A wind power prediction method and system based on data analysis

Abstract

The present application provides a wind power prediction method and system based on data analysis, and the key points of the technical solution include obtaining the power plant wind turbine data, power plant environmental data and power plant wind energy data of the target power plant; setting the power prediction period of the target power plant; analyzing and obtaining the first power influence coefficient, the second power influence coefficient and the third power influence coefficient of the target power plant; and obtaining the wind power influence coefficient of the target power plant in the power prediction period according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient; analyzing the wind turbine configuration data of the target power plant to obtain the wind turbine configuration coefficient of the target power plant; obtaining the periodic power prediction value of the target power plant according to the wind turbine configuration coefficient and the wind power influence coefficient, and obtaining the wind power prediction value of the target power plant within the prediction time according to the periodic power prediction value. The present application improves the comprehensiveness and accuracy of the wind power prediction results.

Description

Technical Field

[0001] The present application relates to the field of wind power technology, and in particular to a wind power prediction method and system based on data analysis. Background Art

[0002] With the increasing attention paid to renewable energy around the world, wind energy, as a clean and renewable energy, has occupied an increasingly important position in the energy field, and the scale of wind power generation has continued to expand. However, due to the intermittent and random nature of wind energy, accurate prediction of wind power is crucial to optimizing the operation of wind power generation systems, improving the stability of power grids, and achieving efficient energy management. Wind power prediction refers to the use of various technical means to estimate the output power of wind farms in the future. Accurate wind power prediction is of great significance to the stable operation of power systems, optimized scheduling, and improving the utilization efficiency of wind energy.

[0003] The prediction of wind power in related technologies often does not fully consider the factors that cause wind power fluctuations, which easily leads to a lack of comprehensiveness and accuracy in wind power prediction results. There is room for improvement. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a wind power prediction method and system based on data analysis, so as to improve the problem that the prediction of wind power in the related art often does not fully consider the factors affecting the wind power fluctuation, which easily leads to the lack of comprehensiveness and accuracy of the wind power prediction results.

[0005] In a first aspect of the present application, a method for wind power prediction based on data analysis is provided, comprising the following steps:

[0006] Acquire wind turbine data of a target power plant, and acquire power plant environment data and wind energy data of the target power plant within a forecast period;

[0007] The power plant wind turbine data includes wind turbine configuration data, wind turbine orientation data and wind turbine layout data of the wind turbines in the target power plant; the power plant wind energy data includes wind speed prediction data and wind direction prediction data of the target power plant within the prediction time;

[0008] Setting a power forecast period of the target power plant according to the wind speed forecast data and wind direction forecast data of the target power plant;

[0009] Analyzing the power plant environment data of the target power plant according to the power forecast period to obtain a first power influence coefficient of the target power plant within the power forecast period;

[0010] Analyzing the wind direction prediction data of the target power plant and the wind turbine orientation data of the wind turbine according to the power prediction period to obtain the wind direction deviation coefficient of the wind turbine; and obtaining the wind energy capture coefficient of the wind turbine according to the wind direction deviation coefficient and the wind speed prediction data of the target power plant;

[0011] Obtaining a second power influence coefficient of the target power plant within a power forecast period according to the wind direction deviation coefficient and the wind energy capture coefficient;

[0012] Analyze the wind speed prediction data of the target power plant and the fan configuration data of the fan to obtain the fan influence coefficient of the fan in the target power plant, and obtain the standard layout interval of the fan in the target power plant according to the fan influence coefficient;

[0013] Comparing and analyzing the fan layout data of the fans in the target power plant with the standard layout interval to obtain a layout analysis result, and obtaining a third power influence coefficient of the target power plant within a power forecast period according to the layout analysis result;

[0014] Obtaining a wind power influence coefficient of a target power plant within a power forecast period according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient;

[0015] Analyzing the fan configuration data of the target power plant to obtain the fan configuration coefficient of the target power plant;

[0016] The periodic power forecast value of the target power plant in the power forecast period is obtained according to the wind turbine configuration coefficient and the wind power influence coefficient, and the wind power forecast value of the target power plant in the forecast time is obtained according to the periodic power forecast value.

[0017] Preferably, power plant environment data, power plant fan data and power plant wind energy data of the target power plant are obtained; the power plant fan data include fan configuration data, fan orientation data and fan layout data of the fans in the target power plant; the power plant wind energy data include wind speed prediction data and wind direction prediction data of the target power plant, specifically:

[0018] The wind turbine configuration data includes wind turbine efficiency and wind turbine diameter of wind turbines in the target power plant;

[0019] The wind turbine orientation data includes wind turbine orientation angles of wind turbines in the target power plant;

[0020] The wind turbine layout data includes wind turbine layout intervals of wind turbines in the target power plant;

[0021] The power plant environmental data includes a predicted temperature value and a predicted air pressure value of the target power plant within a predicted time;

[0022] The wind speed prediction data includes the wind speed prediction value of the target power plant within the prediction time; the wind direction prediction data includes the wind direction prediction angle of the target power plant within the prediction time.

[0023] Preferably, the power prediction period of the target power plant is set according to the wind speed prediction data and wind direction prediction data of the target power plant, specifically:

[0024] According to the wind speed forecast value of the target power plant within the forecast time, the wind speed forecast mean value and the wind speed forecast maximum value of the target power plant within the forecast time are obtained;

[0025] The wind speed forecast mean value of the target power plant within the forecast time is taken and marked to obtain the wind speed forecast mean value YJ; the wind speed forecast maximum value of the target power plant within the forecast time is taken and marked to obtain the wind speed forecast maximum value YM;

[0026] By first calculating the function The first cycle influence coefficient OZX of the target power plant is calculated, where a1 and a2 are preset proportional factors;

[0027] A wind direction angle monitoring period is set according to the prediction time, wherein the wind direction angle monitoring period corresponds to a wind direction angle change threshold, wherein the prediction time includes at least one wind direction angle monitoring period;

[0028] According to the wind direction prediction angle of the target power plant within the prediction time, the change value of the wind direction prediction angle of the target power plant within the wind direction monitoring period is obtained;

[0029] Compare and analyze the predicted wind direction angle change value of the target power plant within the wind direction angle monitoring period with the wind direction angle change threshold;

[0030] If the wind direction angle change value of the target power plant during the wind direction angle monitoring period is less than the wind direction angle change threshold, the wind direction angle change value is marked as a normal predicted angle change value;

