Limited Data Availability in Building Energy Consumption Prediction: A Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution Network
Building energy consumption prediction (BECP) is the essential foundation for attaining energy efficiency in buildings, contributing significantly to tackling global energy challenges and facilitating energy sustainability. However, while data-driven methods have emerged as a crucial method to solvi...
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MDPI AG
2025-07-01
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| author | Bo Wang Qiming Fu You Lu Ke Liu |
| author_facet | Bo Wang Qiming Fu You Lu Ke Liu |
| author_sort | Bo Wang |
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| description | Building energy consumption prediction (BECP) is the essential foundation for attaining energy efficiency in buildings, contributing significantly to tackling global energy challenges and facilitating energy sustainability. However, while data-driven methods have emerged as a crucial method to solving this complex problem, the limited availability of data presents a significant challenge to model training. To address this challenge, this paper presents an innovative method, named Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution Network (LRTL-AtTCN). LRTL-AtTCN integrates the attention mechanism with temporal convolutional network (TCN), improving the ability of extracting global and local dependencies. Moreover, LRTL-AtTCN combines low-rank decomposition, reducing the number of parameters during the transfer learning process with similar buildings, which can achieve better transfer performance in the limited data case. Experimentally, we conduct a comprehensive evaluation across three forecasting horizons—1 week, 2 weeks, and 1 month. Compared to the horizon-matched baseline, LRTL-AtTCN cuts the MAE by 91.2%, 30.2%, and 26.4%, respectively, and lifts the 1-month R<sup>2</sup> from 0.8188 to 0.9286. On every horizon it also outperforms state-of-the-art transfer-learning methods, confirming its strong generalization and transfer capability in BECP. |
| format | Article |
| id | doaj-art-8dabb5256c3d44ad8ea8dfd4ff3fc0a8 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-8dabb5256c3d44ad8ea8dfd4ff3fc0a82025-08-20T03:36:18ZengMDPI AGInformation2078-24892025-07-0116757510.3390/info16070575Limited Data Availability in Building Energy Consumption Prediction: A Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution NetworkBo Wang0Qiming Fu1You Lu2Ke Liu3School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaJiangsu Province Engineering Research Center of Construction Carbon Neutral Technology, Suzhou University of Science and Technology, Suzhou 215009, ChinaBuilding energy consumption prediction (BECP) is the essential foundation for attaining energy efficiency in buildings, contributing significantly to tackling global energy challenges and facilitating energy sustainability. However, while data-driven methods have emerged as a crucial method to solving this complex problem, the limited availability of data presents a significant challenge to model training. To address this challenge, this paper presents an innovative method, named Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution Network (LRTL-AtTCN). LRTL-AtTCN integrates the attention mechanism with temporal convolutional network (TCN), improving the ability of extracting global and local dependencies. Moreover, LRTL-AtTCN combines low-rank decomposition, reducing the number of parameters during the transfer learning process with similar buildings, which can achieve better transfer performance in the limited data case. Experimentally, we conduct a comprehensive evaluation across three forecasting horizons—1 week, 2 weeks, and 1 month. Compared to the horizon-matched baseline, LRTL-AtTCN cuts the MAE by 91.2%, 30.2%, and 26.4%, respectively, and lifts the 1-month R<sup>2</sup> from 0.8188 to 0.9286. On every horizon it also outperforms state-of-the-art transfer-learning methods, confirming its strong generalization and transfer capability in BECP.https://www.mdpi.com/2078-2489/16/7/575building energy consumption predictiontransfer learninglow-rank decompositionTCN |
| spellingShingle | Bo Wang Qiming Fu You Lu Ke Liu Limited Data Availability in Building Energy Consumption Prediction: A Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution Network Information building energy consumption prediction transfer learning low-rank decomposition TCN |
| title | Limited Data Availability in Building Energy Consumption Prediction: A Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution Network |
| title_full | Limited Data Availability in Building Energy Consumption Prediction: A Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution Network |
| title_fullStr | Limited Data Availability in Building Energy Consumption Prediction: A Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution Network |
| title_full_unstemmed | Limited Data Availability in Building Energy Consumption Prediction: A Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution Network |
| title_short | Limited Data Availability in Building Energy Consumption Prediction: A Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution Network |
| title_sort | limited data availability in building energy consumption prediction a low rank transfer learning with attention enhanced temporal convolution network |
| topic | building energy consumption prediction transfer learning low-rank decomposition TCN |
| url | https://www.mdpi.com/2078-2489/16/7/575 |
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