A Systematic Approach to Optimizing Energy-Efficient Automated Systems with Learning Models for Thermal Comfort Control in Indoor Spaces

Erişen S.

in: Thermal Comfort in Built Environment: Challenges and Research Trends, N. Li; Y. He, Editor, MDPI, Basel, pp.128-156, 2024

  • Publication Type: Book Chapter / Chapter Research Book
  • Publication Date: 2024
  • Publisher: MDPI
  • City: Basel
  • Page Numbers: pp.128-156
  • Editors: N. Li; Y. He, Editor
  • Hacettepe University Affiliated: Yes


Energy-efficient automated systems for thermal comfort control in buildings is an emerging

research area that has the potential to be considered through a combination of smart solutions. This

research aims to explore and optimize energy-efficient automated systems with regard to thermal

comfort parameters, energy use, workloads, and their operation for thermal comfort control in indoor

spaces. In this research, a systematic approach is deployed, and building information modeling (BIM)

software and energy optimization algorithms are applied at first to thermal comfort parameters, such

as natural ventilation, to derive the contextual information and compute the building performance of

an indoor environment with Internet of Things (IoT) technologies installed. The open-source dataset

from the experiment environment is also applied in training and testing unique black box models,

which are examined through the users’ voting data acquired via the personal comfort systems (PCS),

thus revealing the significance of Fanger’s approach and the relationship between people and their

surroundings in developing the learning models. The contextual information obtained via BIM

simulations, the IoT-based data, and the building performance evaluations indicated the critical levels

of energy use and the capacities of the thermal comfort control systems. Machine learning models

were found to be significant in optimizing the operation of the automated systems, and deep learning

models were momentous in understanding and predicting user activities and thermal comfort levels

for well-being; this can optimize energy use in smart buildings.