Machine learning method for real-time non-invasive prediction of individual thermal preference in transient conditions

• Published in: Building and environment Vol. 148; pp. 372 - 383
• Main Authors: Cosma, Andrei Claudiu, Simha, Rahul
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.01.2019; Elsevier BV
• Subjects: Accuracy; Air conditioners; Air conditioning; Algorithms; Artificial intelligence; Body parts; Building automation; Data processing; Discomfort; Feedback technology; HVAC control; HVAC equipment; Learning algorithms; Machine learning; Mathematical models; Overheating; Predictions; Preferences; Real time; Skin; Skin temperature; Temperature effects; Temperature gradients; Temperature preferences; Thermal analysis; Thermal comfort; Thermal comfort prediction; Thermographic camera; Ventilation; Feedback technology; Thermographic camera; HVAC control; Building automation; Thermal comfort prediction
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2018.11.017

This work introduces a new technique that provides real-time feedback to a Heating, Ventilation, and Air Conditioning (HVAC) system controller with respect to the occupants' thermal preferences to avoid space overheating. We propose a non-invasive approach for automatic prediction of personal t...