Power Load Forecasting Based on the Combined Model of LSTM and XGBoost

Abstract

Accurate power load forecasting can provide effective and reliable guidance for power construction and grid operation, and plays a very important role in the power grid system. In order to improve the accuracy of power load forecasting, this paper proposes a combined forecast model based on LSTM and XGBoost. The LSTM forecast model and the XGBoost forecast model are firstly established and the power load is predicted by using the two models respectively. Then the combined model predicts the power load by using the error reciprocal method to combine the results from the two single models. Through the experimental verification of the power load data of The Electrician Mathematical Contest in Modeling, the forecast error of the combined model we got is 0.57%, which is significantly lower than the single forecast model.