Temporal Convolutional Network Based Short-term Load Forecasting Model

Abstract

Load forecasting has always been the focus of energy management system research. Recently, with the development of machine learning and artificial intelligence technology, more and more models are applied to load forecasting. In this paper, we design a model based on the temporal convolutional network for short-term load forecasting, which can accurately capture the feature form historical load data. Combine the actual load data collected from a certain region of Shanghai, we compare our model with three traditional models, including ARIMA model, ANN model, and LSTM model. The experiment results show that the model proposed in this paper achieves the best performance and has superior accuracy in short-term load forecasting.