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
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Abstract	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 greenhouse gas emissions and improve energy efficiency by reducing the amount of primary energy consumed, and it has pointed to the development of efficient building energy management systems as key. In traditional buildings, households are responsible for continuously monitoring and controlling the installed Heating, Ventilation, and Air Conditioning (HVAC) system. Unnecessary energy consumption might occur due to, for example, forgetting devices turned on, which overwhelms users due to the need to tune the devices manually. Nowadays, smart buildings are automating this process by automatically tuning HVAC systems according to user preferences in order to improve user satisfaction and optimize energy consumption. Towards achieving this goal, in this paper, we compare 36 Machine Learning algorithms that could be used to forecast indoor temperature in a smart building. More specifically, we run experiments using real data to compare their accuracy in terms of R-coefficient and Root Mean Squared Error and their performance in terms of Friedman rank. The results reveal that the ExtraTrees regressor has obtained the highest average accuracy (0.97%) and performance (0,058%) over all horizons.
AbstractList	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 greenhouse gas emissions and improve energy efficiency by reducing the amount of primary energy consumed, and it has pointed to the development of efficient building energy management systems as key. In traditional buildings, households are responsible for continuously monitoring and controlling the installed Heating, Ventilation, and Air Conditioning (HVAC) system. Unnecessary energy consumption might occur due to, for example, forgetting devices turned on, which overwhelms users due to the need to tune the devices manually. Nowadays, smart buildings are automating this process by automatically tuning HVAC systems according to user preferences in order to improve user satisfaction and optimize energy consumption. Towards achieving this goal, in this paper, we compare 36 Machine Learning algorithms that could be used to forecast indoor temperature in a smart building. More specifically, we run experiments using real data to compare their accuracy in terms of R-coefficient and Root Mean Squared Error and their performance in terms of Friedman rank. The results reveal that the ExtraTrees regressor has obtained the highest average accuracy (0.97%) and performance (0,058%) over all horizons.
Author	Olsson, Carl Magnus Mera, David Alkhabbas, Fahed Davidsson, Paul Alawadi, Sadi Fernández-Delgado, Manuel
Author_xml	– sequence: 1 givenname: Sadi orcidid: 0000-0002-5380-4358 surname: Alawadi fullname: Alawadi, Sadi email: sadi.alawadi@mau.se organization: Internet of Things and People Research Center Department of Computer Science and Media Technology, Malmö University – sequence: 2 givenname: David surname: Mera fullname: Mera, David organization: Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS), Universidade de Santiago de Compostela – sequence: 3 givenname: Manuel surname: Fernández-Delgado fullname: Fernández-Delgado, Manuel organization: Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS), Universidade de Santiago de Compostela – sequence: 4 givenname: Fahed surname: Alkhabbas fullname: Alkhabbas, Fahed organization: Internet of Things and People Research Center Department of Computer Science and Media Technology, Malmö University – sequence: 5 givenname: Carl Magnus surname: Olsson fullname: Olsson, Carl Magnus organization: Internet of Things and People Research Center Department of Computer Science and Media Technology, Malmö University – sequence: 6 givenname: Paul surname: Davidsson fullname: Davidsson, Paul organization: Internet of Things and People Research Center Department of Computer Science and Media Technology, Malmö University
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SubjectTerms	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
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Title	A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings
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