A Control Sequence for Prioritising Ceiling Fan Operation Over Air Conditioners Using Machine Learning to Determine Thermal Comfort

Siva Barathi

Indian Institute for Human Settlements, Bangalore, India

Thounaojam Amanda

Indian Institute for Human Settlements, Bangalore, India
Corresponding Author: amandat@iihs.ac.in

Prasad Vaidya

Indian Institute for Human Settlements, Bangalore, India

A Gopikrishna

Indian Institute for Human Settlements, Bangalore, India

Dalavai Uthej

Indian Institute for Human Settlements, Bangalore, India

Tandon Vipin

Manipal School of Architecture and Planning, Manipal, India

Cite this article

Barathi, S., Amanda, T., Vaidya, P., Gopikrishna, A., Uthej, D., Vipin, T. (2024). A Control Sequence for Prioritising Ceiling Fan Operation Over Air Conditioners Using Machine Learning to Determine Thermal Comfort. In Proceedings of Energise 2023- Lifestyle, Energy Efficiency, and Climate Action, pp 93–99, Alliance for an Energy Efficient Economy. https://doi.org/10.62576/BOWF7492


  • Machine learning for predicting OT as a scalable approach for adaptive comfort controls.
  • Using the corrective power index for cooling to adjust the upper limit of the thermal comfort band based on air speed achieved by ceiling fans.
  • Thermal comfort study of comfort votes and energy consumption, compared to a 24ºC constant setpoint.
  • Cooling energy savings of more than 97% with higher comfort votes for the demonstrated control sequence.


This study aims to use the corrective power of personal comfort systems of -1K to -6K [2] and prioritise ceiling fan operation over AC to reduce energy consumption and implement controls based on Operative temperature (OT). We use a machine learning algorithm that takes indoor air temperature and outdoor values for air temperature, wind speed, and relative humidity as inputs and predicts the indoor OT of a space. The predicted OT is used to determine thermal comfort according to the India Model for Adaptive Comfort (IMAC). We have developed a control sequence that automates ceiling fan speed operation and air-conditioning (AC) set-points. The control sequence is tested in two different rooms; one, a passively designed building with an insulated envelope, and another, a typical uninsulated building, tested against a base case of 24°C set-point suggested by the Bureau of Energy Efficiency (BEE), India, with no ceiling fans operating. The testing shows that the control sequence that prioritises ceiling fan operation has higher comfort votes than the BEE base case, and the control sequence provided more than 80% cooling energy savings compared to the BEE base case.


Adaptive Thermal Comfort, Operative Temperature, Corrective Power Index, Machine Learning, Ceiling Fans  


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