[0031] If the wind direction angle change value of the target power plant during the wind direction angle monitoring period is greater than or equal to the wind direction angle change threshold, the wind direction angle change value is marked as an abnormal predicted angle change value;

[0032] Obtain the total number of normal change values ​​of the normal predicted angle change value, and obtain the total number of abnormal change values ​​of the abnormal predicted angle change value;

[0033] The total number of normal change values ​​is taken and marked to obtain the total number of normal change values ​​ZS; the total number of abnormal change values ​​is taken and marked to obtain the total number of abnormal change values ​​YS; the abnormal predicted angle change value is taken and marked to obtain the abnormal predicted angle change value BZ; the wind direction angle change threshold is taken and marked to obtain the wind direction angle change threshold BY;

[0034] Through the second calculation function The second cycle influence coefficient TZX of the target power plant is calculated, where b1 and b2 are preset proportional factors, and j is the number of the abnormal prediction angle change value;

[0035] The prediction time of the target power plant is taken and marked to obtain the prediction time CS;

[0036] Through the third calculation function The power forecast period YT of the target power plant is calculated, where c1 and c2 are preset proportional factors.

[0037] Preferably, the power plant environmental data is analyzed according to the power forecast period to obtain a first power influence coefficient of the target power plant within the power forecast period, specifically:

[0038] Obtain the predicted mean temperature and predicted mean air pressure of the target power plant within the power prediction period;

[0039] The predicted temperature mean is taken and marked to obtain the predicted temperature mean CQ; the predicted air pressure mean is taken and marked to obtain the predicted air pressure mean CW;

[0040] Through the fourth calculation function The first power influence coefficient OYX of the target power plant in the power forecast period is calculated, where Preset scale factor.

[0041] Preferably, the wind direction prediction data of the target power plant and the wind turbine orientation data of the wind turbine are analyzed according to the power prediction period to obtain the wind direction deviation coefficient of the wind turbine; and the wind energy capture coefficient of the wind turbine is obtained according to the wind direction deviation coefficient and the wind speed prediction data of the target power plant; and the second power influence coefficient of the target power plant within the power prediction period is obtained according to the wind direction deviation coefficient and the wind energy capture coefficient, which is specifically:

[0042] The wind direction prediction angle of the target power plant is obtained by taking and marking the wind direction prediction angle FJ; the fan direction angle of the fan in the target power plant is obtained by taking and marking the fan direction angle CJ;

[0043] Through the fifth calculation function The wind direction deviation coefficient FPX of the fan is calculated, where is the preset scaling factor;

[0044] Obtaining a periodic wind speed forecast mean value of a target power plant within a power forecast period, and taking and marking the periodic wind speed forecast mean value to obtain a periodic wind speed forecast mean value FS;

[0045] Through the sixth calculation function The wind energy capture coefficient FBX of the wind turbine is calculated, where d is a preset proportional factor;

[0046] Through the seventh calculation function The second power influence coefficient TYX of the target power plant within the power forecast period is calculated, wherein e1 and e2 are preset proportional factors.

[0047] Preferably, the wind speed prediction data of the target power plant and the fan configuration data of the fan are analyzed to obtain the fan influence coefficient of the fan in the target power plant, and the standard layout interval of the fan in the target power plant is obtained according to the fan influence coefficient, specifically:

[0048] The fan efficiency of the fan in the target power plant is obtained by taking values ​​and marking the fan efficiency JL; the fan diameter of the fan in the target power plant is obtained by taking values ​​and marking the fan diameter JD;

[0049] Through the eighth calculation function The fan influence coefficient JY of the fan of the target power plant in the predicted power cycle is calculated, where f1, f2, and f3 are preset proportional factors;

[0050] Through the ninth calculation function The standard layout interval AJ of the wind turbines in the target power plant during the predicted power cycle is calculated, where is the preset scaling factor;

[0051] The fan layout data of the fans in the target power plant are compared and analyzed with the standard layout interval to obtain a layout analysis result, and the third power influence coefficient of the target power plant in the power forecast period is obtained according to the layout analysis result, which is specifically:

[0052] If the fan layout intervals of the target power plant fans are all greater than or equal to the standard layout interval AJ within the predicted power cycle, then the target power plant does not have a third power influence coefficient;

[0053] If, in the predicted power cycle, there are wind turbines in the target power plant with a wind turbine layout interval smaller than the standard layout interval AJ, the target power plant has a third power influence coefficient, and the wind turbines with a wind turbine layout interval smaller than the standard layout interval AJ are marked as abnormal wind turbines, and the wind turbine layout interval of the abnormal wind turbines is marked as an abnormal layout interval;

[0054] Obtaining the abnormal proportion of the abnormal wind turbines in the target power plant, and taking values ​​and marking the abnormal proportion to obtain the abnormal proportion ZB; taking values ​​and marking the abnormal layout interval to obtain the abnormal layout interval BJ;

[0055] Through the tenth calculation function The third power influence coefficient SYX of the target power plant is calculated, wherein g1 and g2 are preset proportional factors, u is the number of the abnormal wind turbine, and m is the number of the abnormal wind turbine.

[0056] Preferably, the wind power influence coefficient of the target power plant within the power forecast period is obtained according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient, specifically:

[0057] By the eleventh calculation function The wind power influence coefficient FGX of the target power plant within the power forecast period is calculated, where h1, h2, and h3 are preset proportional factors;

[0058] If the wind power influence coefficient FGX of the target power plant is less than or equal to the preset wind power influence coefficient threshold, it is determined that the wind power influence coefficient FGX of the target power plant is normal;

[0059] If the wind power influence coefficient FGX of the target power plant is greater than the preset wind power influence coefficient threshold, the wind power influence coefficient FGX of the target power plant is judged to be abnormal, and the preset wind power influence coefficient threshold is taken and marked to obtain the wind power influence coefficient threshold YXY;

[0060] Then through the twelfth calculation function Calculate the wind power influence coefficient FGX corresponding to the target power plant with abnormal wind power influence coefficient , where k1 and k2 are preset scaling factors.

[0061] Preferably, the fan configuration data of the target power plant is analyzed to obtain the fan configuration coefficient of the target power plant, specifically:

[0062] Obtaining the total number of wind turbines in the target power plant, and taking values ​​and marking the total number of wind turbines to obtain the total number of wind turbines FZ;

[0063] Through the thirteenth calculation function The wind turbine configuration factor JPX of the target power plant is calculated, where p1 and p2 are preset proportional factors.

