Intelligent Optical Power Prediction Method and Platform

Abstract

This invention relates to the field of photovoltaic (PV) power prediction technology, specifically disclosing an intelligent PV power prediction method and platform. The invention determines the prediction period by selecting multiple time points; performs radiometric matching between predicted meteorological data and preset historical data to determine multiple matching time periods, and extracts multiple matching meteorological data and multiple matching operational data; compares the predicted meteorological data with the multiple matching meteorological data to select a target time period; selects target operational data, and calculates the predicted PV power based on the target operational data and photovoltaic (PV) operational data. This method can perform radiometric matching and meteorological consistency comparison between predicted meteorological data and historical data at multiple time points, select the target time period corresponding to the prediction period, and then perform PV operation ratio processing to calculate the predicted PV power. It eliminates the need for model construction, avoiding the problems of complex model construction processes and weak generalization ability, and ensures the universality and accuracy of PV power prediction.

Description

Technical Field

[0001] This invention belongs to the field of optical power prediction technology, and particularly relates to intelligent optical power prediction methods and platforms. Background Technology

[0002] Photovoltaic power forecasting is the process of estimating and predicting the output power that a photovoltaic system may generate within a certain period of time in the future, based on current and historical meteorological data, environmental parameters, and the operating status of the photovoltaic system.

[0003] Photovoltaic power prediction is of great significance in photovoltaic power generation dispatch, grid load balancing and energy management, and can effectively improve the grid connection capacity of photovoltaic power generation and the stability of system operation.

[0004] Current optical power prediction technologies typically require the construction of relevant prediction models. The accuracy of these models is easily constrained by their quality, and the model construction process is complex with weak generalization ability. Continuous optimization of the models is also necessary, which cannot ensure the universality and accuracy of optical power prediction in real-world applications. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent optical power prediction method and platform, which aims to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] The intelligent optical power prediction method specifically includes the following steps:

[0008] Determine the forecast period, acquire the forecast meteorological data and photovoltaic operation data for the forecast period, and select multiple time points from the forecast period;

[0009] According to multiple time points, the predicted meteorological data is radiometrically matched with preset historical data to determine multiple matching time periods, and multiple matching meteorological data and multiple matching operational data are extracted;

[0010] The predicted meteorological data is compared with multiple matching meteorological data to determine the meteorological consistency, and a target time period is selected from the multiple matching time periods.

[0011] Based on the target time period, target operating data is selected from multiple matched operating data, and the predicted photovoltaic power is calculated based on the target operating data and the photovoltaic operating data.

[0012] As a further limitation of the technical solution of this embodiment of the invention, the step of determining the forecast period, obtaining the forecast meteorological data and photovoltaic operation data of the forecast period, and selecting multiple time points from the forecast period specifically includes the following steps:

[0013] Receive optical power prediction request;

[0014] The optical power prediction request is identified to determine the prediction time period and prediction location;

[0015] Based on the predicted location, obtain the predicted meteorological data for the predicted time period;

[0016] Obtain operation and maintenance management data;

[0017] The operation and maintenance management data is analyzed to extract photovoltaic operation data for the predicted period.

[0018] Get the location selection parameters;

[0019] Based on the location selection parameters, multiple time points are selected from the predicted time period.

[0020] As a further limitation of the technical solution of this invention embodiment, the step of performing radiation matching between the predicted meteorological data and preset historical data according to multiple time points, determining multiple matching time periods, and extracting multiple matching meteorological data and multiple matching operational data specifically includes the following steps:

[0021] Based on the multiple time points mentioned above, multiple predicted radiation data are extracted from the predicted meteorological data;

[0022] Based on multiple time points and multiple corresponding predicted radiation data, radiation matching is performed on preset historical data to determine multiple matching time periods;

[0023] Extract matching meteorological data corresponding to multiple matching time periods from the historical data;

[0024] Extract matching execution data corresponding to multiple matching time periods from the historical data.

