Wind power station power prediction method and system based on geographical environment and climate

Abstract

The invention discloses a wind power plant power prediction method and system based on geographical environment and climate. The method comprises the following steps: S1, collecting historical power output data, geographical environment data and climate data of a wind power plant; s2, according to historical data, selecting geographical environment factors and climatic factors which have great influence on the power output of the wind power plant as characteristic variables; s3, determining the weight of each characteristic variable according to the influence degree of each characteristic variable on the wind power output; s4, using the weighted characteristic values as input, and establishing a wind power station power prediction model; and S5, inputting real-time or future geographical environment data and climate data into the trained power prediction model to obtain a power prediction result of the wind power station. According to the method, the accuracy and reliability of wind power prediction are remarkably improved by comprehensively considering the geographical environment and climate conditions, especially seasonal changes.

Description

Technical Field

[0001] The present invention belongs to the field of data prediction, and more specifically, it relates to a method for predicting wind farm power based on geographical environment and climate. At the same time, the present invention also relates to a system for predicting wind farm power based on geographical environment and climate. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, wind power generation has been widely used in many countries as a clean and renewable energy form. The power forecast of wind farms is of great significance for the dispatching and optimization of power grids and improving the utilization rate of wind energy. However, the power output of wind farms is affected by many factors, including geographical environment, climate conditions, etc. The changes of these factors make the power output of wind farms have certain uncertainty and volatility.

[0003] Existing wind power prediction methods are mainly divided into two categories: physical methods (based on numerical weather forecast models) and statistical methods (based on historical data). Physical methods rely on high-precision meteorological data and complex physical models. Although the prediction accuracy is high, the amount of calculation is large and the requirements for data quality and model parameters are high. Statistical methods establish prediction models by analyzing historical data. They have the advantages of simple calculation and easy implementation, but they often ignore the impact of geographical environment and climate conditions on wind power, resulting in inaccurate prediction results.

[0004] In addition, existing prediction methods usually do not take into account the impact of seasonal climate change on wind power, as well as the seasonal changes in geographical environment data. For example, climate factors such as temperature, humidity, wind speed, wind direction, and air pressure will have different performances in different seasons, and changes in these factors will have a significant impact on wind power. Therefore, a wind power prediction method that can comprehensively consider geographical environment and climate factors is needed to improve the accuracy and reliability of the prediction.

[0005] In summary, the existing technology lacks a wind farm power prediction method that can fully consider geographical environment and climate factors, especially a prediction model that can adapt to seasonal changes. Therefore, developing a wind farm power prediction method that combines geographical environment and climate conditions is of great significance to improving the accuracy and reliability of wind power prediction. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings existing in the prior art, and to propose a wind farm power prediction method and system based on geographical environment and climate. By comprehensively considering the geographical environment and climatic conditions, especially seasonal changes, the accuracy and reliability of wind power prediction are significantly improved. At the same time, the method is easy to implement and apply, providing users with an effective tool to manage and optimize the operation of wind farms.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for predicting wind farm power based on geographical environment and climate includes the following steps:

[0009] S1. Collect historical power output data, geographical environment data and climate data of wind farms, wherein the geographical environment data includes topography, altitude and surface roughness; the climate data includes wind speed, wind direction, temperature, humidity and air pressure;

[0010] S2. Based on historical data, select geographical environment factors and climate factors that have a greater impact on the power output of wind farms as characteristic variables, and clean and standardize the collected data to eliminate the impact of different dimensions;

[0011] S3. Determine the weight of each characteristic variable according to the degree of influence of each characteristic variable on wind power output, multiply each characteristic variable by its corresponding weight to obtain a weighted characteristic value, and calculate the weight values ​​of the geographical environment and climate respectively according to the weighted characteristic values;

[0012] S4. Use the weighted eigenvalues ​​as input to establish a wind farm power prediction model, use historical data to train the power prediction model, and use a portion of the data for cross-validation to evaluate the accuracy and generalization ability of the model;

[0013] S5. Input the real-time or future geographic environment data and climate data into the trained power prediction model to obtain the power prediction result of the wind farm station, and display the predicted wind farm station power output result to the user in the form of a graph or table.

