Improved LSTM renewable energy power generation forecasting method integrating cycle weight and memory decay compensation

Wind power, photovoltaic and other renewable energy power generation have strong nonlinear, periodic and time-varying characteristics. Traditional prediction models often face the problem of insufficient prediction accuracy and poor model stability when dealing with long-term trends, seasonal changes and complex meteorological factors. Therefore, this paper proposes an improved long short-term memory network prediction method. By deeply integrating periodic information at the gating mechanism level and introducing an adaptive memory compensation strategy, it can more accurately capture the dynamic changes of renewable energy power generation and effectively improve the prediction performance. This method provides strong technical support for the optimal scheduling of smart grids, the efficient consumption of renewable energy and the planning and decision-making of power systems, and has good application prospects.