{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T20:20:43Z","timestamp":1760473243957,"version":"3.44.0"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"FCT \u2013 Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia"},{"name":"R&D Units Project Scope","award":["UIDB\/00319\/2020"],"award-info":[{"award-number":["UIDB\/00319\/2020"]}]},{"DOI":"10.13039\/501100023978","name":"CCDR-Norte","doi-asserted-by":"crossref","award":["NORTE-01-0145-FEDER-000086"],"award-info":[{"award-number":["NORTE-01-0145-FEDER-000086"]}],"id":[{"id":"10.13039\/501100023978","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The significance of energy efficiency in the development of smart cities cannot be overstated. It is essential to have a clear understanding of the current energy consumption (EC) patterns in both public and private buildings. One way to achieve this is by employing machine learning classification algorithms, which offer a broader perspective on the factors influencing EC. These algorithms can be applied to real data from databases, making them valuable tools for smart city applications. In this paper, our focus is specifically on the EC of public schools in a Portuguese city, as this plays a crucial role in designing a Smart City. By utilizing a comprehensive dataset on school EC, we thoroughly evaluate multiple ML algorithms. The objective is to identify the most effective algorithm for classifying average EC patterns. The outcomes of this study hold significant value for school administrators and facility managers. By leveraging the predictions generated from the selected algorithm, they can optimize energy usage and, consequently, reduce costs. The use of a comprehensive dataset ensures the reliability and accuracy of our evaluations of various ML algorithms for EC classification.<\/jats:p>","DOI":"10.1093\/jigpal\/jzae058","type":"journal-article","created":{"date-parts":[[2024,5,19]],"date-time":"2024-05-19T08:37:27Z","timestamp":1716107847000},"source":"Crossref","is-referenced-by-count":4,"title":["Behaviour of Machine Learning algorithms in the classification of energy consumption in school buildings"],"prefix":"10.1093","volume":"33","author":[{"given":"Jos\u00c9","family":"Machado","sequence":"first","affiliation":[{"name":"ALGORITMI Centre\/LASI, University of Minho , Braga ,","place":["Portugal"]}]},{"given":"Ant\u00d3nio","family":"Chaves","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre\/LASI, University of Minho , Braga ,","place":["Portugal"]}]},{"given":"Larissa","family":"Montenegro","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre\/LASI, University of Minho , Braga ,","place":["Portugal"]}]},{"given":"Carlos","family":"Alves","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre\/LASI, University of Minho , Braga ,","place":["Portugal"]}]},{"given":"Dalila","family":"Dur\u00c3es","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre\/LASI, University of Minho , Braga ,","place":["Portugal"]}]},{"given":"Ricardo","family":"Machado","sequence":"additional","affiliation":[{"name":"C\u00e2mara Municipal de Guimar\u00e3es , Guimar\u00e3es ,","place":["Portugal"]}]},{"given":"Paulo","family":"Novais","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre\/LASI, University of Minho , Braga ,","place":["Portugal"]}]}],"member":"286","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"key":"2025092510364256800_ref1","doi-asserted-by":"crossref","DOI":"10.3390\/en14227810","article-title":"Machine learning techniques in the energy consumption of buildings: a systematic literature review using text mining and 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