[0014] Preferably, in step S1, the historical power output data of the wind farm is obtained from the operation record of the wind farm, the geographical environment data is obtained through a geographic information system or on-site measurement, and the meteorological data is obtained from a meteorological station or a numerical weather forecast model;

[0015] The step S1 also includes preprocessing the collected data, and using a method combining double quartile and clustering to eliminate abnormal data during power cuts and fan failures.

[0016] Preferably, in step S2, those geographical environment factors and climate factors that have a greater impact on the power output of the wind farm are identified and selected as the characteristic variables of the model, specifically:

[0017] Based on the professional knowledge in the field of wind power, determine which factors may have a significant impact on wind power, calculate the correlation coefficient between each factor and wind power output, and select the one with the highest correlation as the feature. The expression is:

[0018] ;

[0019] In the formula, r is the correlation coefficient, x and y are the data sets of two variables, and n is the number of samples;

[0020] The standardization process is achieved by processing the outliers, specifically:

[0021] Use the interquartile range to identify outliers, expressed as:

[0022] ;

[0023] ;

[0024] In the formula, Q1 and Q3 are the first and third quartiles of the data set, respectively, IQR is the difference between the third quartile and the first quartile, and the definition of outliers is based on IQR. Any data point less than Q1−1.5×IQR or greater than Q3+1.5×IQR is considered an outlier.

[0025] Preferably, the data normalization is used to eliminate the influence of different dimensions and ranges so that all features contribute equally to model training, and the expression is:

[0026] ;

[0027] In the formula, z is the standardized value, x is the original data, μ is the mean of the data, and σ is the standard deviation of the data.

[0028] Preferably, the weighted value expression of the geographical environment data in step S3 is:

[0029] W DL =W D +W H +W C ;

[0030] Where W DL Indicates the weight value of the geographical environment, WD represents the terrain weight, WH represents the altitude weight, and WC represents the surface roughness weight;

[0031] Among them, the weight value of the geographical environment is constant and is not affected by the four seasons;

[0032] The weighted value expression of the climate data is:

[0033] W QH =aW FS +aW FX +bW WD +bW SD +cW QY；

[0034] Where W QH The weight of climate data, W FS Represents the wind speed weight value, W FX Indicates the wind direction weight value, W WD Represents the temperature weight value, W SD Represents the humidity weight value, W QY Represents the air pressure weight value, where a, b, and c represent constants, and the maximum value is 1.

[0035] Preferably, due to the influence of the four seasons, the temperature, humidity, wind speed, wind direction and air pressure will change in stages in different seasons. Therefore, in different seasons, the values ​​of constants a, b, c are changed according to the actual situation, specifically:

[0036] The constant a is 0.7 in spring, 1 in summer, 0.5 in autumn, and 0.3 in winter;

[0037] The constant b is 0.5 in spring, 0.3 in summer, 0.5 in autumn, and 1 in winter;

[0038] The constant c is 0.7 in spring, 0.3 in summer, 0.7 in autumn, and 1 in winter.

[0039] Preferably, the specific process of step S4 is:

[0040] S41, obtaining historical wind speed, wind direction, temperature, humidity and air pressure data, as well as corresponding wind power output data, and determining the main factors affecting wind power;

[0041] S42, weighting the features according to their importance to obtain weighted feature values, and forming a data set, dividing the data set into a training set and a test set, with the ratio of 70% training set and 30% test set allocated;

[0042] S43, select a model, use the training set data to train the model, and analyze the performance of the model on the training set and the test set to check whether there is overfitting or underfitting;

[0043] S44. Select the best performing model as the final model.

[0044] Preferably, step S5 is specifically:

[0045] S51. Obtain geographical environmental data such as wind speed, wind direction, temperature, humidity and air pressure in the current or future period, and clean and format the data to ensure that it meets the requirements of model input;

[0046] S52, using the previously determined weighting coefficient, weighting each eigenvalue of the real-time data to obtain a weighted eigenvalue, and integrating all the weighted eigenvalues ​​into a eigenvector as the input of the model;

[0047] S53, loading the trained wind power prediction model into the system, and inputting the weighted feature vector into the model to obtain the power prediction result of the wind farm;

[0048] S54. Use a chart to show how the predicted wind power changes over time, and organize the predicted wind power and the corresponding time points into a table to facilitate users to view specific predicted values.