[0064] Preferably, the periodic power forecast value of the target power plant in the power forecast period is obtained according to the wind turbine configuration coefficient and the wind power influence coefficient, and the wind power forecast value of the target power plant within the forecast time is obtained according to the periodic power forecast value, specifically:

[0065] By calculating the function The periodic power forecast value GJY of the target power plant in the power forecast period is calculated, where is the preset scaling factor;

[0066] By the fifteenth calculation function The wind power forecast value FGY of the target power plant within the forecast time is calculated, where i is the cycle number of the power forecast cycle within the forecast time, and n is the cycle number of the power forecast cycle.

[0067] In a second aspect of the present application, a wind power prediction system based on data analysis is provided, the system is applied to a wind power prediction method based on data analysis, the system comprising:

[0068] Acquisition module: acquiring the wind turbine data of the target power plant, and acquiring the power plant environment data and wind energy data of the target power plant within the forecast time;

[0069] The power plant wind turbine data includes wind turbine configuration data, wind turbine orientation data and wind turbine layout data of the wind turbines in the target power plant; the power plant wind energy data includes wind speed prediction data and wind direction prediction data of the target power plant within the prediction time;

[0070] Setting module: setting the power prediction period of the target power plant according to the wind speed prediction data and wind direction prediction data of the target power plant;

[0071] A first analysis module: analyzing the power plant environment data of the target power plant according to the power forecast period to obtain a first power influence coefficient of the target power plant within the power forecast period;

[0072] The second analysis module: analyzes the wind direction prediction data of the target power plant and the wind turbine orientation data of the wind turbine according to the power prediction period to obtain the wind direction deviation coefficient of the wind turbine; and obtains the wind energy capture coefficient of the wind turbine according to the wind direction deviation coefficient and the wind speed prediction data of the target power plant;

[0073] Obtaining a second power influence coefficient of the target power plant within a power forecast period according to the wind direction deviation coefficient and the wind energy capture coefficient;

[0074] The third analysis module: performs analysis based on the wind speed prediction data of the target power plant and the fan configuration data of the fan to obtain the fan influence coefficient of the fan in the target power plant, and obtains the standard layout interval of the fan in the target power plant based on the fan influence coefficient;

[0075] Comparing and analyzing the fan layout data of the fans in the target power plant with the standard layout interval to obtain a layout analysis result, and obtaining a third power influence coefficient of the target power plant within a power forecast period according to the layout analysis result;

[0076] Obtaining a wind power influence coefficient of a target power plant within a power forecast period according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient;

[0077] A fourth analysis module: analyzing the wind turbine configuration data of the target power plant to obtain the wind turbine configuration coefficient of the target power plant;

[0078] Wind power prediction module: obtains the periodic power prediction value of the target power plant in the power prediction period according to the wind turbine configuration coefficient and the wind power influence coefficient, and obtains the wind power prediction value of the target power plant within the prediction time according to the periodic power prediction value.

[0079] In summary, the beneficial effects of the present application are as follows: the present application sets the power prediction period of the target power plant according to the wind speed prediction data and wind direction prediction data of the target power plant, divides the prediction time of the target power plant into power prediction periods for segmented wind power prediction, and improves the wind power prediction accuracy of the target power plant; in addition, the present application analyzes the power plant environmental data of the target power plant according to the power prediction period to obtain the first power influence coefficient of the target power plant within the power prediction period; obtains the wind direction deviation coefficient of the wind turbine by analyzing the wind direction prediction data of the target power plant and the wind turbine direction data of the wind turbine according to the power prediction period; and obtains the wind energy capture coefficient of the wind turbine based on the wind direction deviation coefficient and the wind speed prediction data of the target power plant, and obtains the wind energy capture coefficient based on the wind direction deviation coefficient and the wind energy capture coefficient. The second power influence coefficient of the target power plant within the power forecast period; the third power influence coefficient of the target power plant within the power forecast period is obtained by analyzing the wind speed forecast data of the target power plant and the fan configuration data of the wind turbine; finally, the wind power influence coefficient of the target power plant within the power forecast period is obtained based on the first power influence coefficient, the second power influence coefficient and the third power influence coefficient; in addition, the fan configuration coefficient of the target power plant is obtained by analyzing the fan configuration data of the target power plant; and the periodic power forecast value of the target power plant in the power forecast period is obtained based on the fan configuration coefficient and the wind power influence coefficient, and finally, the wind power forecast value of the target power plant within the forecast time is obtained based on the periodic power forecast value, thereby improving the comprehensiveness and accuracy of the wind power forecast result. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the technical solutions of the embodiments of the present application, some of the drawings in the embodiments of the present application will be briefly described below. It should be understood that the following drawings only show some embodiments of the present application and therefore should not be considered as limiting the scope of the present application.

[0081] Figure 1 A schematic diagram of a flow chart of a wind power prediction method based on data analysis provided in an embodiment of the present application;

[0082] Figure 2 A schematic diagram of the structure of a wind power prediction system based on data analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0083] Below is a combination of the embodiments and Figure 1 and Figure 2 The present application is further described in detail, but the implementation methods of the present application are not limited thereto.

[0084] Reference Figure 1 As shown, it is a flow chart of a wind power prediction method based on data analysis provided in an embodiment of the present application. In the embodiment of the present application, the application scenario is to convert the prediction time of the target power plant into a power prediction period, and obtain the first power influence coefficient, the second power influence coefficient and the third power influence coefficient of the target power plant in the power prediction period according to the power prediction period, and obtain the wind power influence coefficient of the target power plant in the power prediction period according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient. In addition, the wind turbine configuration data of the target power plant is analyzed to obtain the wind turbine configuration coefficient of the target power plant. Finally, the periodic power prediction value of the target power plant in the power prediction period is obtained according to the wind turbine configuration coefficient and the wind power influence coefficient, and the wind power prediction value of the target power plant within the prediction time is obtained according to the periodic power prediction value. This is different from the prior art that the prediction of wind power often does not fully consider the influencing factors that cause wind power fluctuations, which easily leads to the problem that the wind power prediction results lack comprehensiveness and accuracy.

