Prediction method of wind farm power generation capacity based on feature clustering and correlation analysis

Abstract

• A wind power forecasting method based on deep sparse autoencoder is proposed. • An improved fuzzy C-means clustering algorithm is proposed. • Combining IFCM with PCC optimizes the input data for model training. • A DSAE model is constructed to effectively improve the prediction accuracy. Accurate wind power forecasting can help power systems achieve economical operation and dispatch management. This paper proposes a short-term wind power forecasting method based on feature clustering and correlation analysis, improving forecasting accuracy through data feature clustering, variable correlation analysis, and building forecasting models. More specifically, the improved fuzzy C-means (IFCM) algorithm is used to cluster the wind power dataset; Pearson correlation coefficient (PCC) is used to explore the correlation between meteorology and wind power variables; Based on the deep sparse auto-encoder (DSAE) model, the predicted value of short-term wind power is obtained. Combined with the actual wind power data, the effectiveness and superiority of the proposed method are verified by comparison.