Generated power prediction method for wind power generation

Abstract

The invention discloses a power generation power prediction method for wind power generation, and relates to the field of power generation power prediction, and the method comprises an environment monitoring step, a preliminary analysis step and a power prediction step. The prediction difficulty coefficient corresponding to the generation power prediction period is obtained by combining the prediction time length of the wind power generation device for systematic analysis, a data basis is effectively provided for subsequent generation power prediction, and historical environment information and a historical electrical parameter set used by the wind power generation device are systematically analyzed and screened to obtain a prediction difficulty coefficient corresponding to the generation power prediction period. The electrical parameter sets of different historical time points and the interval duration between the different historical time points and the data interception time node of the power generation device are further analyzed, so that the accuracy of the corresponding power generation power prediction result of the wind power generation device is effectively improved, and the normal development of wind power generation is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of power generation prediction, and in particular to a method for predicting power generation of wind power generation. Background Art

[0002] As an intermittent energy source, wind power generation has a large fluctuation in power generation. Through wind power generation forecasting, wind farms can accurately predict their own power generation, so as to participate in the bidding in the power market more effectively. This not only helps wind farms improve their profitability, but also promotes fair competition in the power market. At the same time, the power dispatching department can reasonably arrange the output of conventional units and wind farms according to the wind power forecast results, optimize grid dispatching, reduce rotating reserve capacity, and thus save fuel and reduce the operating costs of the power system. However, the existing technology still has shortcomings: When predicting the power generation corresponding to a wind turbine, the existing technology lacks consideration of the prediction duration and drastic changes in weather during the prediction period, thereby reducing the accuracy and effectiveness of the prediction of the power generation corresponding to the wind turbine. At the same time, the existing technology lacks scientific analysis of historical data when predicting power generation, which reduces the effectiveness and rationality of comparative data search, further reduces the accuracy of the prediction of the power generation corresponding to the wind turbine, and inhibits the normal development of wind power generation. Summary of the invention

[0003] The object of the present invention is to provide a method for predicting wind power generation to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for predicting wind power generation power, comprising: Environmental monitoring step: used to monitor the environmental conditions of the area corresponding to the wind power generation device and obtain environmental information corresponding to the target area; Preliminary analysis step: used to perform preliminary analysis on the environmental information corresponding to the target area, and obtain the prediction difficulty coefficient of the wind power generation device corresponding to the power generation prediction period; Power prediction step: used to conduct a comprehensive analysis based on the environmental information corresponding to the target area and the prediction difficulty coefficient of the wind power generation device corresponding to the power generation prediction period, to obtain the power generation prediction result of the wind power generation device corresponding to the power generation prediction period.

[0005] In a preferred embodiment of this scheme, the environmental monitoring step is performed as follows: By extracting the environmental monitoring data generated by the environmental monitoring device installed on the top of the wind power generation device, the historical monitoring environmental information corresponding to the target area is obtained, wherein the historical environmental monitoring information refers to the historical monitoring environmental information of the target area corresponding to each historical time point, wherein the historical monitoring environmental information includes wind resource information, temperature, humidity and air pressure, wherein the wind resource information includes the wind speed and wind direction corresponding to the target area; The environmental prediction information of the target area where the wind power generation device is located is obtained through the meteorological cloud platform, where the environmental prediction information refers to the environmental prediction information corresponding to each prediction time point, and the environmental prediction information includes wind resource prediction information, predicted temperature, predicted humidity and predicted air pressure. The historical monitoring environmental information corresponding to the target area and the environmental prediction information of the target area are recorded as the environmental information corresponding to the target area.

