Abstract

This study aims to use the Corrective Power of personal comfort systems and prioritise ceiling fan operation over air-conditioning to reduce energy consumption and implement controls based on Operative temperature (OT). We use a machine learning model to predict indoor OT of a space. The predicted OT is used to determine thermal comfort according to the India Model for Adaptive Comfort. A control sequence that automates ceiling fan-speed and airconditioning set-points is developers and tested in two different rooms; one, a passive building with an insulated envelope, and another, a typical uninsulated building. The base case is a constant 24°C setpoint, with no ceiling fans operating. The testing shows that the control sequence that prioritises ceiling fan operation has higher comfort votes than the base case, and the control sequence provided more than 80% cooling energy savings compared to the base case.