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

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2023

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Architecture
(2009)
The Atılım University School of Architecture was founded in 2009. As for the number of students, our School is a medium-sized one, as is the case with many others in Europe. As a profession the expectation for which is to deal with people, society and environment in many aspects, architecture requires a similarly sophisticated education. In the Undergraduate Program at the Department of Architecture, we are working to establish such sophistication within the balance of theory and practice. Following the Integrated Doctorate Program that opened in 2010 for undergraduate and graduate alumni, the Thesis and Project Programs at Graduate Levels were opened in 2018. The self-evaluation studies of the Department that are run in coordination with the intra-evaluation and strategy studies of Atılım University are performed in relation to the external evaluations by the Architectural Accrediting Board (MİAK). The Department of Architecture is a member of the “European Association for Architectural Education” (EAAE).

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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.

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Erişen, Serdar/0000-0002-7192-0889

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indoor air, thermal comfort, user occupation, artificial intelligence, machine learning, natural ventilation, building performance, building information modeling

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13

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7

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