[0006] In a preferred embodiment of this protocol, the preliminary analysis step is performed as follows: Establishing a data extraction relationship between the database and the preliminary analysis step, extracting historical power generation data corresponding to the wind power generation device stored in the database, wherein the historical power generation data includes an electrical parameter set corresponding to each historical time point of the wind power generation device, wherein the electrical parameter set includes power generation, current, voltage and power generation; Obtain the required prediction time corresponding to the wind power generation device, and record it as the power generation prediction period corresponding to the wind power generation device; Obtain power prediction time node of wind power generation device; Filter and obtain the environmental prediction information corresponding to the power generation prediction period of the wind power generation device, perform statistics on the environmental prediction information corresponding to the power generation prediction period, obtain the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve and predicted wind speed change curve corresponding to the power generation prediction period, extract information from the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve and predicted wind speed change curve corresponding to the power generation prediction period, and obtain the average predicted temperature, average predicted temperature change rate, average predicted humidity, average predicted humidity change rate, average predicted air pressure, average predicted air pressure change rate, average predicted wind speed and average predicted wind speed change rate corresponding to the power generation prediction period; A three-dimensional model is established according to the orientation of the wind power generation device and the real-time predicted wind direction corresponding to the power generation prediction period, and the real-time angle between the orientation of the wind power generation device and the real-time predicted wind direction corresponding to the power generation prediction period is obtained through the three-dimensional model, and the real-time wind angle corresponding to the wind power generation device is recorded, and the wind angle change curve corresponding to the wind power generation device is counted, and information is extracted from the wind angle change curve corresponding to the wind power generation device to obtain the average wind angle and the average wind angle change rate corresponding to the wind power generation device; By calculating the formula , calculate the environmental complexity coefficient corresponding to the power generation prediction period ; By calculating the formula , calculate the wind power generation stability coefficient corresponding to the power generation prediction period ; By calculating the formula , calculate the prediction difficulty coefficient corresponding to the power generation prediction period ,in , , , , They are respectively represented by the average predicted temperature, average predicted humidity, average predicted air pressure, average predicted wind speed and average wind angle corresponding to the power generation prediction period. , , , , They are respectively expressed as the average predicted temperature change rate, average predicted humidity change rate, average predicted air pressure change rate, average predicted wind speed change rate and average wind angle change rate corresponding to the power generation prediction period, , , , , They are respectively represented by the preset temperature weight, humidity weight, air pressure weight, wind speed weight and wind angle weight. Expressed as a natural constant, It represents the required prediction time corresponding to the wind power generation device.

[0007] In a preferred embodiment of the present invention, the power prediction step is performed as follows: The historical monitoring environment information corresponding to the target area and the historical power generation data corresponding to the wind power generation device are intercepted and divided according to the power generation prediction period corresponding to the wind power generation device and the power prediction time node of the wind power generation device, so as to obtain the historical monitoring environment information corresponding to each historical period of the target area and the historical power generation data corresponding to each historical period of the wind power generation device; Performing data statistical analysis on the historical monitoring environment information of the target area corresponding to each historical period and the historical power generation data of the wind power generation device corresponding to each historical period, obtaining the temperature change curve, humidity change curve, air pressure change curve, wind speed change curve and wind angle change curve corresponding to the historical monitoring environment information of the target area in each historical period, and recording them as the temperature change curve, humidity change curve, air pressure change curve, wind speed change curve and wind angle change curve corresponding to each historical period of the target area; Establish a curve coincidence model by using the temperature change curve, humidity change curve, air pressure change curve, wind speed change curve and wind angle change curve corresponding to each historical period of the target area and the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve, predicted wind speed change curve and wind angle change curve corresponding to the corresponding power generation prediction period, and obtain the coincidence degree of the temperature change curve, humidity change curve, predicted air pressure change curve, predicted wind speed change curve and wind angle change curve corresponding to each historical period of the target area with the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve, predicted wind speed change curve and wind angle change curve corresponding to the corresponding power generation prediction period, which is recorded as the temperature coincidence degree, humidity coincidence degree, air pressure coincidence degree, wind speed coincidence degree and wind angle coincidence degree corresponding to each historical period of the target area; Compare the temperature coincidence, humidity coincidence, air pressure coincidence, wind speed coincidence and wind angle coincidence corresponding to each historical period of the target area with the corresponding coincidence threshold, select the historical periods corresponding to which the temperature coincidence, humidity coincidence, air pressure coincidence, wind speed coincidence and wind angle coincidence are all greater than the coincidence threshold and record them as valid comparison periods, and obtain the valid comparison periods corresponding to the target area by statistics; The environmental prediction information corresponding to each prediction time point of the power generation prediction cycle is matched with the historical monitoring environmental information corresponding to each historical time point of each effective comparison cycle corresponding to the target area, and each historical time point in each effective comparison cycle that is consistent with the environmental prediction information of each prediction time point corresponding to the power generation prediction cycle is screened and obtained, and recorded as each historical time point corresponding to each prediction time point in the power generation prediction cycle, and the interval length between each historical time point corresponding to each prediction time point in the power generation prediction cycle and the power prediction time node of the wind power generation device is statistically obtained, and the historical power generation data corresponding to each effective comparison cycle is screened according to each historical time point corresponding to each prediction time point, and the electrical parameter set corresponding to each historical time point in the power generation prediction cycle is obtained, and the electrical parameter set corresponding to each historical time point of each prediction time point is matched to obtain the proportion of each identical electrical parameter set to the total parameter set corresponding to each prediction time point, and the interval length between each historical time point and the power prediction time node of the wind power generation device in each identical electrical parameter set is screened; Establish a data extraction relationship between the power prediction step and the database, extract the reference coefficients corresponding to each interval duration stored in the database, and filter and obtain the reference coefficients of each historical time point in each same electrical parameter set according to the interval duration between each historical time point in each same electrical parameter set and the power prediction time node of the wind power generation device; By calculating the formula , calculate the electric power reference value corresponding to each same electric parameter set at each prediction time point ; By calculating the formula , calculate the corresponding electric power prediction value at each prediction time point ,in It represents the number of each prediction time point. It is represented by the number corresponding to each set of the same electrical parameters. Expressed as the number of types of electrical parameter sets, Represents the number of each historical time point, Expressed as the number of historical time points, It is represented by the power generation corresponding to each historical time point in each same electrical parameter set at each predicted time point, It is expressed as the reference coefficient corresponding to each historical time point in the same electrical parameter set at each prediction time point. It is expressed as the proportion of each identical electrical parameter set to the total parameter set corresponding to each prediction time point; According to the electric power prediction values ​​corresponding to each prediction time point within the power generation prediction period, an electric power prediction value change curve corresponding to the power generation prediction period is established, and the electric power prediction mean value corresponding to the electric power prediction value change curve is obtained. The electric power prediction value change curve corresponding to the power generation prediction period and the electric power prediction mean value are recorded as the power generation prediction result of the wind turbine device corresponding to the power generation prediction period.

