Research on Wind Power Prediction Based on LSTM Neural Network

Wind power generation is currently the most technologically mature and large-scale power generation method among renewable energy sources in the world. High-precision wind power prediction during the wind power grid connection process has become a critical means for power dispatching and smoothing grid connection fluctuations. Aiming at the problems of uncertainty and poor controllability of wind turbine output, this paper proposes a short-term wind power prediction model based on the Long Short-Term Memory (LSTM) neural network. By comprehensively considering the influence of meteorological factors (e.g., wind speed, direction, temperature, humidity) on prediction results, self-feedback neurons are used to extract time-series features for short-term prediction. The model is verified by real data from a wind power plant. The results show that the proposed method has high prediction accuracy and good applicability for short-term wind power prediction.