A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings

The international community has largely recognized that the Earth’s climate is changing. Mitigating its global effects requires international actions. The European Union (EU) is leading several initiatives focused on reducing the problems. Specifically, the Climate Action tries to both decrease EU g...

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Bibliographic Details
Published in:	Energy systems (Berlin. Periodical) Vol. 13; no. 3; pp. 689 - 705
Main Authors:	Alawadi, Sadi, Mera, David, Fernández-Delgado, Manuel, Alkhabbas, Fahed, Olsson, Carl Magnus, Davidsson, Paul
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022 Springer Nature B.V
Subjects:	Air conditioning Algorithms Buildings Climate action Economics and Management Energy Energy consumption Energy efficiency Energy management systems Energy Policy Energy Systems Greenhouse gases Households HVAC HVAC equipment Machine learning Operations Research/Decision Theory Optimization Original Paper Smart buildings User satisfaction Weather forecasting Smart buildings Time series prediction Energy efficiency Internet of Things Machine Learning
ISSN:	1868-3967, 1868-3975, 1868-3975
Online Access:	Get full text
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