[0008] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains the environmental information of the wind power generation device in the area where the wind power generation device is located and the required prediction time of the wind power generation device, and performs a system analysis based on the required prediction time of the wind power generation device, thereby obtaining the prediction difficulty coefficient corresponding to the power generation prediction period, effectively providing a data basis for subsequent power generation prediction, and indirectly improving the accuracy and effectiveness of power generation prediction; The present invention systematically analyzes and screens the historical environmental information and historical electrical parameter sets used by wind power generation devices, and further analyzes the electrical parameter sets at different historical time points and the intervals between different historical time points and the data capture time nodes of the power generation devices, thereby effectively improving the accuracy of the corresponding power generation prediction results of the wind power generation devices and ensuring the normal development of wind power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0010] Figure 1 It is a schematic diagram of the connection steps of an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] See also Figure 1 , the present invention provides a method for predicting the power generation of wind power generation, the method comprising an environmental monitoring step, a preliminary analysis step and a power prediction step; The environmental monitoring step is connected to the preliminary analysis step, and the preliminary analysis step is connected to the power prediction step.

[0013] The environmental monitoring step is used to monitor the environmental conditions of the area corresponding to the wind power generation device to obtain environmental information corresponding to the target area; Furthermore, the environmental monitoring step is performed as follows: By extracting the environmental monitoring data generated by the environmental monitoring device installed on the top of the wind power generation device, the historical monitoring environmental information corresponding to the target area is obtained, wherein the historical environmental monitoring information refers to the historical monitoring environmental information of the target area corresponding to each historical time point, wherein the historical monitoring environmental information includes wind resource information, temperature, humidity and air pressure, wherein the wind resource information includes the wind speed and wind direction corresponding to the target area; The environmental prediction information of the target area where the wind power generation device is located is obtained through the meteorological cloud platform, where the environmental prediction information refers to the environmental prediction information corresponding to each prediction time point, and the environmental prediction information includes wind resource prediction information, predicted temperature, predicted humidity and predicted air pressure. The historical monitoring environmental information corresponding to the target area and the environmental prediction information of the target area are recorded as the environmental information corresponding to the target area.

