Intelligent optical power prediction method and platform

Abstract

The invention relates to the technical field of optical power prediction, and particularly discloses an intelligent optical power prediction method and platform. According to the method, a plurality of time points are selected by determining a prediction time period; performing radiation matching on the predicted meteorological data and preset historical record data, determining a plurality of matching time periods, and extracting a plurality of matched meteorological data and a plurality of matched operation data; carrying out meteorological conformance comparison on the predicted meteorological data and the plurality of matched meteorological data, and selecting a target time period; and selecting target operation data, and calculating the predicted optical power according to the target operation data and the photovoltaic operation data. According to a plurality of time points, radiation matching and meteorological conformance comparison can be carried out on predicted meteorological data and historical record data, a target time period corresponding to a prediction time period is selected, then ratio processing of photovoltaic operation is carried out, and predicted optical power is calculated, so that a model does not need to be constructed, and the problems of complex model construction process and weak generalization ability are solved; and the universality and accuracy of optical power prediction can be ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of optical power prediction, and in particular relates to an intelligent optical power prediction method and platform. Background Art

[0002] Optical power prediction 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] Optical power prediction is of great significance in photovoltaic power generation scheduling, grid load balancing and energy management, and can effectively improve the access capability of photovoltaic power generation and the stability of system operation.

[0004] Optical power prediction in existing technologies usually requires the construction of relevant prediction models. The accuracy of optical power prediction based on the constructed models is easily restricted by the quality of the models. In addition, the model construction process is complex and the generalization ability is weak. The model also needs to be continuously optimized. Therefore, the universality and accuracy of optical power prediction cannot be ensured in real applications. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide an intelligent optical power prediction method and platform, aiming to solve the problems raised in the background technology.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: The intelligent optical power prediction method comprises the following steps: Determining a forecast period, obtaining forecast meteorological data and photovoltaic operation data for the forecast period, and selecting multiple time points from the forecast period; According to the multiple time points, the predicted meteorological data is radially matched with the preset historical record data to determine multiple matching time periods, and multiple matching meteorological data and multiple matching operation data are extracted; Comparing the predicted meteorological data with the plurality of matching meteorological data for meteorological consistency, and selecting a target time period from the plurality of matching time periods; According to the target time period, target operation data is selected from the plurality of matching operation data, and the predicted light power is calculated based on the target operation data and the photovoltaic operation data.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, determining the forecast period, obtaining forecast meteorological data and photovoltaic operation data for the forecast period, and selecting multiple time points from the forecast period specifically include the following steps: receiving an optical power prediction request; Identifying the optical power prediction request and determining a prediction period and a prediction location; Obtaining forecasted meteorological data for the forecast period according to the forecast location; Obtain operation and maintenance management data; Analyzing the operation and maintenance management data to extract photovoltaic operation data for the forecast period; Get point selection parameters; A plurality of time points are selected from the prediction period according to the point selection parameters.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the step of performing radial matching of the predicted meteorological data with preset historical record data according to the multiple time points, determining multiple matching time periods, and extracting multiple matching meteorological data and multiple matching operation data specifically includes the following steps: Extracting a plurality of predicted radiation data from the predicted meteorological data according to the plurality of time points; According to multiple time points and multiple corresponding predicted radiation data, radiation matching is performed in preset historical record data to determine multiple matching time periods; Extracting matching meteorological data corresponding to a plurality of matching time periods from the historical record data; Matching operation data corresponding to a plurality of matching time periods are extracted from the historical record data.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the comparing the predicted meteorological data with the plurality of matching meteorological data for meteorological consistency and selecting a target time period from the plurality of matching time periods specifically comprises the following steps: Extracting prediction point factor data from the predicted meteorological data according to the plurality of time points and the plurality of preset meteorological factors; Extracting a plurality of matching point factor data from the plurality of matching meteorological data according to the plurality of time points and the plurality of preset meteorological factors; Comparing the predicted point factor data with a plurality of matching point factor data, and calculating a plurality of matching comparison values; Arranging the plurality of matching comparison values ​​and selecting a target comparison value; A corresponding target time period is selected from the plurality of matching time periods according to the target comparison value.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the calculation formula of the multiple matching comparison values ​​is: ; in, Representative Matching periods, Representative time points, the prediction period and multiple matching periods have A point in time, Representative There are a total of meteorological factors Meteorological factors, For the forecast period and The matching comparison value of the matching period, The first The first time point The value of the meteorological factor, For the Matching period The first time point The value of a meteorological factor.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, selecting target operating data from the plurality of matching operating data according to the target time period, and calculating the predicted light power according to the target operating data and the photovoltaic operating data specifically includes the following steps: selecting target operating data from the plurality of matching operating data according to the target time period; Analyzing the target operation data to determine a target optical power and a target operation ratio; Analyzing the photovoltaic operation data to determine a predicted operation ratio; Optical power ratio processing is performed according to the target optical power, the target operating ratio, and the predicted operating ratio to calculate the predicted optical power.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the calculation formula for the predicted optical power is: ; in, To predict the optical power, To predict the running ratio, is the target optical power, is the target operating ratio.

