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In this work, we further explore the capabilities of patch-based Transformers in the context of forecasting a single time series, specifically focusing on energy consumption prediction. Our primary interest lies in long-term forecasting, a relatively under-explored area in the literature. To this end, we evaluate Transformer-based models on two energy consumption datasets \u2014 one public and one private \u2014 and assess their performance. We argue that leveraging patches or patching-like techniques can significantly enhance model efficiency. Lastly, we discuss the current limitations of Transformer-based architectures and propose potential solutions.<\/jats:p>","DOI":"10.1142\/s0218213025400135","type":"journal-article","created":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T02:43:31Z","timestamp":1766198611000},"source":"Crossref","is-referenced-by-count":1,"title":["Patch-Based Transformers for Long-Term Energy Consumption Forecasting"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1427-5486","authenticated-orcid":false,"given":"Dimitris","family":"Karpontinis","sequence":"first","affiliation":[{"name":"Department of Digital Industry Technologies, National & Kapodistrian University of Athens, Psachna, Evia, 34400, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3611-8292","authenticated-orcid":false,"given":"Georgios","family":"Alexandridis","sequence":"additional","affiliation":[{"name":"Department of Digital Industry Technologies, National & Kapodistrian University of Athens, Psachna, Evia, 34400, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"S0218213025400135BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-63227-3_23"},{"key":"S0218213025400135BIB002","unstructured":"Y. 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Long, Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting, arXiv:2106.13008."},{"key":"S0218213025400135BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.122069"},{"key":"S0218213025400135BIB005","doi-asserted-by":"publisher","DOI":"10.3390\/math10132181"},{"key":"S0218213025400135BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.07.007"},{"key":"S0218213025400135BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.01.076"},{"key":"S0218213025400135BIB008","first-page":"76656","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Yi K.","year":"2023"},{"key":"S0218213025400135BIB009","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2024.101758"},{"key":"S0218213025400135BIB010","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122333"},{"key":"S0218213025400135BIB011","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1795"},{"key":"S0218213025400135BIB012","unstructured":"H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong and W. Zhang, Informer: Beyond efficient transformer for long sequence time-series forecasting, arXiv:2012.07436."},{"key":"S0218213025400135BIB013","unstructured":"S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y.X. Wang and X. Yan, Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting, arXiv:1907.00235."},{"key":"S0218213025400135BIB014","unstructured":"T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun and R. Jin, FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting, arXiv:2201.12740."},{"key":"S0218213025400135BIB015","unstructured":"I. Kaufman and O. Azencot, Analyzing deep transformer models for time series forecasting via manifold learning, arXiv:2410.13792."},{"key":"S0218213025400135BIB016","unstructured":"A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit and N. Houlsby, An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale, arXiv:2010.11929 [cs]."},{"key":"S0218213025400135BIB017","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.79"},{"key":"S0218213025400135BIB018","unstructured":"T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest and A. M. Rush, Huggingface\u2019s transformers: State-of-the-art natural language processing, arXiv:1910.03771."},{"key":"S0218213025400135BIB019","unstructured":"I. Loshchilov and F. Hutter, Decoupled weight decay regularization, arXiv:1711.05101."},{"key":"S0218213025400135BIB020","unstructured":"L. Darlow, Q. Deng, A. Hassan, M. Asenov, R. Singh, A. Joosen, A. Barker and A. 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