[0085] A wind power prediction method based on data analysis comprises the following steps:

[0086] Acquire wind turbine data of a target power plant, and acquire power plant environment data and wind energy data of the target power plant within a forecast period;

[0087] The power plant wind turbine data includes the wind turbine configuration data, wind turbine orientation data and wind turbine layout data of the wind turbines in the target power plant; the power plant wind energy data includes the wind speed prediction data and wind direction prediction data of the target power plant within the prediction time;

[0088] Setting a power forecast period of the target power plant according to the wind speed forecast data and wind direction forecast data of the target power plant;

[0089] Analyzing the power plant environment data of the target power plant according to the power forecast period to obtain the first power influence coefficient of the target power plant within the power forecast period;

[0090] The wind direction prediction data of the target power plant and the wind turbine orientation data of the wind turbine are analyzed according to the power prediction period to obtain the wind direction deviation coefficient of the wind turbine; and the wind energy capture coefficient of the wind turbine is obtained according to the wind direction deviation coefficient and the wind speed prediction data of the target power plant;

[0091] The second power influence coefficient of the target power plant within the power forecast period is obtained according to the wind direction deviation coefficient and the wind energy capture coefficient;

[0092] Analyze the wind speed prediction data of the target power plant and the fan configuration data of the fan to obtain the fan influence coefficient of the fan in the target power plant, and obtain the standard layout interval of the fan in the target power plant according to the fan influence coefficient;

[0093] Compare and analyze the fan layout data of the fans in the target power plant with the standard layout interval to obtain a layout analysis result, and obtain a third power influence coefficient of the target power plant within a power forecast period based on the layout analysis result;

[0094] Obtaining a wind power influence coefficient of a target power plant within a power forecast period according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient;

[0095] Analyze the fan configuration data of the target power plant to obtain the fan configuration coefficient of the target power plant;

[0096] The periodic power forecast value of the target power plant in the power forecast period is obtained according to the wind turbine configuration coefficient and the wind power influence coefficient, and the wind power forecast value of the target power plant in the forecast time is obtained according to the periodic power forecast value.

[0097] The power plant environment data, power plant fan data and power plant wind energy data of the target power plant are obtained; the power plant fan data include the fan configuration data, fan orientation data and fan layout data of the fans in the target power plant; the power plant wind energy data include the wind speed prediction data and wind direction prediction data of the target power plant, specifically:

[0098] The wind turbine configuration data includes the wind turbine efficiency and wind turbine diameter of the wind turbines in the target power plant;

[0099] The wind turbine orientation data includes wind turbine orientation angles of wind turbines in the target power plant;

[0100] The wind turbine layout data includes wind turbine layout intervals of wind turbines in the target power plant;

[0101] The power plant environmental data includes the predicted temperature and air pressure of the target power plant within the predicted time;

[0102] The wind speed prediction data includes the wind speed prediction value of the target power plant within the prediction time; the wind direction prediction data includes the wind direction prediction angle of the target power plant within the prediction time.

[0103] It should be noted that the wind turbine efficiency refers to the wind turbine power generation efficiency of the wind turbine in the target power plant; the wind turbine diameter refers to the rotor diameter of the wind turbine in the target power plant;

[0104] In addition, the fan direction angle refers to the direction angle of the fan rotor, and the fan direction angle and the wind direction prediction angle have the same reference. For example, the fan direction angle and the wind direction prediction angle are both based on the fan initial axis as a reference. The angle between the fan rotor and the fan initial axis is the fan direction angle, and the predicted angle between the wind direction and the fan initial axis is the wind direction prediction angle. The fan layout interval refers to the straight-line distance between the fans.

[0105] The power forecast period of the target power plant is set according to the wind speed forecast data and wind direction forecast data of the target power plant, specifically:

[0106] According to the wind speed forecast value of the target power plant within the forecast time, the wind speed forecast mean value and the wind speed forecast maximum value of the target power plant within the forecast time are obtained;

[0107] The wind speed forecast mean value of the target power plant within the forecast time is taken and marked to obtain the wind speed forecast mean value YJ; the wind speed forecast maximum value of the target power plant within the forecast time is taken and marked to obtain the wind speed forecast maximum value YM;

[0108] By first calculating the function The first cycle influence coefficient OZX of the target power plant is calculated, where a1 and a2 are preset proportional factors;

[0109] A wind direction angle monitoring period is set according to the forecast time, and the wind direction angle monitoring period corresponds to a wind direction angle change threshold, wherein the forecast time includes at least one wind direction angle monitoring period;

[0110] According to the wind direction prediction angle of the target power plant within the prediction time, the change value of the wind direction prediction angle of the target power plant within the wind direction monitoring period is obtained;

[0111] Compare and analyze the predicted wind direction angle change value of the target power plant within the wind direction angle monitoring period with the wind direction angle change threshold;

[0112] If the wind direction angle change value of the target power plant during the wind direction angle monitoring period is less than the wind direction angle change threshold, the wind direction angle change value is marked as a normal predicted angle change value;

[0113] If the wind direction angle change value of the target power plant during the wind direction angle monitoring period is greater than or equal to the wind direction angle change threshold, the wind direction angle change value is marked as an abnormal predicted angle change value;

[0114] Obtain the total number of normal change values ​​of the normal predicted angle change value, and obtain the total number of abnormal change values ​​of the abnormal predicted angle change value;

[0115] The total number of normal change values ​​is taken and marked to obtain the total number of normal change values ​​ZS; the total number of abnormal change values ​​is taken and marked to obtain the total number of abnormal change values ​​YS; the abnormal predicted angle change value is taken and marked to obtain the abnormal predicted angle change value BZ; the wind direction angle change threshold is taken and marked to obtain the wind direction angle change threshold BY;

[0116] Through the second calculation function The second cycle influence coefficient TZX of the target power plant is calculated, where b1 and b2 are preset proportional factors, and j is the number of the abnormal prediction angle change value;

[0117] The prediction time of the target power plant is taken and marked to obtain the prediction time CS;

[0118] Through the third calculation function The power forecast period YT of the target power plant is calculated, where c1 and c2 are preset proportional factors.

[0119] In some embodiments, if the predicted mean wind speed of the target power plant within the prediction time is 10 m / s, the predicted mean wind speed YJ is 10; if the predicted maximum wind speed of the target power plant within the prediction time is 20 m / s, the predicted maximum wind speed YM is 20; if the preset proportional factors a1 and a2 are 1 and 0.5, then the first calculation function The first cycle influence coefficient OZX of the target power plant is calculated to be 20;

[0120] If the prediction time of the target power plant is one day, the prediction time CS takes the value of 1. If the first cycle influence coefficient OZX of the target power plant is 20, the second cycle influence coefficient TZX of the target power plant is 30, and the preset proportional factors c1 and c2 are 0.01 and 0.01, then the third calculation function The power forecast period YT of the target power plant is calculated to be 0.5 days, that is, the power forecast period YT of the target power plant is 12 hours.

