A novel dispatch adaptation load feature mapping network for multi-timestep load forecast

Abstract

Power system dispatch (PSD) greatly depends on load forecast (LF) with high accuracy. However, since load curve evolving along time axis is affected by long-term trendy and short-term stochastics electricity consumption modes, it is not easy to forecast load under accuracy requirement of PSD especially for high-proportion renewable energy power system. Therefore, a novel dispatch adaptation load feature mapping network with coordinated memory (LFMN-CN), which can forecast multi-timestep load values in future dispatch span at a time, is proposed. It adopts layered mapping network structure: 1) load features based on periodicity are mapped into two-dimension input matrix in the first layer; 2) nodes in the hidden layer with long-term and current memory, which denote long-term trendy and short-term stochastics electricity consumption modes respectively, are fully connected to construct recurrent LF network; 3) multi-timestep LF values in outer layer are obtained by LF output vector adaptive to timesteps of dispatch span. It has advantages of improved accuracy and dependence only on historical load series. LF results in power system of China show that the proposed model can perform multi-timestep LF more accurately than single-timestep LF.