[0049] A wind farm power prediction system based on geographical environment and climate, the system is used to implement the above method, including:

[0050] Data collection and preprocessing module: This module is responsible for collecting historical power output data, geographical environment data and climate data of wind farms; at the same time, the collected data is cleaned and standardized to eliminate the impact of different dimensions;

[0051] Feature selection and weighting module: Based on historical data, this module selects geographical environment factors and climate factors that have a greater impact on the power output of wind farms as feature variables, and cleans and standardizes the collected data; then, according to the degree of influence of each feature variable on wind power output, the weight of each feature variable is determined, and each feature variable is multiplied by its corresponding weight to obtain the weighted feature value; finally, the weight values ​​of the geographical environment and climate are calculated respectively according to the weighted feature values;

[0052] Model training and prediction module: This module uses weighted eigenvalues ​​as input to establish a wind farm power prediction model; uses historical data to train the power prediction model, and uses a portion of the data for cross-validation to evaluate the accuracy and generalization ability of the model; then, inputs real-time or future geographic environment data and climate data into the trained power prediction model to obtain the power prediction results of the wind farm;

[0053] Result display module: This module displays the predicted wind farm power output results to the user in the form of graphs or tables; the user can intuitively understand the power forecast of the wind farm in different time periods;

[0054] System management and maintenance module: This module is responsible for the management and maintenance of the entire system, including data backup, recovery, and update operations; at the same time, the system can also be optimized and upgraded according to actual needs.

[0055] Technical effects and advantages of the present invention: Compared with the prior art, the present invention provides a method and system for predicting wind farm power based on geographical environment and climate, which has the following effects:

[0056] By comprehensively considering geographical environment and climate factors, this method can more accurately predict the power output of wind farms. By using weighted eigenvalues ​​and weight calculations, the model can better capture the impact of different factors on wind power, thereby improving the accuracy of the prediction. At the same time, the impact of seasonal climate change on wind power is considered. By adjusting the values ​​of constants a, b, and c, the model can adapt to changes in temperature, humidity, wind speed, wind direction, and air pressure in different seasons. Seasonal adjustment improves the prediction performance of the model in different seasons.

[0057] By processing outliers and standardizing data, this method eliminates the impact of different dimensions and ranges, makes all features contribute equally to model training, improves the stability and reliability of the model, combines historical data with real-time data, uses common statistical methods and machine learning models for prediction, and is easy to implement and apply. At the same time, by displaying the prediction results in charts, users can intuitively understand the power prediction of wind farms in different time periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of a method for predicting wind farm power based on geographical environment and climate according to the present invention;

[0059] Figure 2 A flow chart of establishing a wind farm power prediction model in an embodiment of the present invention;

[0060] Figure 3 The figure is a flow chart of predicting the power of a wind farm by using a model in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0062] The present invention provides a method and system for predicting wind farm power based on geographical environment and climate. The system includes a data collection and preprocessing module: the module is responsible for collecting historical power output data, geographical environment data and climate data of wind farms; at the same time, the collected data is cleaned and standardized to eliminate the influence of different dimensions;

[0063] Feature selection and weighting module: Based on historical data, this module selects geographical environment factors and climate factors that have a greater impact on the power output of wind farms as feature variables, and cleans and standardizes the collected data; then, according to the degree of influence of each feature variable on wind power output, the weight of each feature variable is determined, and each feature variable is multiplied by its corresponding weight to obtain the weighted feature value; finally, the weight values ​​of the geographical environment and climate are calculated respectively according to the weighted feature values;

[0064] Model training and prediction module: This module uses weighted eigenvalues ​​as input to establish a wind farm power prediction model; uses historical data to train the power prediction model, and uses a portion of the data for cross-validation to evaluate the accuracy and generalization ability of the model; then, inputs real-time or future geographic environment data and climate data into the trained power prediction model to obtain the power prediction results of the wind farm;

[0065] Result display module: This module displays the predicted wind farm power output results to the user in the form of graphs or tables; the user can intuitively understand the power forecast of the wind farm in different time periods;

[0066] System management and maintenance module: This module is responsible for the management and maintenance of the entire system, including data backup, recovery, and update operations; at the same time, the system can also be optimized and upgraded according to actual needs;

[0067] The above system management and maintenance module is responsible for the management and maintenance of the entire system, including data backup, recovery, and update operations, to ensure the stable operation and continuous optimization of the system.