[0121] The environmental data of the power plant is analyzed according to the power forecast period to obtain the first power impact coefficient of the target power plant within the power forecast period, which is:

[0122] Obtain the predicted mean temperature and predicted mean air pressure of the target power plant within the power prediction period;

[0123] The temperature prediction mean is taken and marked to obtain the temperature prediction mean CQ; the air pressure prediction mean is taken and marked to obtain the air pressure prediction mean CW;

[0124] Through the fourth calculation function The first power influence coefficient OYX of the target power plant in the power forecast period is calculated, where Preset scale factor.

[0125] In some embodiments, if the predicted average temperature of the target power plant during the power forecast period is 20°C, the predicted average temperature value CQ is 20; if the predicted average air pressure of the target power plant during the power forecast period is 100 kPa, the predicted average air pressure value CW is 100; if the preset proportionality factor is 100, then through the fourth calculation function The first power influence coefficient OYX of the target power plant in the power forecast period is calculated to be 20.

[0126] According to the power forecast period, the wind direction forecast data of the target power plant and the wind turbine orientation data of the wind turbine are analyzed to obtain the wind direction deviation coefficient of the wind turbine; and the wind energy capture coefficient of the wind turbine is obtained according to the wind direction deviation coefficient and the wind speed forecast data of the target power plant; the second power influence coefficient of the target power plant in the power forecast period is obtained according to the wind direction deviation coefficient and the wind energy capture coefficient, which is specifically:

[0127] The wind direction prediction angle of the target power plant is obtained by taking and marking the wind direction prediction angle FJ; the fan direction angle of the fan in the target power plant is obtained by taking and marking the fan direction angle CJ;

[0128] Through the fifth calculation function The wind direction deviation coefficient FPX of the fan is calculated, where is the preset scaling factor;

[0129] Obtain the periodic wind speed forecast mean value of the target power plant within the power forecast period, and take and mark the periodic wind speed forecast mean value to obtain the periodic wind speed forecast mean value FS;

[0130] Through the sixth calculation function The wind energy capture coefficient FBX of the wind turbine is calculated, where d is the preset proportional factor;

[0131] Through the seventh calculation function The second power influence coefficient TYX of the target power plant within the power forecast period is calculated, wherein e1 and e2 are preset proportional factors.

[0132] In some embodiments, if the wind direction prediction angle of the target power plant is 50 degrees, the wind direction prediction angle FJ is 50; if the fan direction angle of the fan in the target power plant is 10 degrees, the fan direction angle CJ is 10; if the preset proportionality factor is 0.02, then through the fifth calculation function The wind direction deviation coefficient FPX of the fan is calculated to be 0.8;

[0133] If the target power plant's predicted mean wind speed during the power forecast period is 10 m / s, the predicted mean wind speed FS is 10. If the wind direction deviation coefficient FPX of the wind turbine is 0.8 and the preset proportional factor d is 1, then the sixth calculation function The wind energy capture coefficient FBX of the wind turbine is calculated to be 8; if the preset proportional factors e1 and e2 are 10 and 1, respectively, then the seventh calculation function The second power influence coefficient TYX of the target power plant in the power forecast period is calculated to be 16.

[0134] According to the wind speed prediction data of the target power plant and the fan configuration data of the fan, the fan influence coefficient of the fan in the target power plant is obtained, and the standard layout interval of the fan in the target power plant is obtained according to the fan influence coefficient, which is specifically:

[0135] The fan efficiency of the fan in the target power plant is obtained by taking values ​​and marking the fan efficiency JL; the fan diameter of the fan in the target power plant is obtained by taking values ​​and marking the fan diameter JD;

[0136] Through the eighth calculation function The fan influence coefficient JY of the fan of the target power plant in the predicted power cycle is calculated, where f1, f2, and f3 are preset proportional factors;

[0137] Through the ninth calculation function The standard layout interval AJ of the wind turbines in the target power plant during the predicted power cycle is calculated, where is the preset scaling factor;

[0138] The fan layout data of the fans in the target power plant are compared and analyzed with the standard layout interval to obtain the layout analysis results, and the third power influence coefficient of the target power plant in the power forecast period is obtained according to the layout analysis results, which is specifically:

[0139] If the fan layout intervals of the target power plant fans are all greater than or equal to the standard layout interval AJ during the predicted power cycle, the target power plant does not have a third power influence coefficient;

[0140] If there are wind turbines in the target power plant with a wind turbine layout interval smaller than the standard layout interval AJ within the predicted power cycle, the target power plant has a third power influence coefficient, and the wind turbines with a wind turbine layout interval smaller than the standard layout interval AJ are marked as abnormal wind turbines, and the wind turbine layout interval of the abnormal wind turbines is marked as abnormal layout interval;

[0141] Obtain the abnormal proportion of abnormal wind turbines in the target power plant, and take and mark the abnormal proportion to obtain the abnormal proportion ZB; take and mark the abnormal layout interval to obtain the abnormal layout interval BJ;

[0142] Through the tenth calculation function The third power influence coefficient SYX of the target power plant is calculated, wherein g1 and g2 are preset proportional factors, u is the number of the abnormal wind turbine, and m is the number of the abnormal wind turbine.

[0143] In some embodiments, if the fan efficiency of the fan in the target power plant is 20%, the fan efficiency JL is 20; if the fan diameter of the fan in the target power plant is 20 meters, the fan diameter JD is 20; if the periodic wind speed prediction mean FS of the target power plant is 10; if the preset proportional factors f1, f2, and f3 are 1, 1, and 1, then through the eighth calculation function The fan influence coefficient JY of the target power plant fan in the predicted power cycle is calculated to be 50;

[0144] At this time, if the preset scaling factor is 0.5, then through the ninth calculation function The calculation shows that the standard layout spacing AJ of the wind turbines in the target power plant during the predicted power cycle is 25 meters;

[0145] If the abnormal proportion of abnormal wind turbines in the target power plant is 20%, the abnormal proportion ZB is 20; if the wind turbine layout interval of the wind turbines in the target power plant is 15 meters, the wind turbine layout interval BJ is 15.

