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Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users

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  • Thayane L. Bilésimo

    (Laboratory of Energy Efficiency in Buildings, Research Group on Management of Sustainable Environments, Department of Civil Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil)

  • Enedir Ghisi

    (Laboratory of Energy Efficiency in Buildings, Research Group on Management of Sustainable Environments, Department of Civil Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil)

Abstract

Reducing energy consumption is vital to save natural resources and contribute to the sustainable development in any sector of society. In the building sector, there are many well-known energy efficiency strategies currently being applied. However, considering the advances in technology and in comfort studies, it is possible to see that the current building sector scenario demands new energy efficiency strategies. Such strategies need to be capable of identifying and assuring comfortable environments according to users’ perceptions. Machine learning techniques can be a useful alternative to identify users’ preferences and control lighting and heating, ventilation and air-conditioning systems in buildings. This paper shows a systematic literature review on the use of machine learning algorithms on preference identification and environmental adequacy according to users’ demands. Its contribution is to explore beyond the performance and configurations of the algorithms, addressing users’ preference aspects as well. The strategies found in the literature provided promising results. The most used approach was supervised learning because data can be treated as categories. In general, the control systems have shown good performance, and so have the algorithms. Users were mostly satisfied with environmental conditions. Situations of dissatisfaction were associated with the occupant’s willingness to use the system more than with the control system’s performance. Furthermore, it is also possible to ally user-centred control and energy savings but this relies on occupants’ characteristics and the control strategies used. We underline the importance of identifying whether the users are willing to deal with an automatic control system before making any decision, even if the operation of the system is based on their preferred environmental conditions.

Suggested Citation

  • Thayane L. Bilésimo & Enedir Ghisi, 2024. "Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users," Sustainability, MDPI, vol. 16(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4258-:d:1397236
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    References listed on IDEAS

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    1. Lei, Yue & Zhan, Sicheng & Ono, Eikichi & Peng, Yuzhen & Zhang, Zhiang & Hasama, Takamasa & Chong, Adrian, 2022. "A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings," Applied Energy, Elsevier, vol. 324(C).
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