Short‐term power load forecasting based on multi‐layer bidirectional recurrent neural network

Abstract

Accurate power load forecasting is of great significance to ensure the safety, stability, and economic operation of the power system. In particular, short-term power load forecasting is the basis for grid planning and decision making. In recent years, machine learning algorithms have been widely used for short-term power load forecasting. Specifically, long short-term memory (LSTM) and gated recurrent unit (GRU) are tailored to time series data. In this study, a multi-layer bidirectional recurrent neural network model based on LSTM and GRU is proposed to forecast short-term power load and is validated on two data sets. The experimental result shows that the proposed method is superior to the competition winner in the precision of forecasting on the European Intelligent Technology Network competition data. On power company data in Chongqing, considering the differences of the seasonal load, the hourly peak load of different types of load data is used for experiments. The authors separately forecast the seasonal load and compare it with LSTM, support vector regression and back propagation models. The results of the comparison show the priority of the proposed method in terms of forecasting accuracy as compared to the adopted models.