Power Load Forecasting Using a Refined LSTM

Abstract

The power load forecasting is based on historical energy consumption data of a region to forecast the power consumption of the region for a period of time in the future. Accurate forecasting can provide effective and reliable guidance for power construction and grid operation. This paper proposed a power load forecasting approach using a two LSTM (long-short-term memory) layers neural network. Based on the real power load data provided by EUNITE, a power load forecasting method based on LSTM is constructed. Two models, single-point forecasting model and multiple-point forecasting model, are built to forecast the power of next hour and next half day. The experimental results show that the mean absolute percentage error of the single-point forecasting model is 1.806 and the mean absolute percentage error of the multiple-points forecasting model of LSTM network is 2.496.