Medium-and Long-Term Power Load Forecasting in Shanxi Province Based on RA-LSTM Model
Zhou Shaoni
Wu You
Dou Yuhan
Zheng Yiyang
Abstract：Accurately forecasting medium-and long-term power load is crucial for the planning and operation of power systems.Because traditional methods have limitations in handling nonlinear characteristics and modeling temporal dependencies,and it is difficult to fully capture the complex features of load data,this paper constructs a RA-LSTM model based on residual networks and attention mechanisms.By introducing residual connections,the model alleviates the vanishing gradient problem and enhances its ability in capturing long-term temporal dependencies.Additionally,the integration of attention mechanisms improves the model's sensitivity to key time points and features.Using Shanxi Province as a case study,this paper builds a dataset that integrates temporal characteristics and meteorological factors,and comprehensively evaluates the RA-LSTM model.Experimental results demonstrate that the RA-LSTM model significantly outperforms the baseline BP model and the traditional LSTM model in key metrics,including root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2).The RA-LSTM model reduces MAPE by 41.8%and MAE by 40.9%compared to the BP model,significantly improving prediction accuracy and stability.Significance tests further validate the scientific reliability of the predictions of the RA-LSTM model.The achievement of this study could provide an efficient and robust solution to medium-and long-term load forecasting and lay a theoretical and practical foundation for future exploration of multi-feature integration and model optimization.
Keywords：medium-and long-term power loadforecastingRA-LSTM modelresidual networkattention mechanismdeep learning
Publication Date：2025-01-30
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：10( 78-87 )