[0068] Based on the above system, Figure 1 As shown, the wind farm power prediction method based on geographical environment and climate includes the following steps:

[0069] S1. Collect historical power output data, geographical environment data and climate data of the wind farm station, wherein the geographical environment data includes topography, altitude, and surface roughness; the climate data includes wind speed, wind direction, temperature, humidity and air pressure; in step S1, the historical power output data of the wind farm station is obtained from the operation record of the wind farm, the geographical environment data is obtained through a geographic information system or on-site measurement, and the meteorological data is obtained from a meteorological station or a numerical weather forecast model;

[0070] The step S1 also includes preprocessing the collected data, and using a method combining double quartile and clustering to eliminate abnormal data during power cuts and fan failures.

[0071] S2. According to historical data, geographical environmental factors and climatic factors that have a greater impact on the power output of wind farms are selected as characteristic variables, and the collected data are cleaned and standardized to eliminate the impact of different dimensions; in step S2, those geographical environmental factors and climatic factors that have a greater impact on the power output of wind farms are identified and selected as characteristic variables of the model, specifically:

[0072] Based on the professional knowledge in the field of wind power, determine which factors may have a significant impact on wind power, calculate the correlation coefficient between each factor and wind power output, and select the one with the highest correlation as the feature. The expression is:

[0073] ;

[0074] In the formula, r is the correlation coefficient, x and y are the data sets of two variables, and n is the number of samples;

[0075] The standardization process is achieved by processing the outliers, specifically:

[0076] Use the interquartile range to identify outliers, expressed as:

[0077] ;

[0078] ;

[0079] In the formula, Q1 and Q3 are the first and third quartiles of the data set, respectively, IQR is the difference between the third quartile and the first quartile, and the definition of outliers is based on IQR. Any data point less than Q1−1.5×IQR or greater than Q3+1.5×IQR is considered an outlier.

[0080] Furthermore, the data normalization is used to eliminate the influence of different dimensions and ranges so that all features contribute equally to model training. The expression is:

[0081] ;

[0082] In the formula, z is the standardized value, x is the original data, μ is the mean of the data, and σ is the standard deviation of the data.

[0083] For example, for wind farm power prediction, the following data sets are collected and their correlation coefficients with wind power output are calculated:

[0084] feature Dataset Correlation coefficient r <![CDATA[W D ]]> [0.5,0.6,0.4,0.7] 0.8 <![CDATA[W H ]]> [100,200,300,400] 0.6 <![CDATA[W C ]]> [0.1,0.2,0.3,0.4] 0.9 <![CDATA[W FS ]]> [5,6,7,8] 0.7 <![CDATA[W FX ]]> [180,190,200,210] 0.5 <![CDATA[W WD ]]> [20,22,24,26] 0.4 <![CDATA[W SD ]]> [50,55,60,65] 0.3 <![CDATA[W QY ]]> [1000,1010,1020,1030] 0.2

[0085] Use the interquartile range to identify outliers. The dataset is as follows:

[0086] feature Q1 Q3 IQR Outliers <![CDATA[W D ]]> 0.45 0.55 0.1 <0.25 or >0.85 <![CDATA[W H ]]> 150 250 100 <-50 or >350 <![CDATA[W C ]]> 0.15 0.25 0.1 <-0.05 or >0.35 <![CDATA[W FS ]]> 5.5 7.5 2.0 <1.5 or >11.5 <![CDATA[W FX ]]> 185 215 30 <155 or >245 <![CDATA[W WD ]]> 21 23 2 <17 or >29 <![CDATA[W SD ]]> 52 58 6 <46 or >64 <![CDATA[W QY ]]> 1005 1025 20 <985 or >1045

[0087] Each feature is standardized to obtain a cleaned and standardized feature data set for subsequent model training.

[0088] S3. Determine the weight of each characteristic variable according to the influence of each characteristic variable on wind power output, multiply each characteristic variable by its corresponding weight to obtain a weighted characteristic value, and calculate the weight values ​​of the geographical environment and climate respectively according to the weighted characteristic value; the weighted value expression of the geographical environment data in step S3 is:

[0089] W DL =W D +W H +W C ;

[0090] Where W DL Indicates the weight value of the geographical environment, WD represents the terrain weight, WH represents the altitude weight, and WC represents the surface roughness weight;

[0091] Among them, the weight value of the geographical environment is constant and is not affected by the four seasons;

[0092] The weighted value expression of the climate data is:

[0093] W QH =aW FS +aW FX +bW WD +bW SD +cW QY；

[0094] Where W QH The weight of climate data, W FS Represents the wind speed weight value, W FX Indicates the wind direction weight value, W WD Represents the temperature weight value, W SD Represents the humidity weight value, WQY Represents the air pressure weight value, where a, b, and c represent constants, and the maximum value is 1.

