Research on Wind Power Peak Prediction Method

With the global demand and concern for clean energy, wind energy, as an important part of clean energy, has gradually taken a central position in the power system. This study addresses the instability and volatility of wind energy and aims to provide a stable and reliable supply of wind power to the power system through technical means. Firstly, this paper provides in-depth processing and analysis of wind power datasets, including the processing of outliers and missing values, and correlation analysis with meteorological data and wind turbine status data. Secondly, in order to achieve accurate prediction of the total power generated from wind farms, a clustering-then-prediction model is proposed, in which all wind turbines in a wind farm are classified by the K-shape clustering algorithm, and then wind power prediction is carried out by using three models, Transformer, LSTM, and GRU, with the Transformer model showing the highest prediction accuracy. Finally, this paper proposes a peak and valley detection algorithm for the demand of peak shaving and valley filling in the power grid system, which effectively predicts the peak and valley phases of wind farm power generation, and provides a strong support for the peak and frequency adjustment and stable operation of the power grid. This research provides an effective way for the sustainable development of the wind power industry and the promotion of large-scale grid integration of wind power.