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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\u20141 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 R2 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.<\/jats:p>","DOI":"10.3390\/info16070575","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T06:03:13Z","timestamp":1751868193000},"page":"575","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Limited Data Availability in Building Energy Consumption Prediction: A Low-Rank Transfer Learning with Attention-Enhanced Temporal Convolution Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Bo","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"},{"name":"Jiangsu Province Engineering Research Center of Construction Carbon Neutral Technology, Suzhou University of Science and Technology, Suzhou 215009, China"},{"name":"Jiangsu Province Key Laboratory of Intelligent Energy Efficiency, Suzhou University of Science and Technology, Suzhou 215009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9757-4323","authenticated-orcid":false,"given":"Qiming","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"},{"name":"Jiangsu Province Engineering Research Center of Construction Carbon Neutral Technology, Suzhou University of Science and Technology, Suzhou 215009, China"},{"name":"Jiangsu Province Key Laboratory of Intelligent Energy Efficiency, Suzhou University of Science and Technology, Suzhou 215009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0930-2764","authenticated-orcid":false,"given":"You","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"},{"name":"Jiangsu Province Key Laboratory of Intelligent Energy Efficiency, Suzhou University of Science and Technology, Suzhou 215009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Liu","sequence":"additional","affiliation":[{"name":"Jiangsu Province Engineering Research Center of Construction Carbon Neutral Technology, Suzhou University of Science and Technology, Suzhou 215009, China"},{"name":"School of Architecture and Urban Planning, Suzhou University of Science and Technology, Suzhou 215009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,4]]},"reference":[{"key":"ref_1","unstructured":"Sulkowska, M.S.N., Nugent, A., Nugent, A., Vega, L.A., and Carrazco, C. 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