[0146] The wind power influence coefficient of the target power plant within the power forecast period is obtained according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient, specifically:

[0147] By the eleventh calculation function The wind power influence coefficient FGX of the target power plant within the power forecast period is calculated, where h1, h2, and h3 are preset proportional factors;

[0148] If the wind power influence coefficient FGX of the target power plant is less than or equal to the preset wind power influence coefficient threshold, it is determined that the wind power influence coefficient FGX of the target power plant is normal;

[0149] If the wind power influence coefficient FGX of the target power plant is greater than the preset wind power influence coefficient threshold, the wind power influence coefficient FGX of the target power plant is judged to be abnormal, and the preset wind power influence coefficient threshold is taken and marked to obtain the wind power influence coefficient threshold YXY;

[0150] Then through the twelfth calculation function Calculate the wind power influence coefficient FGX corresponding to the target power plant with abnormal wind power influence coefficient , where k1 and k2 are preset scaling factors.

[0151] In some embodiments, if the first power influence coefficient OYX of the target power plant is 20, the second power influence coefficient TYX of the target power plant is 20, the third power influence coefficient SYX of the target power plant is 10, and the preset proportional factors h1, h2, and h3 are 0.5, 1, and 1, then through the eleventh calculation function The wind power influence coefficient FGX of the target power plant during the power forecast period is calculated to be 40;

[0152] At this time, if the wind power influence coefficient threshold of the target power plant is 30, then the wind power influence coefficient threshold YXY is 30. If the preset proportional factors k1 and k2 are 1 and 1, respectively, then the twelfth calculation function Calculate the wind power influence coefficient FGX corresponding to the target power plant with abnormal wind power influence coefficient is 50.

[0153] The fan configuration data of the target power plant is analyzed to obtain the fan configuration coefficient of the target power plant, which is specifically:

[0154] Obtain the total number of wind turbines in the target power plant, and take values ​​and mark the total number of wind turbines to obtain the total number of wind turbines FZ;

[0155] Through the thirteenth calculation function The wind turbine configuration factor JPX of the target power plant is calculated, where p1 and p2 are preset proportional factors.

[0156] In some embodiments, if the total number of wind turbines in the target power plant is 5, the total number of wind turbines FZ is 5; if the wind turbine efficiency JL of the target power plant is 20, the wind turbine diameter JD of the target power plant is 20; the preset proportional factors p1 and p2 are 0.5 and 0.5, then through the thirteenth calculation function The wind turbine configuration factor JPX of the target power plant is calculated to be 100.

[0157] The periodic power forecast value of the target power plant in the power forecast period is obtained based on the wind turbine configuration coefficient and the wind power influence coefficient, and the wind power forecast value of the target power plant in the forecast time is obtained based on the periodic power forecast value, which is specifically:

[0158] By calculating the function The periodic power forecast value GJY of the target power plant in the power forecast period is calculated, where is the preset scaling factor;

[0159] By the fifteenth calculation function The wind power forecast value FGY of the target power plant within the forecast time is calculated, where i is the cycle number of the power forecast cycle within the forecast time, and n is the cycle number of the power forecast cycle.

[0160] In some embodiments, if the wind turbine configuration coefficient JPX of the target power plant is 100, and the wind power influence coefficient FGX of the target power plant is 20, the preset proportional factor is 1, then through the fourteenth calculation function The calculated periodic power forecast value GJY of the target power plant in the power forecast period is 2000w.

[0161] Reference Figure 2 FIG. 1 is a schematic diagram of a wind power prediction system based on data analysis provided in an embodiment of the present application, the system comprising:

[0162] Acquisition module: acquiring the wind turbine data of the target power plant, and acquiring the power plant environment data and wind energy data of the target power plant within the forecast time;

[0163] The power plant wind turbine data includes the wind turbine configuration data, wind turbine orientation data and wind turbine layout data of the wind turbines in the target power plant; the power plant wind energy data includes the wind speed prediction data and wind direction prediction data of the target power plant within the prediction time;

[0164] Setting module: setting the power prediction period of the target power plant according to the wind speed prediction data and wind direction prediction data of the target power plant;

[0165] The first analysis module: analyzes the power plant environment data of the target power plant according to the power forecast period to obtain the first power influence coefficient of the target power plant within the power forecast period;

[0166] The second analysis module: analyzes the wind direction prediction data of the target power plant and the wind turbine orientation data of the wind turbine according to the power prediction period to obtain the wind direction deviation coefficient of the wind turbine; and obtains the wind energy capture coefficient of the wind turbine according to the wind direction deviation coefficient and the wind speed prediction data of the target power plant;

[0167] The second power influence coefficient of the target power plant within the power forecast period is obtained according to the wind direction deviation coefficient and the wind energy capture coefficient;

[0168] The third analysis module: performs analysis based on the wind speed prediction data of the target power plant and the fan configuration data of the fan to obtain the fan influence coefficient of the fan in the target power plant, and obtains the standard layout interval of the fan in the target power plant based on the fan influence coefficient;

[0169] Compare and analyze the fan layout data of the fans in the target power plant with the standard layout interval to obtain a layout analysis result, and obtain a third power influence coefficient of the target power plant within a power forecast period based on the layout analysis result;

[0170] Obtaining a wind power influence coefficient of a target power plant within a power forecast period according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient;

[0171] The fourth analysis module: analyzing the fan configuration data of the target power plant to obtain the fan configuration coefficient of the target power plant;

[0172] Wind power prediction module: obtains the periodic power prediction value of the target power plant in the power prediction period based on the wind turbine configuration coefficient and the wind power influence coefficient, and obtains the wind power prediction value of the target power plant within the prediction time based on the periodic power prediction value.