[0095] In addition, due to the influence of the four seasons, the temperature, humidity, wind speed, wind direction and air pressure will change periodically in different seasons. Therefore, in different seasons, the values ​​of constants a, b and c change according to the actual situation, specifically:

[0096] The constant a is 0.7 in spring, 1 in summer, 0.5 in autumn, and 0.3 in winter;

[0097] The constant b is 0.5 in spring, 0.3 in summer, 0.5 in autumn, and 1 in winter;

[0098] The constant c is 0.7 in spring, 0.3 in summer, 0.7 in autumn, and 1 in winter.

[0099] For example, when predicting the power of a wind farm in spring, the weighted value W of the climate data is calculated. QH , the parameters are as follows:

[0100] Wind speed weight value W FS is 0.2, and the wind direction weight value W FX is 0.3, the temperature weight value W WD is 0.1, and the humidity weight value W SD is 0.2, the air pressure weight value W QY is 0.2;

[0101] Substituting these values ​​into the weighted value expression W for climate data QH =aW FS +aW FX +bW WD +bW SD +cW QY In, we get:

[0102] W QH =0.7×0.2+0.7×0.3+0.5×0.1+0.5×0.2+0.7×0.2;

[0103] W QH =0.14+0.21+0.05+0.1+0.14;

[0104] W QH =0.64;

[0105] In the above example, the weighted value W of the climate data QH The final output is 0.64.

[0106] By dynamically adjusting the weights of climate factors (the values ​​of constants a, b, and c) according to seasonal changes, this method can more accurately reflect the impact of seasonal climate conditions on wind power output; this dynamic adjustment mechanism makes the prediction model more flexible and adaptable, and can better capture the impact of seasonal climate change on wind farm performance;

[0107] By weighting the feature variables, it is possible to ensure that the model pays more attention to those factors that have a greater impact on wind power output during training; this approach helps improve the model's prediction accuracy because it allows the model to adjust the contribution of each feature to the prediction results based on its importance;

[0108] By weighting the feature variables, the interference of unimportant features can be reduced, thereby enhancing the generalization ability of the model; this means that the model not only performs well on the training data, but also maintains good predictive performance on unseen test data;

[0109] In addition to climate factors, the model also considers the impact of geographic environmental factors (such as topography, altitude and surface roughness) on wind power output; this comprehensive consideration makes the prediction more comprehensive and accurate, as geographic environmental factors also play an important role in the performance of wind farms.

[0110] S4. Use the weighted eigenvalues ​​as input to establish a wind farm power prediction model, use historical data to train the power prediction model, and use a portion of the data for cross-validation to evaluate the accuracy and generalization ability of the model; Figure 2 As shown, the specific process of step S4 is:

[0111] S41, obtaining historical wind speed, wind direction, temperature, humidity and air pressure data, as well as corresponding wind power output data, and determining the main factors affecting wind power;

[0112] S42, weighting the features according to their importance to obtain weighted feature values, and forming a data set, dividing the data set into a training set and a test set, with the ratio of 70% training set and 30% test set allocated;

[0113] S43, select a model, use the training set data to train the model, and analyze the performance of the model on the training set and the test set to check whether there is overfitting or underfitting;

[0114] S44. Select the best performing model as the final model.

[0115] Through the above steps, a wind farm power prediction model with weighted features was established, and the accuracy and generalization ability of the model were evaluated through cross-validation, ensuring that our model can effectively learn from historical data and provide reliable results in future forecasts.

