Short-Term Load Forecasting based on ResNet and LSTM

Abstract

Recent development of artificial intelligence (AI) makes AI applicable to diverse fields, and the smart grid is not an exception. In particular, there have been extensive researches on load forecasting using deep learning. Most existing studies have been conducted on deep neural network (DNN) and recurrent neural network (RNN). Very recently, CNN with shallow network has been studied for short-term load forecasting (STLF). In this paper, we propose a novel framework based on ResNet/LSTM combined model. The proposed model has two steps. First, ResNet extracts latent features of daily and weekly load data. Then, LSTM is applied to train the encoded feature vector with dynamics, and make prediction suitable for volatile load data. By leveraging ResNet and LSTM, the proposed model has the advantage of forecasting load data that has both regularity and inconsistency. To demonstrate the performance, we compare the proposed model with other deep learning models: multi-layer perceptron (MLP), ResNet, LSTM and ResNet/MLP combined model. The results show that the proposed ResNet/LSTM combined model has 21.3% of MAPE improvement in overall, and 25.8% of MAPE improvement for the bottom 25% group in terms of MAPE compared to MLP.