[0173] 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 wind power prediction method based on data analysis, It is characterized in that The following steps are involved: Acquire wind turbine data of a target power plant, and acquire power plant environment data and wind energy data of the target power plant within a forecast period; The power plant wind turbine data includes wind turbine configuration data, wind turbine orientation data and wind turbine layout data of the wind turbines in the target power plant; the power plant wind energy data includes wind speed prediction data and wind direction prediction data of the target power plant within the prediction time; Setting a power forecast period of the target power plant according to the wind speed forecast data and wind direction forecast data of the target power plant; Analyzing the power plant environment data of the target power plant according to the power forecast period to obtain a first power influence coefficient of the target power plant within the power forecast period; Analyzing the wind direction prediction data of the target power plant and the wind turbine orientation data of the wind turbine according to the power prediction period to obtain the wind direction deviation coefficient of the wind turbine; and obtaining the wind energy capture coefficient of the wind turbine according to the wind direction deviation coefficient and the wind speed prediction data of the target power plant; Obtaining a second power influence coefficient of the target power plant within a power forecast period according to the wind direction deviation coefficient and the wind energy capture coefficient; Analyze the wind speed prediction data of the target power plant and the fan configuration data of the fan to obtain the fan influence coefficient of the fan in the target power plant, and obtain the standard layout interval of the fan in the target power plant according to the fan influence coefficient; Comparing and analyzing the fan layout data of the fans in the target power plant with the standard layout interval to obtain a layout analysis result, and obtaining a third power influence coefficient of the target power plant within a power forecast period according to the layout analysis result; Obtaining a wind power influence coefficient of a target power plant within a power forecast period according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient; Analyzing the fan configuration data of the target power plant to obtain the fan configuration coefficient of the target power plant; The periodic power forecast value of the target power plant in the power forecast period is obtained according to the wind turbine configuration coefficient and the wind power influence coefficient, and the wind power forecast value of the target power plant in the forecast time is obtained according to the periodic power forecast value.

2. A wind power prediction method based on data analysis according to claim 1, It is characterized in that Acquire power plant environment data, power plant fan data and power plant wind energy data of the target power plant; the power plant fan data includes fan configuration data, fan orientation data and fan layout data of the fans in the target power plant; the power plant wind energy data includes wind speed prediction data and wind direction prediction data of the target power plant, specifically: The wind turbine configuration data includes wind turbine efficiency and wind turbine diameter of wind turbines in the target power plant; The wind turbine orientation data includes wind turbine orientation angles of wind turbines in the target power plant; The wind turbine layout data includes wind turbine layout intervals of wind turbines in the target power plant; The power plant environmental data includes a predicted temperature value and a predicted air pressure value of the target power plant within a predicted time; The wind speed prediction data includes the wind speed prediction value of the target power plant within the prediction time; the wind direction prediction data includes the wind direction prediction angle of the target power plant within the prediction time.

3. A wind power prediction method based on data analysis according to claim 2, It is characterized in that The power forecast period of the target power plant is set according to the wind speed forecast data and wind direction forecast data of the target power plant, specifically: According to the wind speed forecast value of the target power plant within the forecast time, the wind speed forecast mean value and the wind speed forecast maximum value of the target power plant within the forecast time are obtained; The wind speed forecast mean value of the target power plant within the forecast time is taken and marked to obtain the wind speed forecast mean value YJ; the wind speed forecast maximum value of the target power plant within the forecast time is taken and marked to obtain the wind speed forecast maximum value YM; By first calculating the function The first cycle impact coefficient OZX of the target power plant is calculated, where a 1 、a 2 is the preset scaling factor; A wind direction angle monitoring period is set according to the prediction time, wherein the wind direction angle monitoring period corresponds to a wind direction angle change threshold, wherein the prediction time includes at least one wind direction angle monitoring period; According to the wind direction prediction angle of the target power plant within the prediction time, the change value of the wind direction prediction angle of the target power plant within the wind direction monitoring period is obtained; Compare and analyze the predicted wind direction angle change value of the target power plant within the wind direction angle monitoring period with the wind direction angle change threshold; If the wind direction angle change value of the target power plant during the wind direction angle monitoring period is less than the wind direction angle change threshold, the wind direction angle change value is marked as a normal predicted angle change value; If the wind direction angle change value of the target power plant during the wind direction angle monitoring period is greater than or equal to the wind direction angle change threshold, the wind direction angle change value is marked as an abnormal predicted angle change value; Obtain the total number of normal change values ​​of the normal predicted angle change value, and obtain the total number of abnormal change values ​​of the abnormal predicted angle change value; The total number of normal change values ​​is taken and marked to obtain the total number of normal change values ​​ZS; the total number of abnormal change values ​​is taken and marked to obtain the total number of abnormal change values ​​YS; the abnormal predicted angle change value is taken and marked to obtain the abnormal predicted angle change value BZ; the wind direction angle change threshold is taken and marked to obtain the wind direction angle change threshold BY; Through the second calculation function The second cycle impact coefficient TZX of the target power plant is calculated, where b 1 , b 2 is the preset scale factor, j is the number of the abnormal prediction angle change value; The prediction time of the target power plant is taken and marked to obtain the prediction time CS; Through the third calculation function The power forecast period YT of the target power plant is calculated, where c 1 、c 2 is the preset scale factor.

4. A wind power prediction method based on data analysis according to claim 3, It is characterized in that The power plant environmental data is analyzed according to the power forecast period to obtain a first power impact coefficient of the target power plant within the power forecast period, specifically: Obtain the predicted mean temperature and predicted mean air pressure of the target power plant within the power prediction period; The predicted temperature mean is taken and marked to obtain the predicted temperature mean CQ; the predicted air pressure mean is taken and marked to obtain the predicted air pressure mean CW; Through the fourth calculation function The first power influence coefficient OYX of the target power plant in the power forecast period is calculated, where Preset scale factor.

5. A wind power prediction method based on data analysis according to claim 4, It is characterized in that The wind direction prediction data of the target power plant and the wind turbine orientation data of the wind turbine are analyzed according to the power prediction period to obtain the wind direction deviation coefficient of the wind turbine; and the wind energy capture coefficient of the wind turbine is obtained according to the wind direction deviation coefficient and the wind speed prediction data of the target power plant; and the second power influence coefficient of the target power plant within the power prediction period is obtained according to the wind direction deviation coefficient and the wind energy capture coefficient, which is specifically: The wind direction prediction angle of the target power plant is obtained by taking and marking the wind direction prediction angle FJ; the fan direction angle of the fan in the target power plant is obtained by taking and marking the fan direction angle CJ; Through the fifth calculation function The wind direction deviation coefficient FPX of the fan is calculated, where is the preset scaling factor; Obtaining a periodic wind speed forecast mean value of a target power plant within a power forecast period, and taking and marking the periodic wind speed forecast mean value to obtain a periodic wind speed forecast mean value FS; Through the sixth calculation function The wind energy capture coefficient FBX of the wind turbine is calculated, where d is a preset proportional factor; Through the seventh calculation function The second power influence coefficient TYX of the target power plant in the power forecast period is calculated, where e 1 、e 2 is the preset scale factor.

