{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T18:07:07Z","timestamp":1784311627062,"version":"3.55.0"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"ANID through FONDECYT Regular","award":["1240293"],"award-info":[{"award-number":["1240293"]}]},{"name":"Basal National Center for Artificial Intelligence through CENIA","award":["FB210017"],"award-info":[{"award-number":["FB210017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3570618","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T17:33:41Z","timestamp":1747330421000},"page":"86746-86767","source":"Crossref","is-referenced-by-count":16,"title":["Deep Learning for Time-Series Forecasting With Exogenous Variables in Energy Consumption: A Performance and Interpretability Analysis"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9596-4482","authenticated-orcid":false,"given":"Ava","family":"Mohammadi","sequence":"first","affiliation":[{"name":"Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-3701","authenticated-orcid":false,"given":"Gonzalo","family":"N\u00e1poles","sequence":"additional","affiliation":[{"name":"Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yamisleydi","family":"Salgueiro","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering, Universidad de Talca, Campus Curicó, Curico, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"The Paris Agreement","year":"2015"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2023.113164"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2017.2753802"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/en13184875"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"ref6","article-title":"TimeXer: Empowering transformers for time series forecasting with exogenous variables","author":"Wang","year":"2024","journal-title":"arXiv:2402.19072"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.adapen.2023.100123"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2021.03.012"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/en16031480"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3575813.3597345"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/1878431.1878435"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.03.034"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.02.114"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2016.07.090"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/bigcomp.2017.7881726"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.12.042"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07348-5"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.03.012"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.06.012"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118874"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3060654"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2021.12.067"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2023.100888"},{"key":"ref26","article-title":"A decoder-only foundation model for time-series forecasting","author":"Das","year":"2023","journal-title":"arXiv:2310.10688"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3719207"},{"key":"ref28","article-title":"Time-LLM: Time series forecasting by reprogramming large language models","author":"Jin","year":"2023","journal-title":"arXiv:2310.01728"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.5220\/0010213204890500"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.121607"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02662-2"},{"key":"ref32","article-title":"Explainable artificial intelligence (XAI) on TimeSeries data: A survey","author":"Rojat","year":"2021","journal-title":"arXiv:2104.00950"},{"key":"ref33","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017","journal-title":"arXiv:1705.07874"},{"key":"ref34","volume-title":"Publisher: U.K. Power Networks\u2014London Datastore","year":"2021"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.25080\/majora-92bf1922-011"},{"key":"ref36","volume-title":"GitHub\u2014Sktime\/Pytorch-Forecasting: Time Series Forecasting With PyTorch","year":"2024"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"ref38","article-title":"Fast and accurate deep network learning by exponential linear units (ELUs)","author":"Clevert","year":"2015","journal-title":"arXiv:1511.07289"},{"key":"ref39","article-title":"Language modeling with gated convolutional networks","author":"Dauphin","year":"2016","journal-title":"arXiv:1612.08083"},{"key":"ref40","article-title":"Layer normalization","author":"Lei Ba","year":"2016","journal-title":"arXiv:1607.06450"},{"key":"ref41","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"arXiv:1706.03762"},{"key":"ref42","article-title":"A multi-horizon quantile recurrent forecaster","author":"Wen","year":"2017","journal-title":"arXiv:1711.11053"},{"key":"ref43","first-page":"1","article-title":"Reversible instance normalization for accurate time-series forecasting against distribution shift","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kim"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"ref45","article-title":"Ranger21: A synergistic deep learning optimizer","author":"Wright","year":"2021","journal-title":"arXiv:2106.13731"},{"key":"ref46","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.segan.2023.101171"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-3245-6_9"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/pesgm.2016.7741076"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/sefet57834.2023.10245994"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/elit53502.2021.9501104"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/isgteurope62998.2024.10863218"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11005580.pdf?arnumber=11005580","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T04:42:44Z","timestamp":1748061764000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11005580\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3570618","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}