A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network

Abstract

Natural gas is often said to be the most environmentally friendly fossil fuel. Its usage has increased significantly in recent years. Meanwhile, accurate forecasting of natural gas spot prices has become critical to energy management, economic growth, and environmental protection. This work offers a novel model based on the temporal convolutional network (TCN) and dynamic learning rate for predicting natural gas spot prices over the following two weekdays. The residual block structure of TCN provides good prediction accuracy, and the dilated causal convolutions minimize the amount of computation. The dynamic learning rate setting was adopted to enhance the model’s prediction accuracy and robustness. Compared with three existing models, i.e., the one-dimensional convolutional neural network (1D-CNN), gate recurrent unit (GRU), and long short-term memory (LSTM), the proposed model can achieve better performance over other models with mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE) scores of 4.965%, 0.216, and 0.687, respectively. These attractive advantages make the proposed model a promising candidate for long-term stability in natural gas spot price forecasting.

Keywords

• forecasting of natural gas spot prices
• TCN
• dilated causal convolutions
• residual block
• dynamic learning rate