[0013] An intelligent optical power prediction platform for executing any of the above intelligent optical power prediction methods is characterized in that the platform specifically includes a prediction data acquisition module, a historical radiation matching module, a meteorological coincidence comparison module, and an optical power prediction calculation module, wherein: A forecast data acquisition module is used to determine a forecast period, acquire forecast meteorological data and photovoltaic operation data for the forecast period, and select multiple time points from the forecast period; A historical radiation matching module is used to perform radiation matching on the predicted meteorological data and preset historical record data according to the multiple time points, determine multiple matching time periods, and extract multiple matching meteorological data and multiple matching operation data; A meteorological consistency comparison module is used to compare the predicted meteorological data with the plurality of matching meteorological data for meteorological consistency, and select a target time period from the plurality of matching time periods; The optical power prediction calculation module is used to select target operation data from the multiple matching operation data according to the target time period, and calculate the predicted optical power according to the target operation data and the photovoltaic operation data.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the prediction data acquisition module specifically includes: A request receiving unit, configured to receive an optical power prediction request; a request identification unit, configured to identify the optical power prediction request and determine a prediction period and a prediction position; A meteorological data acquisition unit, configured to acquire the predicted meteorological data for the predicted period according to the predicted location; Operation and maintenance data acquisition unit, used to acquire operation and maintenance management data; an operation data extraction unit, configured to analyze the operation and maintenance management data and extract photovoltaic operation data for the forecast period; Selection parameter acquisition unit, used to obtain point selection parameters; A time point selection unit is used to select multiple time points from the prediction period according to the point selection parameters.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the meteorological match comparison module specifically includes: A prediction point factor data extraction unit, configured to extract prediction point factor data from the predicted meteorological data according to the plurality of time points and the plurality of preset meteorological factors; a matching point factor data extraction unit, configured to extract a plurality of matching point factor data from the plurality of matching meteorological data according to the plurality of time points and the plurality of preset meteorological factors; a matching comparison calculation unit, configured to compare the predicted point location factor data with a plurality of matching point location factor data, and calculate a plurality of matching comparison values; a comparison value arranging unit, configured to arrange the plurality of matching comparison values ​​and select a target comparison value; The target period selection unit is configured to select a corresponding target period from the plurality of matching periods according to the target comparison value.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention can compare the predicted meteorological data with the historical record data for radiation matching and meteorological consistency at multiple time points, select the target period corresponding to the predicted 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 such as complex model building process and weak generalization ability. In addition, the present invention can ensure the versatility and accuracy of light power prediction. (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 record data according to the multiple time points and the multiple corresponding predicted radiation data. It can determine multiple matching time periods that have the same radiation as the predicted time period at multiple time points, realize the coarse screening of historical time periods, reduce the amount of data for subsequent meteorological matching comparisons, and thus improve data processing speed and efficiency; (3) The present invention can compare the relevant data of the forecast period with that of multiple matching periods according to multiple meteorological factors, calculate multiple matching comparison values, and then arrange the multiple matching comparison values ​​to select the target period and the corresponding target operation data. It can compare and select the target period and target operation data that best match the forecast period from historical records, thereby providing an accurate reference data basis for subsequent optical power ratio processing and calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0018] Figure 1 The figure shows a flow chart of an intelligent optical power prediction method provided by an embodiment of the present invention.

[0019] Figure 2 The application architecture diagram of the intelligent optical power prediction platform provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present 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 only used to explain the present invention and are not intended to limit the present invention.

[0021] It is understandable that in the existing technology, optical power prediction usually requires the construction of a relevant prediction model. The accuracy of the prediction is easily restricted by the quality of the model. Moreover, the model construction process is complex and the generalization ability is weak. The model needs to be continuously optimized later, which cannot ensure the universality and accuracy of optical power prediction in actual applications.

