Akademik Veri Yönetim Sistemi
Thermal Comfort in Built Environment: Challenges and Research Trends, N. Li; Y. He, Editör, MDPI, Basel, ss.128-156, 2024
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.