6. A wind power prediction method based on data analysis according to claim 5, It is characterized in that According to the wind speed prediction data of the target power plant and the fan configuration data of the fan, the fan influence coefficient of the fan in the target power plant is obtained, and the standard layout interval of the fan in the target power plant is obtained according to the fan influence coefficient, which is specifically: The fan efficiency of the fan in the target power plant is obtained by taking values ​​and marking the fan efficiency JL; the fan diameter of the fan in the target power plant is obtained by taking values ​​and marking the fan diameter JD; Through the eighth calculation function The fan influence coefficient JY of the target power plant fan in the predicted power cycle is calculated, where f 1 、f 2 、f 3 is the preset scaling factor; Through the ninth calculation function The standard layout interval AJ of the wind turbines in the target power plant during the predicted power cycle is calculated, where is the preset scaling factor; The fan layout data of the fans in the target power plant are compared and analyzed with the standard layout interval to obtain a layout analysis result, and the third power influence coefficient of the target power plant in the power forecast period is obtained according to the layout analysis result, which is specifically: If the fan layout intervals of the target power plant fans are all greater than or equal to the standard layout interval AJ within the predicted power cycle, then the target power plant does not have a third power influence coefficient; If, in the predicted power cycle, there are wind turbines in the target power plant with a wind turbine layout interval smaller than the standard layout interval AJ, the target power plant has a third power influence coefficient, and the wind turbines with a wind turbine layout interval smaller than the standard layout interval AJ are marked as abnormal wind turbines, and the wind turbine layout interval of the abnormal wind turbines is marked as an abnormal layout interval; Obtaining the abnormal proportion of the abnormal wind turbines in the target power plant, and taking values ​​and marking the abnormal proportion to obtain the abnormal proportion ZB; taking values ​​and marking the abnormal layout interval to obtain the abnormal layout interval BJ; Through the tenth calculation function The third power influence coefficient SYX of the target power plant is calculated, where g 1 , g 2 is the preset proportional factor, u is the number of the abnormal fan, and m is the number of abnormal fans.

7. A wind power prediction method based on data analysis according to claim 6, It is characterized in that The wind power influence coefficient of the target power plant within the power forecast period is obtained according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient, specifically: By the eleventh calculation function The wind power influence coefficient FGX of the target power plant in the power forecast period is calculated, where h 1 、h 2 、h 3 is the preset scaling factor; If the wind power influence coefficient FGX of the target power plant is less than or equal to the preset wind power influence coefficient threshold, it is determined that the wind power influence coefficient FGX of the target power plant is normal; If the wind power influence coefficient FGX of the target power plant is greater than the preset wind power influence coefficient threshold, the wind power influence coefficient FGX of the target power plant is judged to be abnormal, and the preset wind power influence coefficient threshold is taken and marked to obtain the wind power influence coefficient threshold YXY; Then through the twelfth calculation function Calculate the wind power influence coefficient FGX corresponding to the target power plant with abnormal wind power influence coefficient , where k 1 , k 2 is the preset scale factor.

8. A wind power prediction method based on data analysis according to claim 7, It is characterized in that The fan configuration data of the target power plant is analyzed to obtain the fan configuration coefficient of the target power plant, which is specifically: Obtaining the total number of wind turbines in the target power plant, and taking values ​​and marking the total number of wind turbines to obtain the total number of wind turbines FZ; Through the thirteenth calculation function The wind turbine configuration factor JPX of the target power plant is calculated, where p 1 、p 2 Preset scale factor.

9. A wind power prediction method based on data analysis according to claim 8, It is characterized in that The periodic power forecast value of the target power plant in the power forecast period is obtained according to the wind turbine configuration coefficient and the wind power influence coefficient, and the wind power forecast value of the target power plant in the forecast time is obtained according to the periodic power forecast value, specifically: By calculating the function The periodic power forecast value GJY of the target power plant in the power forecast period is calculated, where is the preset scaling factor; By the fifteenth calculation function The wind power forecast value FGY of the target power plant within the forecast time is calculated, where i is the cycle number of the power forecast cycle within the forecast time, and n is the cycle number of the power forecast cycle.

10. A wind power prediction system based on data analysis, the system being used to implement a wind power prediction method based on data analysis as claimed in any one of claims 1 to 9, It is characterized in that The system includes: Acquisition module: acquiring the wind turbine data of the target power plant, and acquiring the power plant environment data and wind energy data of the target power plant within the forecast time; The power plant wind turbine data includes wind turbine configuration data, wind turbine orientation data and wind turbine layout data of the wind turbines in the target power plant; the power plant wind energy data includes wind speed prediction data and wind direction prediction data of the target power plant within the prediction time; Setting module: setting the power prediction period of the target power plant according to the wind speed prediction data and wind direction prediction data of the target power plant; A first analysis module: analyzing the power plant environment data of the target power plant according to the power forecast period to obtain a first power influence coefficient of the target power plant within the power forecast period; The second analysis module: analyzes the wind direction prediction data of the target power plant and the wind turbine orientation data of the wind turbine according to the power prediction period to obtain the wind direction deviation coefficient of the wind turbine; and obtains the wind energy capture coefficient of the wind turbine according to the wind direction deviation coefficient and the wind speed prediction data of the target power plant; Obtaining a second power influence coefficient of the target power plant within a power forecast period according to the wind direction deviation coefficient and the wind energy capture coefficient; The third analysis module: performs analysis based on the wind speed prediction data of the target power plant and the fan configuration data of the fan to obtain the fan influence coefficient of the fan in the target power plant, and obtains the standard layout interval of the fan in the target power plant based on the fan influence coefficient; Comparing and analyzing the fan layout data of the fans in the target power plant with the standard layout interval to obtain a layout analysis result, and obtaining a third power influence coefficient of the target power plant within a power forecast period according to the layout analysis result; Obtaining a wind power influence coefficient of a target power plant within a power forecast period according to the first power influence coefficient, the second power influence coefficient and the third power influence coefficient; A fourth analysis module: analyzing the wind turbine configuration data of the target power plant to obtain the wind turbine configuration coefficient of the target power plant; Wind power prediction module: obtains the periodic power prediction value of the target power plant in the power prediction period according to the wind turbine configuration coefficient and the wind power influence coefficient, and obtains the wind power prediction value of the target power plant within the prediction time according to the periodic power prediction value.

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