Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements

• Published in: Energy and buildings Vol. 225; p. 110305
• Main Authors: Shan, Xin, Yang, En-Hua
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 15.10.2020; Elsevier BV
• Subjects: Classification; Classifiers; EEG; electroencephalogram (EEG); Electroencephalography; Energy; Energy conservation; Human sensing; Interpolation; Learning algorithms; Machine learning; Real time; Supervised learning; Thermal comfort; Thermal comfort; Supervised learning; Human sensing; electroencephalogram (EEG); Machine learning
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2020.110305

In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time change...