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

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.

Automated summary

Energy-efficient automated systems for thermal comfort control in buildings are explored and optimized through a systematic approach using building information modeling and energy optimization algorithms. The significance of Fanger's approach and the relationship between people and their surroundings in developing learning models is revealed through the training and testing of black box models and users' voting data. Contextual information obtained through BIM simulations, IoT data, and building performance evaluations indicates critical levels of energy use and the capacities of thermal comfort control systems. Machine learning and deep learning models play significant roles in optimizing the operation of automated systems and predicting user activities and thermal comfort levels for well-being, thereby optimizing energy use in smart buildings.