[0025] As a further limitation of the technical solution of this embodiment of the invention, the step of comparing the predicted meteorological data with multiple matching meteorological data to select the target time period from the multiple matching time periods specifically includes the following steps:

[0026] Based on multiple time points and multiple preset meteorological factors, the predicted location factor data is extracted from the predicted meteorological data;

[0027] Based on multiple time points and multiple preset meteorological factors, extract multiple matching point factor data from multiple matching meteorological data;

[0028] The predicted location factor data is compared with multiple matching location factor data to calculate multiple matching comparison values;

[0029] Arrange the multiple matching comparison values ​​and select the target comparison value;

[0030] Based on the target comparison value, the corresponding target time period is selected from the multiple matching time periods.

[0031] As a further limitation of the technical solution of this embodiment of the invention, the calculation formula for the plurality of matching comparison values ​​is as follows:

[0032] ;

[0033] in, Representing the Matching time periods, Representing the Each time point, the prediction period, and multiple matching periods all have [various characteristics]. At a certain point in time, Representing the There are 10 meteorological factors, totaling 100 meteorological factors. One meteorological factor, For the prediction period and the first The matching comparison value for each matching time period. For the prediction period, the first The first time point The values ​​of each meteorological factor, For the first In the matching time period, the first The first time point The numerical values ​​of each meteorological factor.

[0034] As a further limitation of the technical solution of this embodiment of the invention, the step of selecting target operating data from multiple matching operating data according to the target time period, and calculating the predicted photovoltaic power according to the target operating data and the photovoltaic operating data specifically includes the following steps:

[0035] Based on the target time period, select the target running data from multiple matching running data;

[0036] The target operating data is analyzed to determine the target optical power and the target operating ratio;

[0037] The photovoltaic operation data is analyzed to determine the predicted operating ratio;

[0038] The optical power ratio is processed based on the target optical power, the target operating ratio, and the predicted operating ratio to calculate the predicted optical power.

[0039] As a further limitation of the technical solution of this embodiment of the invention, the formula for calculating the predicted optical power is as follows:

[0040] ;

[0041] in, To predict optical power, To predict the running rate, For the target optical power, The target running ratio.

[0042] An intelligent optical power prediction platform for executing any of the intelligent optical power prediction methods described above is characterized in that the platform specifically includes a prediction data acquisition module, a historical radiation matching module, a meteorological consistency comparison module, and an optical power prediction calculation module, wherein:

[0043] The forecast data acquisition module is used to determine the forecast period, acquire the forecast meteorological data and photovoltaic operation data of the forecast period, and select multiple time points from the forecast period.

[0044] The historical radiation matching module is used to perform radiation matching between the predicted meteorological data and preset historical data according to multiple time points, determine multiple matching time periods, and extract multiple matching meteorological data and multiple matching operational data.

[0045] The meteorological consistency comparison module is used to compare the predicted meteorological data with multiple matching meteorological data to select a target time period from the multiple matching time periods;

[0046] The photovoltaic power prediction calculation module is used to select target operating data from multiple matching operating data according to the target time period, and calculate the predicted photovoltaic power based on the target operating data and the photovoltaic operating data.

[0047] As a further limitation of the technical solution of this embodiment of the invention, the prediction data acquisition module specifically includes:

[0048] The request receiving unit is used to receive optical power prediction requests;

[0049] The request identification unit is used to identify the optical power prediction request and determine the prediction time period and prediction location;

[0050] A meteorological data acquisition unit is used to acquire predicted meteorological data for the predicted time period based on the predicted location;

[0051] The operation and maintenance data acquisition unit is used to acquire operation and maintenance management data;

[0052] The operation data extraction unit is used to analyze the operation and maintenance management data and extract the photovoltaic operation data for the predicted period.

[0053] Select the parameter acquisition unit to obtain the point selection parameters;

[0054] The time point selection unit is used to select multiple time points from the predicted time period according to the location selection parameters.

[0055] As a further limitation of the technical solution of this embodiment of the invention, the meteorological consistency comparison module specifically includes:

[0056] The prediction point factor data extraction unit is used to extract prediction point factor data from the prediction meteorological data according to multiple time points and multiple preset meteorological factors.

[0057] The matching point factor data extraction unit is used to extract multiple matching point factor data from multiple matching meteorological data according to multiple time points and multiple preset meteorological factors;

[0058] The matching comparison calculation unit is used to compare the predicted location factor data with multiple matching location factor data and calculate multiple matching comparison values.

