New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems

Nadia Jahanafroozi, Saman Shokrpour, Fatemeh Nejati, Omrane Benjeddou, Mohammad Worya Khordehbinan, Afshin Marani and Moncef L. Nehdi ()
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Nadia Jahanafroozi: Department of Architecture College of Design, North Carolina State University, Raleigh, NC 27695, USA
Saman Shokrpour: Faculty of Architecture & Urbanism, Tabriz Islamic Art University (TIAU), Tabriz 5164736931, Iran
Fatemeh Nejati: Department of Art and Architecture, Faculty of Architecture, Khatam University, Tehran 1991813741, Iran
Omrane Benjeddou: Civil Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj 16273, Saudi Arabia
Mohammad Worya Khordehbinan: Cultur & Art Applied Scientific Teaching Center Kurdistan Branch, University of Applied Science and Technology, Sanandaj 6618758671, Iran
Afshin Marani: Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada
Moncef L. Nehdi: Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada

Sustainability, 2022, vol. 14, issue 21, 1-14

Abstract: Energy-efficient buildings have attracted vast attention as a key component of sustainable development. Thermal load analysis is a pivotal step for the proper design of heating, ventilation, and air conditioning (HVAC) systems for increasing thermal comfort in energy-efficient buildings. In this work, novel a methodology is proposed to predict the cooling load ( L C ) of residential buildings based on their geometrical characteristics. Multi-layer perceptron (MLP) neural network was coupled with metaheuristic algorithms to attain its optimum hyperparameter values. According to the results, the L C pattern can be promisingly captured and predicted by all developed hybrid models. Nevertheless, the comparison analysis revealed that the electrostatic discharge algorithm (ESDA) achieved the most powerful MLP model. Hence, utilizing the proposed methodology would give new insights into the thermal load analysis method and bridge the existing gap between the most recently developed computational intelligence techniques and energy performance analysis in the sustainable design of energy-efficient residential buildings.

Keywords: sustainability; energy performance; thermal load; neural networks; optimization algorithms (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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