[0022] To address the above-mentioned issues, an embodiment of the present invention determines a prediction period, obtains predicted meteorological data and photovoltaic operation data for the prediction period, and selects multiple time points from the prediction period; performs radiation matching on the predicted meteorological data with preset historical records at the multiple time points, determines multiple matching periods, and extracts multiple matching meteorological data and multiple matching operation data; compares the predicted meteorological data with the multiple matching meteorological data for meteorological consistency, and selects a target period from the multiple matching periods; selects target operation data from the multiple matching operation data based on the target period, and calculates predicted optical power based on the target operation data and the photovoltaic operation data. This method can perform radiation matching and meteorological consistency comparison on the predicted meteorological data with the historical records at multiple time points, select a target period corresponding to the prediction period, and then perform photovoltaic operation ratio processing to calculate the predicted optical power. This method eliminates the need for model construction, avoids the problems of complex model construction process and weak generalization ability, and ensures the versatility and accuracy of optical power prediction.

[0023] Figure 1 The figure shows a flow chart of an intelligent optical power prediction method provided by an embodiment of the present invention.

[0024] Specifically, in a preferred embodiment of the present invention, the intelligent optical power prediction method comprises the following steps: Step S101 : determining a forecast period, obtaining forecast meteorological data and photovoltaic operation data for the forecast period, and selecting multiple time points from the forecast period.

[0025] In an embodiment of the present invention, a light power prediction request uploaded by a prediction management personnel is received, the light power prediction request is identified, the prediction period and prediction location are determined, and according to the prediction location, the predicted meteorological data of the prediction period is obtained through meteorological channels, and 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 of the prediction period is extracted, and the point selection parameters are obtained. According to the point selection parameters, multiple time points are selected from the prediction period.

[0026] It is understandable that the prediction period is a time period in the future, and the longest interval between the prediction period and the current time cannot exceed the preset standard interval (for example: 3 days), so as to ensure the accuracy of the obtained prediction period; the prediction location is the geographical location of the photovoltaic power station.

[0027] It is understood that the weather channel may be the National Weather Service, a weather station website, ECMWF, NOAA, IBM The Weather Company, AccuWeather and / or Weather Underground, etc.

[0028] It is understandable that 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 on the surface of photovoltaic panels), and inspection and maintenance (checking photovoltaic panels and related components for cracks, aging, looseness, deformation and other problems).

[0029] It is understandable that, in the embodiment of the present invention, the point selection parameter is the interval time between adjacent time points (for example, 1 hour).

[0030] Step S102 : performing radial matching on the predicted meteorological data and the preset historical record data according to the multiple time points, determining multiple matching time periods, and extracting multiple matching meteorological data and multiple matching operation data.

[0031] In an embodiment of the present invention, predicted radiation data corresponding to multiple time points are extracted from the predicted meteorological data, and radiation matching is performed in the preset historical record data according to the multiple time points and the multiple corresponding predicted radiation data to determine multiple matching time periods, and then matching meteorological data corresponding to the multiple matching time periods are extracted from the historical record data, and matching operation data corresponding to the multiple matching time periods are extracted from the historical record data.

[0032] It can be understood that on the 24-hour time scale, multiple matching time periods are the same as the prediction time period, and in the prediction time period and multiple matching time periods, multiple corresponding time points have the same radiation amount. For example, the prediction time period and multiple matching time periods are both 10:00-14:00, among which the radiation amount of the prediction time period and multiple matching time periods at 10:00 is the same, the radiation amount of the prediction time period and multiple matching time periods at 11:00 is the same, the radiation amount of the prediction time period and multiple matching time periods at 12:00 is the same, the radiation amount of the prediction time period and multiple matching time periods at 13:00 is the same, and the radiation amount of the prediction time period and multiple matching time periods at 14:00 is the same.

[0033] Step S103 : performing meteorological consistency comparison between the predicted meteorological data and the plurality of matching meteorological data, and selecting a target time period from the plurality of matching time periods.

[0034] In an embodiment of the present invention, relevant data extraction is performed on the predicted meteorological data and the multiple matching meteorological data according to multiple time points and multiple preset meteorological factors. From the predicted meteorological data, predicted point factor data of the multiple meteorological factors at the multiple time points are extracted, and from the multiple matching meteorological data, multiple matching point factor data of the multiple meteorological factors at the multiple time points are extracted. By comparing the predicted point factor data with the multiple matching point factor data, multiple matching comparison values ​​are calculated, and then the multiple matching comparison values ​​are arranged, and the largest matching comparison value is selected and marked as the target comparison value. Then, from the 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 matching comparison values ​​is: ; in, Representative Matching periods, Representative time points, the prediction period and multiple matching periods have A point in time, Representative There are a total of meteorological factors Meteorological factors, For the forecast period and The matching comparison value of the matching period, The first The first time point The value of the meteorological factor, For the Matching period The first time point The value of a meteorological factor.

