Volume 33, Issue 3 (2023)
IJAUP 2023, 33(3) |
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1- School of Architectural and Environmental Design, University of Science and Technology, Tehran, Iran
2- School of Architecture, University College London(UCL)
3- Engineering Faculty, Islamic Azad University, Hashtgerd Branch, Hashtgerd, Iran
4- School of Architecture and Environmental Design, Building, and Environment Research Laboratory, Iran University of Science and Technology, Tehran, Iran ,sanayeayan@iust.ac.ir
2- School of Architecture, University College London(UCL)
3- Engineering Faculty, Islamic Azad University, Hashtgerd Branch, Hashtgerd, Iran
4- School of Architecture and Environmental Design, Building, and Environment Research Laboratory, Iran University of Science and Technology, Tehran, Iran ,
Abstract:
In recent years, increasing attention has been given to improving the energy efficiency of buildings in order to reduce their environmental impact and operational costs. As a result, multi-objective optimization methods have become an important tool for optimizing building energy performance. This research reviews building performance analysis approaches in a comparative method and results in a systematic overview of the existing multi-objective optimization methods used in the field of building energy performance. This review covers a wide range of optimization techniques, including genetic algorithms (NSGA-II), evolutionary algorithms, particle swarm intelligence algorithms, and other metaheuristic approaches. Furthermore, the review provides a comprehensive analysis of the strengths and weaknesses of each method in different fields such as daylight, ventilation, and thermal performance analysis. In order to achieve the aims of the research alongside reviewing the Scopus scientific database, various relevant studies were investigated. Eventually, this study provides. Eventually, this review identifies gaps in the literature potential in research directions and proposes multiple ways for future research.
Keywords: Multi-objective Optimization, Building envelopes, Thermal Performance, NSGA-II, Genetic Algorithm.
Type of Study: Review Paper |
Subject:
Architectural Science Review
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