A Systematic Approach To Optimizing Energy-Efficient Automated Systems With Learning Models for Thermal Comfort Control in Indoor Spaces
Loading...
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Mdpi
Open Access Color
OpenAIRE Downloads
OpenAIRE Views
Abstract
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.
Description
Erişen, Serdar/0000-0002-7192-0889
ORCID
Keywords
indoor air, thermal comfort, user occupation, artificial intelligence, machine learning, natural ventilation, building performance, building information modeling
Turkish CoHE Thesis Center URL
Fields of Science
Citation
WoS Q
Scopus Q
Source
Volume
13
Issue
7