[0035] It can be understood that multiple matching point factor data correspond to multiple matching meteorological data.

[0036] It is understandable that in the calculation process of multiple matching comparison values, , which can convert each time point The values ​​of each meteorological factor are compared and converted into a value in the range of (0,1]. The closer to 1, the more consistent the time point is, and the closer to 0, the less consistent the time point is. The time points are superimposed and averaged, and the The closer the value is to 1, the more consistent the corresponding matching period is with the predicted period; the closer it is to 0, the less consistent the corresponding matching period is with the predicted period.

[0037] Step S104 : selecting target operating data from the plurality of matching operating data according to the target time period, and calculating predicted optical power according to the target operating data and the photovoltaic operating data.

[0038] In an embodiment of the present invention, matching operation data corresponding to a target period is selected from a plurality of matching operation data and marked as target operation data. The target operation data is then analyzed to determine the target optical power and target operation ratio of photovoltaic power generation in the target period. The photovoltaic operation data is also analyzed to determine the predicted operation ratio. Then, based on the target optical power, the target operation ratio, and the predicted operation ratio, optical power ratio processing is performed on the predicted period to calculate the predicted optical power of the predicted period. Specifically, the calculation formula for the predicted optical power is: ; in, To predict the optical power, To predict the running ratio, is the target optical power, is the target operating ratio.

[0039] It can be understood that since the comparison value of the target period and the predicted period is the largest, it is determined that the meteorological conditions of the target period and the predicted period are most consistent. Therefore, it can be determined that the power generation power of the unit photovoltaic panel in the target period and the predicted period is the same, and then according to the different ratios of the normal operation of the photovoltaic panels in the target period and the predicted period, the predicted light power corresponding to the operation and maintenance status of the predicted period is calculated.

[0040] Further, Figure 2 The application architecture diagram of the intelligent optical power prediction platform provided by an embodiment of the present invention is shown.

[0041] In another preferred embodiment of the present invention, the intelligent optical power prediction platform specifically includes: The forecast data acquisition module 101 is used to determine a forecast period, acquire forecast meteorological data and photovoltaic operation data for the forecast period, and select multiple time points from the forecast period.

[0042] In an embodiment of the present invention, the prediction data acquisition module 101 receives a light power prediction request uploaded by a prediction management personnel, identifies the light power prediction request, determines the prediction period and prediction location, obtains the predicted meteorological data of the prediction period through meteorological channels according to the prediction location, and then obtains the operation and maintenance management data of the photovoltaic power station. By analyzing the operation and maintenance management data, the photovoltaic operation data of the prediction period is extracted, and the point selection parameters are obtained. According to the point selection parameters, multiple time points are selected from the prediction period.

[0043] Specifically, in a preferred embodiment of the present invention, the prediction data acquisition module 101 specifically includes: A request receiving unit, configured to receive an optical power prediction request; a request identification unit, configured to identify the optical power prediction request and determine a prediction period and a prediction position; A meteorological data acquisition unit, configured to acquire the predicted meteorological data for the predicted period according to the predicted location; Operation and maintenance data acquisition unit, used to acquire operation and maintenance management data; an operation data extraction unit, configured to analyze the operation and maintenance management data and extract photovoltaic operation data for the forecast period; Selection parameter acquisition unit, used to obtain point selection parameters; A time point selection unit is used to select multiple time points from the prediction period according to the point selection parameters.

[0044] Furthermore, the intelligent optical power prediction platform also includes: The historical radiation matching module 102 is used to perform radiation matching on the predicted meteorological data and the preset historical record data according to the multiple time points, determine multiple matching time periods, and extract multiple matching meteorological data and multiple matching operation data.

[0045] In an embodiment of the present 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 in the preset historical record data according to the multiple time points and the multiple corresponding predicted radiation data, determines multiple matching time periods, and then extracts matching meteorological data corresponding to the multiple matching time periods from the historical record data, and extracts matching operation data corresponding to the multiple matching time periods from the historical record data.

[0046] The meteorological consistency comparison module 103 is configured to compare the predicted meteorological data with the plurality of matching meteorological data for meteorological consistency, and select a target time period from the plurality of matching time periods.

