Estimation of the Mean Radiant Temperature in Office Buildings Using an Artificial Neural Network Developed in a Phyton Environment

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2025

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Taylor & Francis Ltd

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Energy Systems Engineering
(2009)
The Department of Energy Systems Engineering admitted its first students and started education in the academic year of 2009-2010 under Atılım University School of Engineering. In this Department, all kinds of energy are presented in modules (conventional energy, renewable energy, hydrogen energy, bio-energy, nuclear energy, energy planning and management) from their detection, production and procession; to their transfer and distribution. A need is to arise for a surge of energy systems engineers to ensure energy supply security and solve environmental issues as the most important problems of the fifty years to come. In addition, Energy Systems Engineering is becoming among the most important professions required in our country and worldwide, especially within the framework of the European Union harmonization process, and within the free market economy.
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Mechanical Engineering
(2009)
The Atılım University Department of Mechanical Engineering started education in 2009, and offers graduate and doctorate degree programs, in addition to its undergraduate program. Our main goal is to graduate Mechanical Engineers who have the skills to design, analyze and synthesize; who are able to convert advanced technology and innovations into products; and who have the culture of research and cooperation. While our graduates reach this goal, they adopt the principle of life-long learning, and develop a sense of entrepreneurship, paying importance to professional ethics. With a curriculum prepared in line with the criteria of MÜDEK, we help our students develop themselves professionally, and socially. Graduates of mechanical engineering may be employed in many sectors and in a wide array of positions. Able to work under any field that involves production and energy conversion, graduates of the department may also gain expertise in fields such as aviation, automotive, or material engineering.

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Thermal comfort describes an occupant's state of mind in a thermal environment, influenced by six parameters: air velocity, relative humidity, air temperature, mean radiant temperature (MRT), clothing value, and metabolic rate. MRT is the most problematic parameter since the obtaining process is difficult and time-consuming. MRT can be acquired by several methods such as calculations, measurements, assumptions, and software programmes. However, the methods have complexities and uncertainties. Comprehensive models are needed to obtain MRT. To this aim, this study presents an alternative method using one of the artificial intelligence methods, Artificial Neural Network (ANN), to predict MRT for indoor environments to abstain from the difficulties and complexities. A case building is selected in a university office building in Ankara, T & uuml;rkiye. The proposed model is developed and coded in a Python programming environment to predict the MRT using ANN. The results indicate that the ANN model, using only four inputs, predicts MRT with an R-2 value of 0.94 compared to the globe thermometer measurement method. The model's advantages over methods include simplicity, time efficiency and learning from the limited datasets such as difficulty in calculating terms like MRT.

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Thermal Comfort, Artificial Neural Network, Mean Radiant Temperature, Indoor Environment, Office Buildings

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