[0116] S5. Input the real-time or future geographic environment data and climate data into the trained power prediction model to obtain the power prediction result of the wind farm station, and display the predicted wind farm station power output result to the user in the form of a graph or table; Figure 3 As shown, step S5 is specifically as follows:

[0117] S51. Obtain geographical environmental data such as wind speed, wind direction, temperature, humidity and air pressure in the current or future period, and clean and format the data to ensure that it meets the requirements of model input;

[0118] S52, using the previously determined weighting coefficient, weighting each eigenvalue of the real-time data to obtain a weighted eigenvalue, and integrating all the weighted eigenvalues ​​into a eigenvector as the input of the model;

[0119] S53, loading the trained wind power prediction model into the system, and inputting the weighted feature vector into the model to obtain the power prediction result of the wind farm;

[0120] S54. Use a chart to show how the predicted wind power changes over time, and organize the predicted wind power and the corresponding time points into a table to facilitate users to view specific predicted values.

[0121] In summary, compared with the prior art, the present invention has the following effects:

[0122] By comprehensively considering geographical environment and climate factors, this method can more accurately predict the power output of wind farms. By using weighted eigenvalues ​​and weight calculations, the model can better capture the impact of different factors on wind power, thereby improving the accuracy of the prediction. At the same time, the impact of seasonal climate change on wind power is considered. By adjusting the values ​​of constants a, b, and c, the model can adapt to changes in temperature, humidity, wind speed, wind direction, and air pressure in different seasons. Seasonal adjustment improves the prediction performance of the model in different seasons.

[0123] By processing outliers and standardizing data, this method eliminates the impact of different dimensions and ranges, makes all features contribute equally to model training, improves the stability and reliability of the model, combines historical data with real-time data, uses common statistical methods and machine learning models for prediction, and is easy to implement and apply. At the same time, by displaying the prediction results in charts, users can intuitively understand the power prediction of wind farms in different time periods.

[0124] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting wind farm power based on geographical environment and climate, characterized in that: The steps include: S1. Collect historical power output data, geographical environment data and climate data of wind farms, wherein the geographical environment data includes topography, altitude and surface roughness; the climate data includes wind speed, wind direction, temperature, humidity and air pressure; S2. Based on historical data, select geographical environment factors and climate factors that have a greater impact on the power output of wind farms as characteristic variables, and clean and standardize the collected data to eliminate the impact of different dimensions; S3. Determine the weight of each characteristic variable according to the degree of influence of each characteristic variable on wind power output, multiply each characteristic variable by its corresponding weight to obtain a weighted characteristic value, and calculate the weight values ​​of the geographical environment and climate respectively according to the weighted characteristic values; S4. Use the weighted eigenvalues ​​as input to establish a wind farm power prediction model, use historical data to train the power prediction model, and use a portion of the data for cross-validation to evaluate the accuracy and generalization ability of the model; S5. Input the real-time or future geographic environment data and climate data into the trained power prediction model to obtain the power prediction result of the wind farm station, and display the predicted wind farm station power output result to the user in the form of a graph or table.

2. A method for predicting wind farm power based on geographical environment and climate according to claim 1, characterized in that: In step S1, the historical power output data of the wind farm is obtained from the operation record of the wind farm, the geographical environment data is obtained through a geographic information system or on-site measurement, and the meteorological data is obtained from a meteorological station or a numerical weather forecast model; The step S1 also includes preprocessing the collected data, and using a method combining double quartile and clustering to eliminate abnormal data during power outages and fan failures.

3. A method for predicting wind farm power based on geographical environment and climate according to claim 1, characterized in that: In step S2, the geographical environment factors and climate factors that have a greater impact on the power output of the wind farm are identified and selected as the characteristic variables of the model, specifically: Based on the professional knowledge in the field of wind power, determine which factors may have a significant impact on wind power, calculate the correlation coefficient between each factor and wind power output, and select the one with the highest correlation as the feature. The expression is: ； In the formula, r is the correlation coefficient, x and y are the data sets of two variables, and n is the number of samples; The standardization process is achieved by processing the outliers, specifically: Use the interquartile range to identify outliers, expressed as: ； ； In the formula, Q1 and Q3 are the first and third quartiles of the data set, respectively, IQR is the difference between the third quartile and the first quartile, and the definition of outliers is based on IQR. Any data point less than Q1−1.5×IQR or greater than Q3+1.5×IQR is considered an outlier.

4. A method for predicting wind farm power based on geographical environment and climate according to claim 3, characterized in that: The data normalization is used to eliminate the influence of different dimensions and ranges so that all features contribute equally to model training. The expression is: ； In the formula, z is the standardized value, x is the original data, μ is the mean of the data, and σ is the standard deviation of the data.

