaic panel at the tail end;

[0231] A light radiation intensity meter 3 is provided on the head photovoltaic panel of at least one photovoltaic string, and a wind speed meter 1 is provided on the tail photovoltaic panel;

[0232] A wind speed meter 1 and a light radiation intensity meter 3 are provided on the head end photovoltaic panel of at least one photovoltaic string;

[0233] A wind speed meter 1 and a light radiation intensity meter 3 are provided at the tail end photovoltaic panel of at least one photovoltaic string;

[0234] At least one middle photovoltaic panel of at least one photovoltaic string is provided with a wind speed measuring instrument 1, and another middle photovoltaic panel is provided with a light radiation intensity meter 3; that is, the wind speed measuring instrument 1 and the light radiation intensity meter 3 are respectively provided on the adjacent or non-adjacent middle photovoltaic panels of the photovoltaic string, that is, the wind speed measuring instrument 1 is provided in a middle Shanghai 2024-11487 (M240487CNI)

[0235] On the middle photovoltaic panel, the light radiation intensity meter 3 is arranged on another middle photovoltaic panel;

[0236] A weighing unit 5 is provided on the top of the junction box 4 at the flat ground, and the weighing unit 5 is used to measure the weight of dust and impurities to centrally map the cleanliness of the photovoltaic panels at the flat ground.

[0237] The mapping is:

[0238] If the weight measured by weighing unit 5 is less than 10 grams, the cleanliness factor of the photovoltaic panel on flat ground is in the range of 0.9-1;

[0239] The weight measured by the weighing unit 5 is between 10 and 30 grams, and the cleanliness factor of the photovoltaic panel on the flat ground is between 0.7 and 0.9;

[0240] The weight measured by the weighing unit 5 is between 30 and 50 grams, and the cleanliness factor of the photovoltaic panel on the flat ground is between 0.6 and 0.7;

[0241] If the weight measured by weighing unit 5 is greater than 50 grams, the cleanliness factor of the photovoltaic panel on flat ground is in the range of 0.4-0.6;

[0242] On flat land, the controller in the control room of the photovoltaic power station obtains the meteorological temperature from the meteorological department. Because there is no difference between the windward and leeward sides of the slope on flat land, the heat dissipation and cleanliness conditions on flat land are the same. Therefore, there is no need to measure the temperature of each photovoltaic string independently, but only need to use the same meteorological temperature.

[0243] The flat ground data includes the wind speed measured by the anemometer 1 of each photovoltaic string, the meteorological temperature, the light radiation intensity measured by the light radiation intensity meter 3, the cleanliness factor measured by the junction box 4 at the flat ground where the photovoltaic string is connected, and the photovoltaic string power generation.

[0244] Model selection:

[0245] A variety of machine learning algorithms can be used for prediction, such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Trees, or deep learning methods such as Long Short-Term Memory (LSTM) networks.

[0246] Those skilled 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 by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. 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 Shanghai 2024-11487 (M240487CNI) for each specific application

[0247] Different methods may be used to implement the described functionality, but such implementation should not be considered beyond the scope of the present disclosure.

[0248] It should be noted that the serial numbers of the embodiments of the present disclosure are only for description and do not represent the advantages or disadvantages of the embodiments.

[0249] The above are only preferred embodiments of the present disclosure, and the patent scope of the present disclosure is not limited thereto. Any equivalent structure or equivalent process transformation made using the contents of the present disclosure and the drawings, or directly or indirectly applied in other related technical fields, and the various embodiments or schemes can be arbitrarily combined, are all included in the patent protection scope of the present disclosure.

Claims

1. A method for predicting light power of photovoltaic power stations, applied in the field of green electricity hydrogen production, characterized in that: The method comprises: Acquire climate and environmental data of the photovoltaic power station in real time, wherein the climate and environmental data includes altitude data, slope data, time data, temperature data, humidity data, wind speed data and light intensity data; The climate and environment data are grouped according to the climate and environment data of the slope and the climate and environment data of the flat land; Preprocess the slope climate environment data and the flat climate environment data to obtain a data group 1 and a data group 2 aligned in time, wherein the data group 1 includes the slope environment data and the data group 2 includes the flat environment data; The power generation capacity is predicted based on the data set one and the data set two.