[0059] The comparison value sorting unit is used to sort multiple matching comparison values ​​and select a target comparison value;

[0060] The target time period selection unit is used to select the corresponding target time period from multiple matching time periods based on the target comparison value.

[0061] Compared with the prior art, the beneficial effects of the present invention are:

[0062] (1) The present invention can perform radiation matching and meteorological consistency comparison between predicted meteorological data and historical data according to multiple time points, select the target time period corresponding to the predicted time period, and then perform photovoltaic operation ratio processing to calculate the predicted light power. There is no need to build a model, and there are no problems of complex model building process and weak generalization ability. It can also ensure the universality and accuracy of light power prediction.

[0063] (2) The present invention can extract multiple predicted radiation data from the predicted meteorological data according to multiple time points, and then perform radiation matching on the preset historical data according to multiple time points and multiple corresponding predicted radiation data. It can determine multiple matching time periods with the same radiation at multiple time points as the predicted time period, realize the coarse screening of historical time periods, reduce the amount of data for subsequent meteorological consistency comparison, and thus improve the data processing speed and efficiency.

[0064] (3) The present invention can compare the relevant data of the predicted time period with the data of multiple matching time periods according to multiple meteorological factors, calculate multiple comparison values, arrange the multiple comparison values, select the target time period and the corresponding target operation data, and compare and filter the target time period and target operation data that best match the predicted time period from the historical records, thereby providing an accurate reference data basis for subsequent optical power ratio processing and calculation. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0066] Figure 1 A flowchart illustrating the intelligent optical power prediction method provided in an embodiment of the present invention is shown.

[0067] Figure 2 The following is an application architecture diagram of the intelligent optical power prediction platform provided in an embodiment of the present invention. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0069] Understandably, in existing technologies, optical power prediction usually requires the construction of relevant prediction models. Optical power prediction is then based on these models, and the accuracy of the prediction is easily constrained by the quality of the models. Moreover, the model construction process is complex, has weak generalization ability, and requires continuous optimization. Therefore, it is impossible to ensure the universality and accuracy of optical power prediction in real-world applications.

[0070] To address the aforementioned issues, this invention provides an embodiment that determines a forecast period, acquires forecast meteorological data and photovoltaic (PV) operation data for that period, and selects multiple time points from the forecast period. Based on these multiple time points, the forecast meteorological data is radiometrically matched with preset historical data to determine multiple matching time periods, and multiple matching meteorological data and multiple matching operation data are extracted. The forecast meteorological data is then compared with the multiple matching meteorological data for meteorological consistency, and a target time period is selected from the multiple matching time periods. Based on the target time period, target operation data is selected from the multiple matching operation data, and the predicted photovoltaic power is calculated based on the target operation data and the PV operation data. This method enables the radiometric matching and meteorological consistency comparison of forecast meteorological data with historical data at multiple time points, selecting the target time period corresponding to the forecast period, and then performing PV operation ratio processing to calculate the predicted photovoltaic power. This eliminates the need for model construction, avoiding the problems of complex model construction processes and weak generalization capabilities, and ensures the universality and accuracy of photovoltaic power prediction.

[0071] Figure 1 A flowchart illustrating the intelligent optical power prediction method provided in an embodiment of the present invention is shown.

[0072] Specifically, in a preferred embodiment of the present invention, the intelligent optical power prediction method includes the following steps:

[0073] Step S101: Determine the forecast period, obtain the forecast meteorological data and photovoltaic operation data for the forecast period, and select multiple time points from the forecast period.

[0074] In this embodiment of the invention, a solar power prediction request uploaded by a prediction manager is received. The prediction time period and prediction location are determined by identifying the solar power prediction request. Based on the prediction location, the predicted meteorological data for the prediction time period is obtained through meteorological channels. Then, the operation and maintenance management data of the photovoltaic power station is obtained. By analyzing the operation and maintenance management data, the photovoltaic operation data for the prediction time period is extracted, and the location selection parameters are obtained. Based on the location selection parameters, multiple time points are selected from the prediction time period.

[0075] It is understandable that the forecast period is a future time period, and the longest interval between the forecast period and the current time cannot exceed the preset standard interval (e.g., 3 days) to ensure the accuracy of the obtained forecast period; the forecast location is the geographical location of the photovoltaic power station.

