A Review of Metaheuristic Optimization Techniques for Effective Energy Conservation in Buildings

Theogan Logan Pillay and Akshay Kumar Saha ()
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Theogan Logan Pillay: Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South Africa
Akshay Kumar Saha: Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South Africa

Energies, 2024, vol. 17, issue 7, 1-36

Abstract: The built environment is a significant contributor to global energy consumption and greenhouse gas emissions. Advancements in the adoption of environmentally friendly building technology have become crucial in promoting sustainable development. These advancements play a crucial role in conserving energy. The aim is to achieve an optimal design by balancing various interrelated factors. The emergence of innovative techniques to address energy conservation have been witnessed in the built environment. This review examines existing research articles that explore different metaheuristic optimization techniques (MOTs) for energy conservation in buildings. The focus is on evaluating the simplicity and stochastic nature of these optimization techniques. The findings of the review present theoretical and mathematical models for each algorithm and assess their effectiveness in problem solving. A systematic analysis of selected algorithms using MOT is conducted, considering factors that influence wellbeing, occupant health, and indoor environmental quality. The study examines the variations among swarm intelligence MOTs based on complexity, advantages, and disadvantages. The algorithms’ performances are based on the concept of uncertainty in consistently providing optimal solutions. The paper highlights the application of each technique in achieving energy conservation in buildings.

Keywords: algorithm; green building; swarm intelligence; energy conservation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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