[0014] It should be noted that the area corresponding to the wind power generation device is recorded as the target area.

[0015] The preliminary analysis step is used to perform preliminary analysis on the environmental information corresponding to the target area to obtain the prediction difficulty coefficient of the wind power generation device corresponding to the power generation prediction period; Furthermore, the preliminary analysis step is performed as follows: Establishing a data extraction relationship between the database and the preliminary analysis step, extracting historical power generation data corresponding to the wind power generation device stored in the database, wherein the historical power generation data includes an electrical parameter set corresponding to each historical time point of the wind power generation device, wherein the electrical parameter set includes power generation, current, voltage and power generation; Obtain the required prediction time corresponding to the wind power generation device, and record it as the power generation prediction period corresponding to the wind power generation device; Obtain power prediction time node of wind power generation device; It should be noted that the time points before the data capture time node are all historical data.

[0016] Filter and obtain the environmental prediction information corresponding to the power generation prediction period of the wind power generation device, perform statistics on the environmental prediction information corresponding to the power generation prediction period, obtain the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve and predicted wind speed change curve corresponding to the power generation prediction period, extract information from the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve and predicted wind speed change curve corresponding to the power generation prediction period, and obtain the average predicted temperature, average predicted temperature change rate, average predicted humidity, average predicted humidity change rate, average predicted air pressure, average predicted air pressure change rate, average predicted wind speed and average predicted wind speed change rate corresponding to the power generation prediction period; A three-dimensional model is established according to the orientation of the wind power generation device and the real-time predicted wind direction corresponding to the power generation prediction period, and the real-time angle between the orientation of the wind power generation device and the real-time predicted wind direction corresponding to the power generation prediction period is obtained through the three-dimensional model, and the real-time wind angle corresponding to the wind power generation device is recorded, and the wind angle change curve corresponding to the wind power generation device is counted, and information is extracted from the wind angle change curve corresponding to the wind power generation device to obtain the average wind angle and the average wind angle change rate corresponding to the wind power generation device; By calculating the formula , calculate the environmental complexity coefficient corresponding to the power generation prediction period ; By calculating the formula , calculate the wind power generation stability coefficient corresponding to the power generation prediction period ; By calculating the formula , calculate the prediction difficulty coefficient corresponding to the power generation prediction period ,in , , , , They are respectively represented by the average predicted temperature, average predicted humidity, average predicted air pressure, average predicted wind speed and average wind angle corresponding to the power generation prediction period. , , , , They are respectively expressed as the average predicted temperature change rate, average predicted humidity change rate, average predicted air pressure change rate, average predicted wind speed change rate and average wind angle change rate corresponding to the power generation prediction period, , , , , They are respectively represented by the preset temperature weight, humidity weight, air pressure weight, wind speed weight and wind angle weight. Expressed as a natural constant, It represents the required prediction time corresponding to the wind power generation device.

[0017] The power prediction step is used to perform a comprehensive analysis based on the environmental information corresponding to the target area and the prediction difficulty coefficient of the wind power generation device corresponding to the power generation prediction period, to obtain the power generation prediction result of the wind power generation device corresponding to the power generation prediction period.