[0076] It is understandable that meteorological channels could include the National Meteorological Administration, meteorological observatory websites, ECMWF, NOAA, IBM The Weather Company, AccuWeather, and / or Weather Underground, etc.

[0077] Understandably, the operation and maintenance management data records the operation and maintenance status of multiple photovoltaic panels in a photovoltaic power station at different times, including normal operation, abnormal damage, cleaning and maintenance (cleaning dust, bird droppings, fallen leaves and other pollutants from the surface of the photovoltaic panels), and inspection and maintenance (inspecting the photovoltaic panels and related components for cracks, aging, loosening, deformation and other problems).

[0078] It is understood that, in this embodiment of the invention, the point selection parameter is the interval between adjacent time points (e.g., 1 hour).

[0079] Step S102: According to multiple time points, perform radiation matching between the predicted meteorological data and preset historical data to determine multiple matching time periods, and extract multiple matching meteorological data and multiple matching operational data.

[0080] In this embodiment of the invention, predicted radiation data corresponding to multiple time points are extracted from the predicted meteorological data, and radiation matching is performed on preset historical data according to the multiple time points and the multiple corresponding predicted radiation data to determine multiple matching time periods. Then, matching meteorological data corresponding to multiple matching time periods are extracted from the historical data, and matching operational data corresponding to multiple matching time periods are extracted from the historical data.

[0081] It is understandable that, on a 24-hour timescale, multiple matching time periods are the same as the predicted time period, and multiple corresponding time points within the predicted time period and multiple matching time periods have the same radiation levels. For example, if the predicted time period and multiple matching time periods are both from 10:00 to 14:00, then the radiation levels at 10:00, 11:00, 12:00, 13:00, and 14:00 are the same.

[0082] Step S103: Compare the predicted meteorological data with multiple matching meteorological data to determine the meteorological consistency, and select the target time period from the multiple matching time periods.

[0083] In this embodiment of the invention, based on multiple time points and multiple preset meteorological factors, relevant data extraction is performed on predicted meteorological data and multiple matching meteorological data. From the predicted meteorological data, predicted location factor data for multiple meteorological factors at multiple time points is extracted, and from the multiple matching meteorological data, multiple matching location factor data for multiple meteorological factors at multiple time points is extracted. By comparing the predicted location factor data with the multiple matching location factor data, multiple comparison values ​​are calculated. These multiple comparison values ​​are then arranged, and the largest comparison value is selected and marked as the target comparison value. Furthermore, from multiple matching time periods, the matching time period corresponding to the target comparison value is selected and marked as the target time period. Specifically, the calculation formula for the multiple comparison values ​​is as follows:

[0084] ;

[0085] in, Representing the Matching time periods, Representing the Each time point, the prediction period, and multiple matching periods all have [various characteristics]. At a certain point in time, Representing the There are 10 meteorological factors, totaling 100 meteorological factors. One meteorological factor, For the prediction period and the first The matching comparison value for each matching time period. For the prediction period, the first The first time point The values ​​of each meteorological factor, For the first In the matching time period, the first The first time point The numerical values ​​of each meteorological factor.

[0086] It is understandable that multiple matching point factor data correspond to multiple matching meteorological data.

[0087] It is understandable that during the calculation of multiple matching comparison values, through It can record each point in time. The values ​​of each meteorological factor were compared and converted into values ​​ranging from (0,1). Values ​​closer to 1 indicate a closer match at the time point, while values ​​closer to 0 indicate a less consistent match. Then... The average value is calculated by overlaying the data at each time point and then calculating the sum. The correlation comparison value for each time point is as follows: the closer it is to 1, the more consistent the corresponding matching time period is with the predicted time period; the closer it is to 0, the less consistent the corresponding matching time period is with the predicted time period.

[0088] Step S104: Select target operating data from multiple matching operating data according to the target time period, and calculate the predicted photovoltaic power based on the target operating data and the photovoltaic operating data.