2. The method according to claim 1, characterized in that The data preprocessing of the slope climate environment data and the flat land climate environment data comprises: Data cleaning, processing of missing values ​​and outliers; Data standardization: standardize or normalize the cleaned data; Feature extraction, extracting target feature data from the standardized or normalized environmental data, wherein the target feature data includes time features, meteorological features, and geographical features; Data alignment: align the target feature data of the slope and the target feature data of the flat land in time.

3. The method according to claim 1, characterized in that The method further comprises: The performing of optical power prediction of the photovoltaic power station based on the data group 1 and the data group 2 comprises: Inputting the slope optical power prediction model and the flat optical power prediction model corresponding to the data group 1 and the data group 2 respectively obtains the slope optical power prediction value and the flat optical power prediction value; Based on the slope optical power prediction value and the flat land optical power prediction value, a total optical power theoretical prediction value is calculated.

4. The method according to claim 3, characterized in that The method further comprises: The optical power prediction value of the flat land optical power prediction model is input into the slope land optical power prediction model. Based on the input optical power prediction value of the flat land optical power prediction model, the slope land optical power prediction model is The prediction model outputs a first deviation value, where the first deviation value is the difference between the slope optical power prediction value and the flat land optical power prediction value; Inputting the optical power prediction value of the slope optical power prediction model into the flat land optical power prediction model, and based on the input optical power prediction value of the slope optical power prediction model, the flat land optical power prediction model outputs a second deviation value, wherein the second deviation value is the difference between the flat land optical power prediction value and the optical power prediction value of the slope optical power prediction model; Correct the slope optical power prediction value according to the first deviation value to obtain a first corrected slope optical power prediction value; Correct the predicted value of the flat-ground optical power according to the second deviation value to obtain a first corrected value of the predicted value of the flat-ground optical power; The first corrected value of the predicted light power of the photovoltaic power station is calculated based on the first corrected value of the predicted light power of the slope and the first corrected value of the predicted light power of the flat.

5. The method according to claim 4, characterized in that The method of correcting the slope optical power prediction value according to the first deviation value to obtain a first correction value of the slope optical power prediction value includes: the first correction value of the slope optical power prediction value=the slope optical power prediction value+K1*first deviation value, K1 first correction coefficient; The method of correcting the predicted value of the flat-ground optical power according to the second deviation value to obtain a first corrected value of the predicted value of the flat-ground optical power includes: the first corrected value of the predicted value of the flat-ground optical power=the predicted value of the flat-ground optical power+K2*the second deviation value, K2 the second correction coefficient; The method of calculating the first corrected value of the predicted light power of the photovoltaic power station based on the first corrected value of the predicted light power of the slope and the first corrected value of the predicted light power of the flat land comprises: summing the first corrected value of the predicted light power of the slope and the first corrected value of the predicted light power of the flat land to obtain the first corrected value of the predicted light power of the photovoltaic power station.

