High-Precision Power Load Forecasting Using Real-time Temperature Information and Deep Learning Method

Abstract

Load forecasting is an important part of power system planning, which directly affects the safety and reliability of power grid operation. Real-time and high-precision load forecasting results are the key to improve the efficiency of the entire power grid. In order to solve the problem of low prediction accuracy of existing algorithms, based on the deep analysis of the strong correlation between temperature and electricity consumption, Long Short Memory Network (LSTM) is constructed, which implements the deep mining of the characteristics of historical electricity consumption data and the deep self-learning of the correlation between electricity consumption and temperature, and realizes the power load forecasting. Compared with traditional load forecasting techniques, the prediction results were significantly improved. In addition, the experiments utilizing the Google Tensor-flow platform were carried out to further study the impact of the combination of different activation functions on the prediction performance of the LSTM algorithm. The verification results shown that the prediction accuracy was significantly improved by using the ELU activation function than other activation functions. The developed algorithm could more effectively solve the low precision problem that is common in current prediction algorithms.