Prediction of Thermal Energy Demand Using Fuzzy-Based Models Synthesized with Metaheuristic Algorithms

Hamzah Ali Alkhazaleh, Navid Nahi, Mohammad Hossein Hashemian, Zohreh Nazem, Wameed Deyah Shamsi and Moncef L. Nehdi ()
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Hamzah Ali Alkhazaleh: College of Engineering and IT, University of Dubai, Academic City, Dubai 14143, United Arab Emirates
Navid Nahi: Department of Architecture, Islamic Azad University, Tehran Science and Research Branch (East Azerbaijan), Tehran 14778-93855, Iran
Mohammad Hossein Hashemian: Department of Architecture, Tehran University, Kish Campus, Kish 13114-16846, Iran
Zohreh Nazem: Department of Architecture and Urban Design, Islamic Azad University Qazvin Branch, Qazvin 34185-1416, Iran
Wameed Deyah Shamsi: Information Technology Unit, Al-Mustaqbal University College, Babylon 51001, Iraq
Moncef L. Nehdi: Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada

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

Abstract: Increasing consumption of energy calls for proper approximation of demand towards a sustainable and cost-effective development. In this work, novel hybrid methodologies aim to predict the annual thermal energy demand (ATED) by analyzing the characteristics of the building, such as transmission coefficients of the elements, glazing, and air-change conditions. For this objective, an adaptive neuro-fuzzy-inference system (ANFIS) was optimized with equilibrium optimization (EO) and Harris hawks optimization (HHO) to provide a globally optimum training. Moreover, these algorithms were compared to two benchmark techniques, namely grey wolf optimizer (GWO) and slap swarm algorithm (SSA). The performance of the designed hybrids was evaluated using different accuracy indicators, and based on the results, ANFIS-EO and ANFIS-HHO (with respective RMSEs equal to 6.43 and 6.90 kWh·m −2 ·year −1 versus 9.01 kWh·m −2 ·year −1 for ANFIS-GWO and 11.80 kWh·m −2 ·year −1 for ANFIS-SSA) presented the most accurate analysis of the ATED. Hence, these models are recommended for practical usages, i.e., the early estimations of ATED, leading to a more efficient design of buildings.

Keywords: sustainability; building energy; thermal energy demand; ANFIS; Harris hawks optimization (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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