6. The method according to claim 5, characterized in that The method further comprises: Obtain the flat land optical power historical prediction data, slope optical power historical prediction data and photovoltaic power station optical power historical prediction data with the same time characteristics, meteorological characteristics and geographical characteristics as the data group 1 and the data group 2, the flat land optical power historical prediction data with the same time characteristics, meteorological characteristics and geographical characteristics Shanghai 2024-11487 (M240487CNI) The historical prediction data, the historical prediction data of slope optical power and the historical prediction data of photovoltaic power station optical power refer to the deviation within the preset range, and the optical power historical prediction data are the optical power theoretical prediction value data whose deviation between the optical power theoretical prediction value and the optical power actual value is within the preset range; The first correction value of the slope land optical power prediction value of the photovoltaic power station is compared with the slope land optical power historical prediction data to obtain a third deviation value, and the first correction value of the flat land optical power prediction value of the photovoltaic power station is compared with the flat land optical power historical prediction data to obtain a fourth deviation value; Determine a second correction value of the slope optical power prediction value according to the third deviation value, and determine a second correction value of the flat optical power prediction value according to the fourth deviation value, A second correction value of the predicted optical power value is calculated based on the second correction value of the predicted optical power value on the slope and the second correction value of the predicted optical power value on the flat land; Comparing the second correction value of the optical power prediction value with the historical optical power prediction data of the photovoltaic power station to obtain a fifth deviation value; When the fifth deviation value is less than or equal to the set deviation value, outputting a theoretical predicted value of the total optical power of the photovoltaic power station; If the fifth deviation value is greater than the set deviation value, the second correction value of the optical power prediction value is corrected to obtain a third correction value of the optical power prediction value, and the third correction value of the optical power prediction value is output as the theoretical prediction value of the total optical power of the photovoltaic power station.

7. The method according to any one of claims 3 to 6, characterized in that: The method further comprises: Obtain actual optical power data during the forecast period; The actual optical power data is compared with the theoretical predicted value of the total optical power of the photovoltaic power station to obtain a sixth deviation value. If the sixth deviation value is less than or equal to the set deviation value, the third correction value of the optical power prediction value is stored as historical data; if the sixth deviation value is greater than the set deviation value, the slope optical power prediction model is optimized according to the first deviation value and the third deviation value; and the flat optical power prediction model is optimized according to the second deviation value and the fourth deviation value.

8. The method according to any one of claims 3 to 6, characterized in that: The photovoltaic power station is provided with a plurality of photovoltaic strings, each of the photovoltaic strings comprises a plurality of photovoltaic panels arranged in series, and the plurality of photovoltaic panels are connected in parallel to a combiner box; Shanghai 2024-11487(M240487CNI) The obtaining of the current climate and environmental data of the photovoltaic power station includes obtaining environmental data of each photovoltaic string; Grouping the environmental data according to the climatic environmental data of the slope and the climatic environmental data of the flat land includes: The photovoltaic strings with wind speed less than or equal to the first preset value in the photovoltaic power station are divided into a low-wind group on the slope within the station; The photovoltaic strings in the photovoltaic power station with the first preset value < wind speed ≦ the second preset value are divided into the slope moderate wind group within the station; The photovoltaic strings with wind speeds greater than the second preset value in the photovoltaic power station are divided into the high wind group on the slope within the station; The data of each photovoltaic string in the in-station slope low wind group, the in-station slope medium wind group and the in-station slope high wind group include photovoltaic data and environmental data; Grouping the photovoltaic data and environmental data according to slope data and flat land data includes: The photovoltaic strings with wind speed ≤ the fourth preset value in the photovoltaic power station are divided into the flat ground slow wind group in the station; The photovoltaic strings with wind speed greater than the third preset value in the photovoltaic power station are divided into the flat ground and wind groups within the station.

9. The method according to claim 8, characterized in that The slope optical power prediction model and the flat land optical power prediction model respectively include multiple parallel DNN neural network modules. The slope low wind group, the slope medium wind group and the slope high wind group of each photovoltaic string within the statistical time are respectively input into the independent DNN neural network module of the slope optical power prediction model. The data of the flat land slow wind group and the flat land and wind group within the station are respectively input into the independent DNN neural network module of the flat land optical power prediction model. The output power generation of each DNN neural network module of the slope optical power prediction model and the flat land optical power prediction model are respectively accumulated to obtain the slope optical power prediction value and the flat land optical power prediction value.

10. A light power prediction system applicable to a photovoltaic power station, wherein the system executes the method according to any one of claims 1 to 9 when in operation, characterized in that: It includes a photovoltaic power station control room controller and multiple photovoltaic strings. The photovoltaic power station control room controller communicates with each photovoltaic string in the photovoltaic power station and exchanges data.

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