[0018] Furthermore, the power prediction step is performed as follows: The historical monitoring environment information corresponding to the target area and the historical power generation data corresponding to the wind power generation device are intercepted and divided according to the power generation prediction period corresponding to the wind power generation device and the power prediction time node of the wind power generation device, so as to obtain the historical monitoring environment information corresponding to each historical period of the target area and the historical power generation data corresponding to each historical period of the wind power generation device; Performing data statistical analysis on the historical monitoring environment information of the target area corresponding to each historical period and the historical power generation data of the wind power generation device corresponding to each historical period, obtaining the temperature change curve, humidity change curve, air pressure change curve, wind speed change curve and wind angle change curve corresponding to the historical monitoring environment information of the target area in each historical period, and recording them as the temperature change curve, humidity change curve, air pressure change curve, wind speed change curve and wind angle change curve corresponding to each historical period of the target area; Establish a curve coincidence model by using the temperature change curve, humidity change curve, air pressure change curve, wind speed change curve and wind angle change curve corresponding to each historical period of the target area and the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve, predicted wind speed change curve and wind angle change curve corresponding to the corresponding power generation prediction period, and obtain the coincidence degree of the temperature change curve, humidity change curve, predicted air pressure change curve, predicted wind speed change curve and wind angle change curve corresponding to each historical period of the target area with the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve, predicted wind speed change curve and wind angle change curve corresponding to the corresponding power generation prediction period, which is recorded as the temperature coincidence degree, humidity coincidence degree, air pressure coincidence degree, wind speed coincidence degree and wind angle coincidence degree corresponding to each historical period of the target area; Compare the temperature coincidence, humidity coincidence, air pressure coincidence, wind speed coincidence and wind angle coincidence corresponding to each historical period of the target area with the corresponding coincidence threshold, select the historical periods corresponding to which the temperature coincidence, humidity coincidence, air pressure coincidence, wind speed coincidence and wind angle coincidence are all greater than the coincidence threshold and record them as valid comparison periods, and obtain the valid comparison periods corresponding to the target area by statistics; It should be noted that the coincidence thresholds corresponding to the temperature coincidence, humidity coincidence, air pressure coincidence, wind speed coincidence and wind angle coincidence are all the same.

[0019] The environmental prediction information corresponding to each prediction time point of the power generation prediction cycle is matched with the historical monitoring environmental information corresponding to each historical time point of each effective comparison cycle corresponding to the target area, and each historical time point in each effective comparison cycle that is consistent with the environmental prediction information of each prediction time point corresponding to the power generation prediction cycle is screened and obtained, and recorded as each historical time point corresponding to each prediction time point in the power generation prediction cycle, and the interval length between each historical time point corresponding to each prediction time point in the power generation prediction cycle and the power prediction time node of the wind power generation device is statistically obtained, and the historical power generation data corresponding to each effective comparison cycle is screened according to each historical time point corresponding to each prediction time point, and the electrical parameter set corresponding to each historical time point in the power generation prediction cycle is obtained, and the electrical parameter set corresponding to each historical time point of each prediction time point is matched to obtain the proportion of each identical electrical parameter set to the total parameter set corresponding to each prediction time point, and the interval length between each historical time point and the power prediction time node of the wind power generation device in each identical electrical parameter set is screened; Establish a data extraction relationship between the power prediction step and the database, extract the reference coefficients corresponding to each interval duration stored in the database, and filter and obtain the reference coefficients of each historical time point in each same electrical parameter set according to the interval duration between each historical time point in each same electrical parameter set and the power prediction time node of the wind power generation device; By calculating the formula , calculate the electric power reference value corresponding to each same electric parameter set at each prediction time point ; By calculating the formula , calculate the corresponding electric power prediction value at each prediction time point ,in It represents the number of each prediction time point. It is represented by the number corresponding to each set of the same electrical parameters. Expressed as the number of types of electrical parameter sets, Represents the number of each historical time point, Expressed as the number of historical time points, It is represented by the power generation corresponding to each historical time point in each same electrical parameter set at each predicted time point, It is expressed as the reference coefficient corresponding to each historical time point in the same electrical parameter set at each prediction time point. It is expressed as the proportion of each identical electrical parameter set to the total parameter set corresponding to each prediction time point; According to the electric power prediction values ​​corresponding to each prediction time point within the power generation prediction period, an electric power prediction value change curve corresponding to the power generation prediction period is established, and the electric power prediction mean value corresponding to the electric power prediction value change curve is obtained. The electric power prediction value change curve corresponding to the power generation prediction period and the electric power prediction mean value are recorded as the power generation prediction result of the wind turbine device corresponding to the power generation prediction period.

[0020] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for predicting wind power generation power, characterized in that: include: Environmental monitoring step: used to monitor the environmental conditions of the area corresponding to the wind power generation device and obtain environmental information corresponding to the target area; Preliminary analysis step: used to perform preliminary analysis on the environmental information corresponding to the target area, and obtain the prediction difficulty coefficient of the wind power generation device corresponding to the power generation prediction period; Power prediction step: used to conduct a comprehensive analysis based on the environmental information corresponding to the target area and the prediction difficulty coefficient of the wind power generation device corresponding to the power generation prediction period, to obtain the power generation prediction result of the wind power generation device corresponding to the power generation prediction period.

