Short-term forecasting electricity load by long short-term memory and reinforcement learning for optimization of hyper-parameters

Abstract

Electricity load forecasting is an essential operation of the power system. Deep learning is used to improve accurate electricity load forecasting. In this study, combining Long short-term memory and reinforcement learning are proposed to encourage the advantage of a single approach for forecasting. Importance input features, including the mutual feature of electricity load, are used to increase accuracy. First, multi-time series input can handle by Long short-term memory and the addition of features supports to the load feature will make the model better efficient. Because the LSTM model is quite complex, choosing a good set of hyperparameters is difficult. Therefore, the purpose of using reinforcement learning is to optimize hyper-parameters of the Long short-term memory model. The proposed model is the combination of Long-short term memory and reinforcement learning. The proposed model will be applied in two electricity load data sets, the real-life data of Vietnam Electricity and the other public data set. In one day ahead forecasting, the proposed model archives superior performance than the benchmark.