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In This work we propose two advanced ensemble models to improve the performance of energy consumption in smart homes, the first one is a voting ensemble model based on a ranking weight averaging that combines following basic machine learning techniques: decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGB). The second one is the stacking ensemble model in which the basic models (DT-RF-XGB) are combined through stacked generalization, then uses a secondary layer model or meta-learner (RF) to provide output prediction. The findings obtained show that the proposed ensemble model based on DT-RF-XGB using stacking technique surpasses all other basic algorithms with R2 around 0.9825.<\/jats:p>","DOI":"10.3233\/ais-230134","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T11:07:06Z","timestamp":1718363226000},"page":"1-14","source":"Crossref","is-referenced-by-count":2,"title":["Forecasting energy demand and efficiency in a smart home environment through advanced ensemble model: Stacking and voting"],"prefix":"10.1177","author":[{"given":"Nadia","family":"Drir","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, University of Science and Technology Houari Boumediene (USTHB), BP 32, El Alia, 16111 Bab-Ezzouar, Algiers, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Younes","family":"Kebour","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of Science and Technology Houari 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