2. A method for predicting wind power generation according to claim 1, characterized in that: The environmental monitoring step is performed as follows: By extracting the environmental monitoring data generated by the environmental monitoring device installed on the top of the wind power generation device, the historical monitoring environmental information corresponding to the target area is obtained, wherein the historical environmental monitoring information refers to the historical monitoring environmental information of the target area corresponding to each historical time point, wherein the historical monitoring environmental information includes wind resource information, temperature, humidity and air pressure, wherein the wind resource information includes the wind speed and wind direction corresponding to the target area; The environmental prediction information of the target area where the wind power generation device is located is obtained through the meteorological cloud platform, where the environmental prediction information refers to the environmental prediction information corresponding to each prediction time point, and the environmental prediction information includes wind resource prediction information, predicted temperature, predicted humidity and predicted air pressure. The historical monitoring environmental information corresponding to the target area and the environmental prediction information of the target area are recorded as the environmental information corresponding to the target area.

3. A method for predicting wind power generation according to claim 2, characterized in that: The preliminary analysis steps are performed as follows: Establishing a data extraction relationship between the database and the preliminary analysis step, extracting historical power generation data corresponding to the wind power generation device stored in the database, wherein the historical power generation data includes an electrical parameter set corresponding to each historical time point of the wind power generation device, wherein the electrical parameter set includes power generation, current, voltage and power generation; Obtain the required prediction time corresponding to the wind power generation device, and record it as the power generation power prediction period corresponding to the wind power generation device; Obtain power prediction time node of wind power generation device; Filter and obtain the environmental prediction information corresponding to the power generation prediction period of the wind power generation device, perform statistics on the environmental prediction information corresponding to the power generation prediction period, obtain the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve and predicted wind speed change curve corresponding to the power generation prediction period, extract information from the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve and predicted wind speed change curve corresponding to the power generation prediction period, and obtain the average predicted temperature, average predicted temperature change rate, average predicted humidity, average predicted humidity change rate, average predicted air pressure, average predicted air pressure change rate, average predicted wind speed and average predicted wind speed change rate corresponding to the power generation prediction period; A three-dimensional model is established according to the orientation of the wind power generation device and the real-time predicted wind direction corresponding to the power generation prediction period, and the real-time angle between the orientation of the wind power generation device and the real-time predicted wind direction corresponding to the power generation prediction period is obtained through the three-dimensional model, and the real-time wind angle corresponding to the wind power generation device is recorded, and the wind angle change curve corresponding to the wind power generation device is counted, and information is extracted from the wind angle change curve corresponding to the wind power generation device to obtain the average wind angle and the average wind angle change rate corresponding to the wind power generation device; By calculating the formula , calculate the environmental complexity coefficient corresponding to the power generation prediction period ; By calculating the formula , calculate the wind power generation stability coefficient corresponding to the power generation prediction period ; By calculating the formula , calculate the prediction difficulty coefficient corresponding to the power generation prediction period ,in , , , , They are respectively represented by the average predicted temperature, average predicted humidity, average predicted air pressure, average predicted wind speed and average wind angle corresponding to the power generation prediction period. , , , , They are respectively expressed as the average predicted temperature change rate, average predicted humidity change rate, average predicted air pressure change rate, average predicted wind speed change rate and average wind angle change rate corresponding to the power generation prediction period, , , , , They are respectively represented by the preset temperature weight, humidity weight, air pressure weight, wind speed weight and wind angle weight. Expressed as a natural constant, It represents the required prediction time corresponding to the wind power generation device.

