A compound framework for short-term gas load forecasting combining time-enhanced perception transformer and two-stage feature extraction

Abstract

The development of accurate and reliable gas load forecasting models is critical to residential safety and gas company economics. However, since the seq2seq neural network conveys information by sequentially updating the hidden states, when processing gas load data, the information needs to be passed several times before it can affect the prediction results at subsequent time steps. This can lead to a lack of ability of the model to capture long-term dependencies. To address the above issues, we propose a novel compound framework. Time-enhanced perception transformer (TEPT) in the framework achieves parallel computation of historical data and stronger location awareness through convolutional self-attention and improved attention scores. This results in better capture of local patterns and long-term dependencies in gas loads. In addition, the framework proposes two-stage feature extraction (TSFE) for decomposing the important parts of the original information and extracting more refined features based on them. We constructed a real gas load data set from Wuxi, China to verify the effectiveness. The results show that MAE, MAPE, and RMSE of the proposed method are 3.45, 1.07%, and 4.68, which provide higher accuracy compared to other prediction methods.