[0089] In this embodiment of the invention, matching operating data corresponding to a target time period is selected from multiple matching operating data sets and marked as target operating data. The target operating data is then analyzed to determine the target photovoltaic power and target operating ratio for the target time period. Furthermore, the photovoltaic operating data is analyzed to determine the predicted operating ratio. Based on the target photovoltaic power, target operating ratio, and predicted operating ratio, the photovoltaic power ratio for the predicted time period is processed to calculate the predicted photovoltaic power for that period. Specifically, the formula for calculating the predicted photovoltaic power is as follows:

[0090] ;

[0091] in, To predict optical power, To predict the running rate, For the target optical power, The target running ratio.

[0092] Understandably, since the comparison value between the target period and the forecast period is the largest, it is determined that the weather conditions of the target period and the forecast period are most consistent. Therefore, it can be determined that the power generation of a unit photovoltaic panel is the same in the target period and the forecast period. Then, according to the different ratios of normal operation of photovoltaic panels in the target period and the forecast period, the predicted photovoltaic power corresponding to the operation and maintenance status of the forecast period can be calculated.

[0093] Furthermore, Figure 2 The following is an application architecture diagram of the intelligent optical power prediction platform provided in an embodiment of the present invention.

[0094] In another preferred embodiment of the present invention, the intelligent optical power prediction platform specifically includes:

[0095] The forecast data acquisition module 101 is used to determine the forecast period, acquire the forecast meteorological data and photovoltaic operation data of the forecast period, and select multiple time points from the forecast period.

[0096] In this embodiment of the invention, the prediction data acquisition module 101 receives a solar power prediction request uploaded by the prediction management personnel. By identifying the solar power prediction request, the prediction period and prediction location are determined. Based on the prediction location, the prediction meteorological data for the prediction period is obtained through meteorological channels. Then, the operation and maintenance management data of the photovoltaic power station is obtained. By analyzing the operation and maintenance management data, the photovoltaic operation data for the prediction period is extracted, and the location selection parameters are obtained. Based on the location selection parameters, multiple time points are selected from the prediction period.

[0097] Specifically, in a preferred embodiment provided by the present invention, the prediction data acquisition module 101 specifically includes:

[0098] The request receiving unit is used to receive optical power prediction requests;

[0099] The request identification unit is used to identify the optical power prediction request and determine the prediction time period and prediction location;

[0100] A meteorological data acquisition unit is used to acquire predicted meteorological data for the predicted time period based on the predicted location;

[0101] The operation and maintenance data acquisition unit is used to acquire operation and maintenance management data;

[0102] The operation data extraction unit is used to analyze the operation and maintenance management data and extract the photovoltaic operation data for the predicted period.

[0103] Select the parameter acquisition unit to obtain the point selection parameters;

[0104] The time point selection unit is used to select multiple time points from the predicted time period according to the location selection parameters.

[0105] Furthermore, the intelligent optical power prediction platform also includes:

[0106] The historical radiation matching module 102 is used to perform radiation matching between the predicted meteorological data and the preset historical data according to multiple time points, determine multiple matching time periods, and extract multiple matching meteorological data and multiple matching operational data.

[0107] In this embodiment of the invention, the historical radiation matching module 102 extracts predicted radiation data corresponding to multiple time points from the predicted meteorological data, and performs radiation matching on the preset historical data according to the multiple time points and the multiple corresponding predicted radiation data to determine multiple matching time periods. Then, it extracts the matching meteorological data corresponding to the multiple matching time periods from the historical data, and extracts the matching operation data corresponding to the multiple matching time periods from the historical data.

[0108] The meteorological consistency comparison module 103 is used to compare the predicted meteorological data with multiple matching meteorological data and select a target time period from the multiple matching time periods.

[0109] In this embodiment of the invention, the meteorological consistency comparison module 103 extracts relevant data from predicted meteorological data and multiple matching meteorological data according to multiple time points and multiple preset meteorological factors. From the predicted meteorological data, it extracts predicted location factor data for multiple meteorological factors at multiple time points, and from the multiple matching meteorological data, it extracts multiple matching location factor data for multiple meteorological factors at multiple time points. By comparing the predicted location factor data with the multiple matching location factor data, it calculates multiple consistency comparison values, arranges the multiple consistency comparison values, selects the largest consistency comparison value, and marks it as the target comparison value. Then, from multiple matching time periods, it selects the matching time period corresponding to the target comparison value and marks it as the target time period. Specifically, the calculation formula for the multiple consistency comparison values ​​is as follows:

[0110] ;

[0111] in, Representing the Matching time periods, Representing the Each time point, the prediction period, and multiple matching periods all have [various characteristics]. At a certain point in time, Representing the There are 10 meteorological factors, totaling 100 meteorological factors. One meteorological factor, For the prediction period and the first The matching comparison value for each matching time period. For the prediction period, the first The first time point The values ​​of each meteorological factor, For the first In the matching time period, the first The first time point The numerical values ​​of each meteorological factor.

