文献类型：期刊文献

英文题名：Short-term Natural Gas Sales Forecasting under Cold Wave Weather Based on Error Correction Model

作者：Han, Kejiang[1]; Wang, Peng[2]; Jiang, Junyu[3]; Zhang, Tian[4]; Xie, Xiang[1]

第一作者：Han, Kejiang

机构：[1] China Petroleum Planning, Engineering Institute, Beijing, China; [2] PetroChina Natual Gas Sales Company, Kunlun Energy Company Limited, Beijing, China; [3] School of Financial Technology, Dongbei University of Finance and Economics, Liaoning, Dalian, China; [4] School of Management, Beijing Union University, Beijing, China

第一机构：China Petroleum Planning, Engineering Institute, Beijing, China

年份：2025

起止页码：601-607

外文期刊名：Proceedings of 2025 6th International Conference on Computer Information and Big Data Applications, CIBDA 2025

收录：EI(收录号：20254319382312)

语种：英文

外文关键词：Extreme weather - Gases - Natural gas - Prediction models - Sales - Weather forecasting

摘要：With the frequent occurrence of extreme weather events such as cold waves, natural gas consumption has experienced significant fluctuations, posing challenges to the forecasting model performance. To address this issue, this paper proposes a framework based on the Prophet-LSTM baseline prediction model and the Transformer-LSTNet error correction model, specifically designed to handle conditions of large number of outliers during cold waves. First, the Prophet model is used for trend decomposition, providing more effective features for the LSTM model, which generates the baseline prediction values. Then, the Transformer-LSTNet model is employed for error correction. By adding the prediction errors to the baseline forecast, the model adjusts the natural gas demand during cold wave periods, thus enhancing the stability and accuracy of natural gas load forecasting. Experimental results show that, after error correction, the MAPE for cold wave periods decreased from 7.07% to 5.54%. In conclusion, this method significantly improves the accuracy of natural gas load forecasting during cold wave conditions. ? 2025 Copyright held by the owner/author(s).