Short-term Power Load Forecasting Based on EMD-GWO-BP

Abstract

Abstract The short-range prediction of electricity demand holds immense importance in the strategic planning and advancement of the energy sector. However, the actual load sequence data reflects multiple complex properties, such as nonlinearity, non-stationarity, and temporal variation. In order to accurately forecast the load, this paper presents a three-level hybrid integrated short-term load prediction method composed of empirical mode decomposition (EMD), Grey Wolf Optimization (GWO) and BP neural network (BP). EMD is used to decompose load data to obtain good power consumption characteristics; GWO is used to obtain the optimal weight and threshold required for BP prediction. The hybrid integration method (EMD-GWO-BP) was evaluated using the 2017 annual data of Chaoyang County, Liaoning Province. The EMD-GWO-BP method was compared with the other two mainstream coupling methods (BP, GWO-BP). The statistical analysis Indicates that the suggested method within this document shows better forecast precision on three standard scales of MAPE, MAD and RMSE, which reflects the advanced nature of this method.