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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Vydané v:	Energy systems (Berlin. Periodical) Ročník 13; číslo 3; s. 689 - 705
Hlavní autori:	Alawadi, Sadi, Mera, David, Fernández-Delgado, Manuel, Alkhabbas, Fahed, Olsson, Carl Magnus, Davidsson, Paul
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
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