{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:20:33Z","timestamp":1780086033434,"version":"3.54.0"},"reference-count":31,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T00:00:00Z","timestamp":1700352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LGG22F030008"],"award-info":[{"award-number":["LGG22F030008"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["2023C01129"],"award-info":[{"award-number":["2023C01129"]}]},{"name":"Key Research and Development Projects of \u201cVanguard\u201d and \u201cLeading Goose\u201d in Zhejiang Province","award":["LGG22F030008"],"award-info":[{"award-number":["LGG22F030008"]}]},{"name":"Key Research and Development Projects of \u201cVanguard\u201d and \u201cLeading Goose\u201d in Zhejiang Province","award":["2023C01129"],"award-info":[{"award-number":["2023C01129"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Predicting energy consumption in large exposition centers presents a significant challenge, primarily due to the limited datasets and fluctuating electricity usage patterns. This study introduces a cutting-edge algorithm, the contrastive transformer network (CTN), to address these issues. By leveraging self-supervised learning, the CTN employs contrastive learning techniques across both temporal and contextual dimensions. Its transformer-based architecture, tailored for efficient feature extraction, allows the CTN to excel in predicting energy consumption in expansive structures, especially when data samples are scarce. Rigorous experiments on a proprietary dataset underscore the potency of the CTN in this domain.<\/jats:p>","DOI":"10.3390\/s23229270","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T01:54:12Z","timestamp":1700445252000},"page":"9270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Small Sample Building Energy Consumption Prediction Using Contrastive Transformer Networks"],"prefix":"10.3390","volume":"23","author":[{"given":"Wenxian","family":"Ji","sequence":"first","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Spatial Planning and Design, Hangzhou City University, 51 Huzhou Street, Hangzhou 310015, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaorun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, K., Kang, H., Wang, W., Jiang, P., and Li, N. 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