[0112] Specifically, in the preferred embodiment provided by the present invention, the meteorological consistency comparison module 103 specifically includes:

[0113] The prediction point factor data extraction unit is used to extract prediction point factor data from the prediction meteorological data according to multiple time points and multiple preset meteorological factors.

[0114] The matching point factor data extraction unit is used to extract multiple matching point factor data from multiple matching meteorological data according to multiple time points and multiple preset meteorological factors;

[0115] The matching comparison calculation unit is used to compare the predicted location factor data with multiple matching location factor data and calculate multiple matching comparison values.

[0116] The comparison value sorting unit is used to sort multiple matching comparison values ​​and select a target comparison value;

[0117] The target time period selection unit is used to select the corresponding target time period from multiple matching time periods based on the target comparison value.

[0118] Furthermore, the intelligent optical power prediction platform also includes:

[0119] The photovoltaic power prediction calculation module 104 is used to select target operating data from multiple matching operating data according to the target time period, and calculate the predicted photovoltaic power based on the target operating data and the photovoltaic operating data.

[0120] In this embodiment of the invention, the photovoltaic power prediction calculation module 104 selects the matching operating data corresponding to the target time period from multiple matching operating data, marks it as the target operating data, analyzes the target operating data to determine the target photovoltaic power and target operating ratio for the target time period, and analyzes the photovoltaic operating data to determine the predicted operating ratio. Then, based on the target photovoltaic power, target operating ratio, and predicted operating ratio, the photovoltaic power ratio is processed for the prediction time period to calculate the predicted photovoltaic power for the prediction time period. Specifically, the formula for calculating the predicted photovoltaic power is as follows:

[0121] ;

[0122] in, To predict optical power, To predict the running rate, For the target optical power, The target running ratio.

[0123] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A smart optical power prediction method, characterized in that, The method specifically includes the following steps: Determine the forecast period, acquire the forecast meteorological data and photovoltaic operation data for the forecast period, and select multiple time points from the forecast period; According to multiple time points, the predicted meteorological data is radiometrically matched with preset historical data to determine multiple matching time periods, and multiple matching meteorological data and multiple matching operational data are extracted; The predicted meteorological data is compared with multiple matching meteorological data to determine the meteorological consistency, and a target time period is selected from the multiple matching time periods. Based on the target time period, target operating data is selected from multiple matching operating data, and the predicted photovoltaic power is calculated based on the target operating data and the photovoltaic operating data. The step of performing radiometric matching between the predicted meteorological data and preset historical data according to multiple time points, determining multiple matching time periods, and extracting multiple matching meteorological data and multiple matching operational data specifically includes the following steps: Based on the multiple time points mentioned above, multiple predicted radiation data are extracted from the predicted meteorological data; Based on multiple time points and multiple corresponding predicted radiation data, radiation matching is performed on preset historical data to determine multiple matching time periods; Extract matching meteorological data corresponding to multiple matching time periods from the historical data; Extract matching execution data corresponding to multiple matching time periods from the historical data; The step of comparing the predicted meteorological data with multiple matching meteorological data to select a target time period from the multiple matching time periods specifically includes the following steps: Based on multiple time points and multiple preset meteorological factors, the predicted location factor data is extracted from the predicted meteorological data; Based on multiple time points and multiple preset meteorological factors, extract multiple matching point factor data from multiple matching meteorological data; The predicted location factor data is compared with multiple matching location factor data to calculate multiple matching comparison values; Arrange the multiple matching comparison values ​​and select the target comparison value; Based on the target comparison value, select the corresponding target time period from the multiple matching time periods; The formula for calculating multiple matching comparison values ​​is as follows: ； in, Representing the Matching time periods, Representing the Each time point, the prediction period, and multiple matching periods all have [various characteristics]. At a certain point in time, Representing the There are 10 meteorological factors, totaling 100 meteorological factors. One meteorological factor, For the prediction period and the first The matching comparison value for each matching time period. For the prediction period, the first The first time point The values ​​of each meteorological factor, For the first In the matching time period, the first The first time point The numerical values ​​of each meteorological factor.