[0047] In an embodiment of the present invention, the meteorological match comparison module 103 extracts relevant data from the predicted meteorological data and the multiple matching meteorological data according to multiple time points and multiple preset meteorological factors, extracts the predicted point factor data of the multiple meteorological factors at the multiple time points from the predicted meteorological data, and extracts the multiple matching point factor data of the multiple meteorological factors at the multiple time points from the multiple matching meteorological data, calculates multiple matching comparison values ​​by comparing the predicted point factor data with the multiple matching point factor data, arranges the multiple matching comparison values, selects the largest matching comparison value, and marks it as the target comparison value, and then selects the matching period corresponding to the target comparison value from the multiple matching period and marks it as the target period. Specifically, the calculation formula for the multiple matching comparison values ​​is: ; in, Representative Matching periods, Representative time points, the prediction period and multiple matching periods have A point in time, Representative There are a total of meteorological factors Meteorological factors, For the forecast period and The matching comparison value of the matching period, The first The first time point The value of the meteorological factor, For the Matching period The first time point The value of a meteorological factor.

[0048] Specifically, in a preferred embodiment of the present invention, the meteorological match comparison module 103 specifically includes: A prediction point factor data extraction unit, configured to extract prediction point factor data from the predicted meteorological data according to the plurality of time points and the plurality of preset meteorological factors; a matching point factor data extraction unit, configured to extract a plurality of matching point factor data from the plurality of matching meteorological data according to the plurality of time points and the plurality of preset meteorological factors; a matching comparison calculation unit, configured to compare the predicted point location factor data with a plurality of matching point location factor data, and calculate a plurality of matching comparison values; a comparison value arranging unit, configured to arrange the plurality of matching comparison values ​​and select a target comparison value; The target period selection unit is configured to select a corresponding target period from the plurality of matching periods according to the target comparison value.

[0049] Furthermore, the intelligent optical power prediction platform also includes: The optical power prediction calculation module 104 is configured to select target operation data from the plurality of matching operation data according to the target time period, and calculate the predicted optical power according to the target operation data and the photovoltaic operation data.

[0050] In an embodiment of the present invention, the optical power prediction and calculation module 104 selects matching operation data corresponding to the target period from multiple matching operation data, marks it as target operation data, and then analyzes the target operation data to determine the target optical power and target operation ratio of photovoltaic power generation in the target period. The photovoltaic operation data is also analyzed to determine the predicted operation ratio. Then, based on the target optical power, the target operation ratio, and the predicted operation ratio, the optical power ratio is processed for the predicted period to calculate the predicted optical power for the predicted period. Specifically, the calculation formula for the predicted optical power is: ; in, To predict the optical power, To predict the running ratio, is the target optical power, is the target operating ratio.

[0051] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned 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. An intelligent optical power prediction method, characterized in that: The method specifically comprises the following steps: Determining a forecast period, obtaining forecast meteorological data and photovoltaic operation data for the forecast period, and selecting multiple time points from the forecast period; According to the multiple time points, the predicted meteorological data is radially matched with the preset historical record data to determine multiple matching time periods, and multiple matching meteorological data and multiple matching operation data are extracted; Comparing the predicted meteorological data with the plurality of matching meteorological data for meteorological consistency, and selecting a target time period from the plurality of matching time periods; According to the target time period, target operation data is selected from the plurality of matching operation data, and the predicted light power is calculated based on the target operation data and the photovoltaic operation data.

2. The intelligent optical power prediction method according to claim 1, characterized in that: Determining a forecast period, obtaining forecast meteorological data and photovoltaic operation data for the forecast period, and selecting multiple time points from the forecast period specifically include the following steps: receiving an optical power prediction request; Identifying the optical power prediction request and determining a prediction period and a prediction location; Obtaining forecasted meteorological data for the forecast period according to the forecast location; Obtain operation and maintenance management data; Analyzing the operation and maintenance management data to extract photovoltaic operation data for the forecast period; Get point selection parameters; A plurality of time points are selected from the prediction period according to the point selection parameters.

3. The intelligent optical power prediction method according to claim 1, wherein: The step of performing radial matching between the predicted meteorological data and preset historical record data according to the multiple time points, determining multiple matching time periods, and extracting multiple matching meteorological data and multiple matching operation data specifically includes the following steps: Extracting a plurality of predicted radiation data from the predicted meteorological data according to the plurality of time points; According to multiple time points and multiple corresponding predicted radiation data, radiation matching is performed in preset historical record data to determine multiple matching time periods; Extracting matching meteorological data corresponding to a plurality of matching time periods from the historical record data; Matching operation data corresponding to a plurality of matching time periods are extracted from the historical record data.

