Power System Load Forecasting and Energy Management Model Based on Data-Driven

Traditional load forecasting methods, such as time series analysis and regression analysis, have limitations in handling large-scale, high-dimensional, and nonlinear data. Therefore, this article proposes a data-driven model based on Long Short Term Memory (LSTM) networks, which can effectively handle long-term dependencies in time-series data and avoid gradient vanishing or explosion problems by introducing input gates, forget gates, and output gates. In this study, a new model construction method is proposed by combining the load characteristics of power system and energy management requirements, and the empirical analysis and performance evaluation are carried out. Parameters are optimized by combining grid search with K-fold cross-validation, and auxiliary information such as weather and date type is introduced to enhance the input characteristics by adopting the strategy of time series and depth feature fusion. Case study shows that LSTM model has high accuracy and stability in load forecasting, and its mean absolute error (MAE) and root mean square error (RMSE) are lower than those of traditional methods. The research in this paper provides a new load forecasting method for smart grid and energy management, which is helpful to optimize power generation plan, reduce operating cost and environmental pollution, and promote efficient utilization and sustainable development of energy.