4. A method for predicting wind power generation according to claim 3, characterized in that: The power prediction step is performed as follows: The historical monitoring environment information corresponding to the target area and the historical power generation data corresponding to the wind power generation device are intercepted and divided according to the power generation prediction period corresponding to the wind power generation device and the power prediction time node of the wind power generation device, so as to obtain the historical monitoring environment information corresponding to each historical period of the target area and the historical power generation data corresponding to each historical period of the wind power generation device; Performing data statistical analysis on the historical monitoring environment information of the target area corresponding to each historical period and the historical power generation data of the wind power generation device corresponding to each historical period, obtaining the temperature change curve, humidity change curve, air pressure change curve, wind speed change curve and wind angle change curve corresponding to the historical monitoring environment information of the target area in each historical period, and recording them as the temperature change curve, humidity change curve, air pressure change curve, wind speed change curve and wind angle change curve corresponding to each historical period of the target area; Establish a curve coincidence model by using the temperature change curve, humidity change curve, air pressure change curve, wind speed change curve and wind angle change curve corresponding to each historical period of the target area and the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve, predicted wind speed change curve and wind angle change curve corresponding to the corresponding power generation prediction period, and obtain the coincidence degree of the temperature change curve, humidity change curve, predicted air pressure change curve, predicted wind speed change curve and wind angle change curve corresponding to each historical period of the target area with the predicted temperature change curve, predicted humidity change curve, predicted air pressure change curve, predicted wind speed change curve and wind angle change curve corresponding to the corresponding power generation prediction period, which is recorded as the temperature coincidence degree, humidity coincidence degree, air pressure coincidence degree, wind speed coincidence degree and wind angle coincidence degree corresponding to each historical period of the target area; Compare the temperature coincidence, humidity coincidence, air pressure coincidence, wind speed coincidence and wind angle coincidence corresponding to each historical period of the target area with the corresponding coincidence threshold, select the historical periods corresponding to which the temperature coincidence, humidity coincidence, air pressure coincidence, wind speed coincidence and wind angle coincidence are all greater than the coincidence threshold and record them as valid comparison periods, and obtain the valid comparison periods corresponding to the target area by statistics; The environmental prediction information corresponding to each prediction time point of the power generation prediction cycle is matched with the historical monitoring environmental information corresponding to each historical time point of each effective comparison cycle corresponding to the target area, and each historical time point in each effective comparison cycle that is consistent with the environmental prediction information of each prediction time point corresponding to the power generation prediction cycle is screened and obtained, and recorded as each historical time point corresponding to each prediction time point in the power generation prediction cycle, and the interval length between each historical time point corresponding to each prediction time point in the power generation prediction cycle and the power prediction time node of the wind power generation device is statistically obtained, and the historical power generation data corresponding to each effective comparison cycle is screened according to each historical time point corresponding to each prediction time point, and the electrical parameter set corresponding to each historical time point in the power generation prediction cycle is obtained, and the electrical parameter set corresponding to each historical time point of each prediction time point is matched to obtain the proportion of each identical electrical parameter set to the total parameter set corresponding to each prediction time point, and the interval length between each historical time point and the power prediction time node of the wind power generation device in each identical electrical parameter set is screened; Establish a data extraction relationship between the power prediction step and the database, extract the reference coefficients corresponding to each interval duration stored in the database, and filter and obtain the reference coefficients of each historical time point in each same electrical parameter set according to the interval duration between each historical time point in each same electrical parameter set and the power prediction time node of the wind power generation device; By calculating the formula , calculate the electric power reference value corresponding to each same electric parameter set at each prediction time point ; By calculating the formula , calculate the corresponding electric power prediction value at each prediction time point ,in It represents the number of each prediction time point. It is represented by the number corresponding to each set of the same electrical parameters. Expressed as the number of types of electrical parameter sets, It is represented by the number of each historical time point. Expressed as the number of historical time points, It is represented by the power generation corresponding to each historical time point in each same electrical parameter set at each predicted time point, It is expressed as the reference coefficient corresponding to each historical time point in the same electrical parameter set at each prediction time point. It is expressed as the proportion of each identical electrical parameter set to the total parameter set corresponding to each prediction time point; According to the electric power prediction values ​​corresponding to each prediction time point within the power generation prediction period, an electric power prediction value change curve corresponding to the power generation prediction period is established, and the electric power prediction mean value corresponding to the electric power prediction value change curve is obtained. The electric power prediction value change curve corresponding to the power generation prediction period and the electric power prediction mean value are recorded as the power generation prediction result of the wind turbine device corresponding to the power generation prediction period.

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