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A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-8025-4734
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2020 (English)In: Energy Systems, Springer Verlag, ISSN 1868-3967, E-ISSN 1868-3975, Vol. 13, p. 689-705Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 13, p. 689-705
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Energy Systems
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URN: urn:nbn:se:mau:diva-13827DOI: 10.1007/s12667-020-00376-xISI: 000509132000001Scopus ID: 2-s2.0-85078337875OAI: oai:DiVA.org:mau-13827DiVA, id: diva2:1416536
Available from: 2020-03-24 Created: 2020-03-24 Last updated: 2024-02-05Bibliographically approved

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Alawadi, SadiAlkhabbas, FahedOlsson, Carl MagnusDavidsson, Paul

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