{"id":9792,"date":"2026-08-09T14:36:58","date_gmt":"2026-08-09T06:36:58","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9792"},"modified":"2026-08-09T15:58:52","modified_gmt":"2026-08-09T07:58:52","slug":"jase-202611-34-025","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-025","title":{"rendered":"AFW-Attention-LSTM for Short-Term Heating Energy Prediction"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Yu Sun<\/i><\/a>, Yadan Liu, Xiaoxia Tao, and Hui Su<\/p>\n\n\n\n

Shendong Coal Group Co., Ltd, CHINA ENERGY INVESTMENT, Ordos City Inner Mongolia Autonomous Region, 017209, China<\/p>\n<\/div>\n\n\n\n

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Received: June 07, 2026
Accepted: June 30, 2026
Publication Date: August 09, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Prediction Curve on an Extreme Cold Day<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.025<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Short-term heating energy consumption prediction is essential for intelligent control and efficient energy allocation in centralized heating systems. To address the limited nonlinear modeling ability of traditional time-series methods, the insufficient long-term dependency learning of conventional machine learning models, and the weak adaptability of existing LSTM-based models to multi-source heterogeneous features, this study proposes an improved AFW-Attention-LSTM model integrating adaptive feature weighting and local attention mechanisms. The model follows a four-stage framework of feature optimization, temporal extraction, key segment enhancement, and prediction output. The adaptive feature weighting module dynamically adjusts the importance of environmental, operational, and building-related features to reduce redundant information interference, while the local attention mechanism emphasizes critical temporal segments such as extreme operating conditions and period transitions. Experiments using 121 days of real operational data from a residential community in a severe cold region show that AFW-Attention-LSTM outperforms standard LSTM, GRU, and Attention-LSTM under both normal and extreme operating conditions. In 1\u201324 h forecasting tasks, it achieves lower MAE and RMSE, with a maximum prediction accuracy of 96.7% for 1-hour forecasting under normal conditions. The model also demonstrates good training efficiency and prediction stability, providing an effective technical solution for accurate short-term heating energy consumption forecasting and supporting intelligent scheduling and optimized operation of centralized heating systems. However, since the data were collected from a single residential community, further validation across different regions and heating systems is required.<\/p>\n\n\n\n

Keywords: Adaptive feature weighting; Local attention mechanism; Multi-source heterogeneous data; Centralized heating system; Energy allocation<\/em><\/p>\n\n\n\n

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