5. The method for predicting wind farm power based on geographical environment and climate according to claim 1, characterized in that: The weighted value expression of the geographical environment data in step S3 is: IN DL =In D +W H +W C ; Where W DL Indicates the weight value of the geographical environment, WD represents the terrain weight, WH represents the altitude weight, and WC represents the surface roughness weight; Among them, the weight value of the geographical environment is constant and is not affected by the four seasons; The weighted value expression of the climate data is: W QH =aW FS +aW FX +bW WD +bW SD +cW QY； Where W QH The weight of climate data, W FS Represents the wind speed weight value, W FX Indicates the wind direction weight value, W WD Represents the temperature weight value, W SD Represents the humidity weight value, W QY Represents the air pressure weight value, where a, b, and c represent constants, and the maximum value is 1.

6. The method for predicting wind farm power based on geographical environment and climate according to claim 5, characterized in that: Due to the influence of the four seasons, the temperature, humidity, wind speed, wind direction and air pressure will change periodically in different seasons. Therefore, in different seasons, the values ​​of constants a, b and c change according to the actual situation, specifically: The constant a is 0.7 in spring, 1 in summer, 0.5 in autumn, and 0.3 in winter; The constant b is 0.5 in spring, 0.3 in summer, 0.5 in autumn, and 1 in winter; The constant c is 0.7 in spring, 0.3 in summer, 0.7 in autumn, and 1 in winter.

7. A method for predicting wind farm power based on geographical environment and climate according to claim 1, characterized in that: The specific process of step S4 is as follows: S41, obtaining historical wind speed, wind direction, temperature, humidity and air pressure data, as well as corresponding wind power output data, and determining the main factors affecting wind power; S42, weighting the features according to their importance to obtain weighted feature values, and forming a data set, dividing the data set into a training set and a test set, with the ratio of 70% training set and 30% test set allocated; S43, select a model, use the training set data to train the model, and analyze the performance of the model on the training set and the test set to check whether there is overfitting or underfitting; S44. Select the best performing model as the final model.

8. The method for predicting wind farm power based on geographical environment and climate according to claim 1, characterized in that: Step S5 is specifically as follows: S51. Obtain geographical environmental data such as wind speed, wind direction, temperature, humidity and air pressure in the current or future period, and clean and format the data to ensure that it meets the requirements of model input; S52, using the previously determined weighting coefficient, weighting each eigenvalue of the real-time data to obtain a weighted eigenvalue, and integrating all the weighted eigenvalues ​​into a eigenvector as the input of the model; S53, loading the trained wind power prediction model into the system, and inputting the weighted feature vector into the model to obtain the power prediction result of the wind farm; S54. Use a chart to show how the predicted wind power changes over time, and organize the predicted wind power and the corresponding time points into a table to facilitate users to view specific predicted values.

9. A wind farm power prediction system based on geographical environment and climate according to claim 1, wherein the system is used to implement any of the methods of claims 1-8, characterized in that: include: Data collection and preprocessing module: This module is responsible for collecting historical power output data, geographical environment data and climate data of wind farms; at the same time, the collected data is cleaned and standardized to eliminate the impact of different dimensions; Feature selection and weighting module: Based on historical data, this module selects geographical environment factors and climate factors that have a greater impact on the power output of wind farms as feature variables, and cleans and standardizes the collected data; then, according to the degree of influence of each feature variable on wind power output, the weight of each feature variable is determined, and each feature variable is multiplied by its corresponding weight to obtain the weighted feature value; finally, the weight values ​​of the geographical environment and climate are calculated respectively according to the weighted feature values; Model training and prediction module: This module uses weighted eigenvalues ​​as input to establish a wind farm power prediction model; uses historical data to train the power prediction model, and uses a portion of the data for cross-validation to evaluate the accuracy and generalization ability of the model; then, inputs real-time or future geographic environment data and climate data into the trained power prediction model to obtain the power prediction results of the wind farm; Result display module: This module displays the predicted wind farm power output results to the user in the form of graphs or tables; Users can intuitively understand the power forecast of wind farms in different time periods; System management and maintenance module: This module is responsible for the management and maintenance of the entire system, including data backup, recovery, and update operations; at the same time, the system can also be optimized and upgraded according to actual needs.

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