4. The intelligent optical power prediction method according to claim 1, wherein: Comparing the predicted meteorological data with the plurality of matching meteorological data for meteorological consistency and selecting a target time period from the plurality of matching time periods specifically comprises the following steps: Extracting prediction point factor data from the predicted meteorological data according to the plurality of time points and the plurality of preset meteorological factors; Extracting a plurality of matching point factor data from the plurality of matching meteorological data according to the plurality of time points and the plurality of preset meteorological factors; Comparing the predicted point factor data with a plurality of matching point factor data, and calculating a plurality of matching comparison values; Arranging the plurality of matching comparison values ​​and selecting a target comparison value; A corresponding target time period is selected from the plurality of matching time periods according to the target comparison value.

5. The intelligent optical power prediction method according to claim 4, characterized in that: The calculation formula for the plurality of matching comparison values ​​is: ； in, Representative Matching periods, Representative time points, the prediction period and multiple matching periods have A point in time, Representative There are a total of meteorological factors Meteorological factors, For the forecast period and The matching comparison value of the matching period, The first The first time point The value of the meteorological factor, For the Matching period The first time point The value of a meteorological factor.

6. The intelligent optical power prediction method according to claim 1, characterized in that: The step of selecting target operating data from a plurality of matching operating data according to the target time period, and calculating the predicted light power according to the target operating data and the photovoltaic operating data specifically comprises the following steps: selecting target operating data from the plurality of matching operating data according to the target time period; Analyzing the target operation data to determine a target optical power and a target operation ratio; Analyzing the photovoltaic operation data to determine a predicted operation ratio; Optical power ratio processing is performed according to the target optical power, the target operating ratio, and the predicted operating ratio to calculate the predicted optical power.

7. The intelligent optical power prediction method according to claim 6, characterized in that: The calculation formula for the predicted optical power is: ； in, To predict the optical power, To predict the running ratio, is the target optical power, is the target operating ratio.

8. An intelligent optical power prediction platform for executing the intelligent optical power prediction method according to any one of claims 1 to 7, characterized in that: The platform specifically includes a prediction data acquisition module, a historical radiation matching module, a meteorological coincidence comparison module, and an optical power prediction calculation module, wherein: A forecast data acquisition module is used to determine a forecast period, acquire forecast meteorological data and photovoltaic operation data for the forecast period, and select multiple time points from the forecast period; A historical radiation matching module is used to perform radiation matching on the predicted meteorological data and preset historical record data according to the multiple time points, determine multiple matching time periods, and extract multiple matching meteorological data and multiple matching operation data; A meteorological consistency comparison module is used to compare the predicted meteorological data with the plurality of matching meteorological data for meteorological consistency, and select a target time period from the plurality of matching time periods; The optical power prediction calculation module is used to select target operation data from the multiple matching operation data according to the target time period, and calculate the predicted optical power according to the target operation data and the photovoltaic operation data.

9. The intelligent optical power prediction platform according to claim 8, characterized in that: The prediction data acquisition module specifically includes: A request receiving unit, configured to receive an optical power prediction request; a request identification unit, configured to identify the optical power prediction request and determine a prediction period and a prediction position; A meteorological data acquisition unit, configured to acquire the predicted meteorological data for the predicted period according to the predicted location; Operation and maintenance data acquisition unit, used to acquire operation and maintenance management data; an operation data extraction unit, configured to analyze the operation and maintenance management data and extract photovoltaic operation data for the forecast period; Selection parameter acquisition unit, used to obtain point selection parameters; A time point selection unit is used to select multiple time points from the prediction period according to the point selection parameters.

10. The intelligent optical power prediction platform according to claim 8, characterized in that: The meteorological compliance comparison module specifically includes: A prediction point factor data extraction unit, configured to extract prediction point factor data from the predicted meteorological data according to the plurality of time points and the plurality of preset meteorological factors; a matching point factor data extraction unit, configured to extract a plurality of matching point factor data from the plurality of matching meteorological data according to the plurality of time points and the plurality of preset meteorological factors; a matching comparison calculation unit, configured to compare the predicted point location factor data with a plurality of matching point location factor data, and calculate a plurality of matching comparison values; a comparison value arranging unit, configured to arrange the plurality of matching comparison values ​​and select a target comparison value; The target period selection unit is configured to select a corresponding target period from the plurality of matching periods according to the target comparison value.

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