{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T10:57:26Z","timestamp":1775645846701,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:00:00Z","timestamp":1637539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms \u201cconsumption,\u201d \u201cresidential,\u201d and \u201celectricity\u201d are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas \u201ccluster\u201d is the more commonly used term in the IC domain. The paper also shows that there are strong relations between \u201cResidential Energy Consumption\u201d and \u201cElectricity Consumption,\u201d \u201cHeating\u201d and \u201cClimate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.<\/jats:p>","DOI":"10.3390\/en14227810","type":"journal-article","created":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T02:55:17Z","timestamp":1637636117000},"page":"7810","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis"],"prefix":"10.3390","volume":"14","author":[{"given":"Ahmed","family":"Abdelaziz","sequence":"first","affiliation":[{"name":"Nova Information Management School, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal"},{"name":"Information System Department, Higher Technological Institute, HTI, Cairo 44629, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4223-7079","authenticated-orcid":false,"given":"Vitor","family":"Santos","sequence":"additional","affiliation":[{"name":"Nova Information Management School, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1445-2695","authenticated-orcid":false,"given":"Miguel Sales","family":"Dias","sequence":"additional","affiliation":[{"name":"Department of Information Science and Technology, Instituto Universit\u00e1rio de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.enbuild.2012.09.005","article-title":"Energy intelligent buildings based on user activity: A survey","volume":"56","author":"Nguyen","year":"2013","journal-title":"Energy Build."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/TSG.2010.2089069","article-title":"Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid","volume":"1","author":"Wong","year":"2010","journal-title":"IEEE Trans. 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