2. The intelligent optical power prediction method according to claim 1, characterized in that, The process of determining the forecast period, acquiring forecast meteorological data and photovoltaic operation data for the forecast period, and selecting multiple time points from the forecast period specifically includes the following steps: Receive optical power prediction request; The optical power prediction request is identified to determine the prediction time period and prediction location; Based on the predicted location, obtain the predicted meteorological data for the predicted time period; Obtain operation and maintenance management data; The operation and maintenance management data is analyzed to extract photovoltaic operation data for the predicted period. Get the location selection parameters; Based on the location selection parameters, multiple time points are selected from the predicted time period.

3. The intelligent optical power prediction method according to claim 1, characterized in that, The step of selecting target operating data from multiple matching operating data according to the target time period, and calculating the predicted photovoltaic power based on the target operating data and the photovoltaic operating data specifically includes the following steps: Based on the target time period, select the target running data from multiple matching running data; The target operating data is analyzed to determine the target optical power and the target operating ratio; The photovoltaic operation data is analyzed to determine the predicted operating ratio; The optical power ratio is processed based on the target optical power, the target operating ratio, and the predicted operating ratio to calculate the predicted optical power.

4. The intelligent optical power prediction method according to claim 3, characterized in that, The formula for calculating the predicted optical power is as follows: ； in, To predict optical power, To predict the running rate, For the target optical power, The target running ratio.

5. An intelligent optical power prediction platform for executing the intelligent optical power prediction method as described in any one of claims 1 to 4, characterized in that, The platform specifically includes a prediction data acquisition module, a historical radiation matching module, a meteorological consistency comparison module, and a light power prediction calculation module, wherein: The forecast data acquisition module is used to determine the forecast period, acquire the forecast meteorological data and photovoltaic operation data of the forecast period, and select multiple time points from the forecast period. The historical radiation matching module is used to perform radiation matching between the predicted meteorological data and preset historical data according to multiple time points, determine multiple matching time periods, and extract multiple matching meteorological data and multiple matching operational data. The meteorological consistency comparison module is used to compare the predicted meteorological data with multiple matching meteorological data to select a target time period from the multiple matching time periods; The photovoltaic power prediction calculation module is used to select target operating data from multiple matching operating data according to the target time period, and calculate the predicted photovoltaic power based on the target operating data and the photovoltaic operating data.

6. The intelligent optical power prediction platform according to claim 5, characterized in that, The prediction data acquisition module specifically includes: The request receiving unit is used to receive optical power prediction requests; The request identification unit is used to identify the optical power prediction request and determine the prediction time period and prediction location; A meteorological data acquisition unit is used to acquire predicted meteorological data for the predicted time period based on the predicted location; The operation and maintenance data acquisition unit is used to acquire operation and maintenance management data; The operation data extraction unit is used to analyze the operation and maintenance management data and extract the photovoltaic operation data for the predicted period. Select the parameter acquisition unit to obtain the point selection parameters; The time point selection unit is used to select multiple time points from the predicted time period according to the location selection parameters.

7. The intelligent optical power prediction platform according to claim 5, characterized in that, The meteorological consistency comparison module specifically includes: The prediction point factor data extraction unit is used to extract prediction point factor data from the prediction meteorological data according to multiple time points and multiple preset meteorological factors. The matching point factor data extraction unit is used to extract multiple matching point factor data from multiple matching meteorological data according to multiple time points and multiple preset meteorological factors; The matching comparison calculation unit is used to compare the predicted location factor data with multiple matching location factor data and calculate multiple matching comparison values. The comparison value sorting unit is used to sort multiple matching comparison values ​​and select a target comparison value; The target time period selection unit is used to select the corresponding target time period from multiple matching time periods based on the target comparison value.

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