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Multi-objective modified satin Bowerbird optimization algorithm used for simulation-based energy consumption optimization of yearly energy demand of lighting and cooling in a test case room

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  • Wang, Chuan'an
  • Pouramini, Somayeh

Abstract

An effective method of optimizing building energy performance through simulation is introduced in this study. The Modified Satin Bowerbird Optimization Algorithm and the DesignBuilder simulation tools are used to find optimal solutions for improving building energy efficiency. Five distinct climatic zones of China are examined in terms of decision variables including window size, overhang characteristics, and building orientation. The objective functions are based on the energy demand for lighting and cooling over a year. Analyses of optimization employing either a single-criterion or a multi-criteria approach delve into the interactions between these functions. According to the results, buildings had a reduction of 24.0 %–42.7 % in their overall yearly energy consumption. 22.9 %, 24.0 %, 35.1 %, 37.2 %, and 42.7 % yearly overall energy consumption reduction are specific to Harbin, Beijing, Guangzhou, Shanghai, and Kunming, respectively. Under varying weather conditions, architectural decision variables have a significant impact on energy consumption.

Suggested Citation

  • Wang, Chuan'an & Pouramini, Somayeh, 2024. "Multi-objective modified satin Bowerbird optimization algorithm used for simulation-based energy consumption optimization of yearly energy demand of lighting and cooling in a test case room," Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:energy:v:292:y:2024:i:c:s0360544224002792
    DOI: 10.1016/j.energy.2024.130508
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    References listed on IDEAS

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    1. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    2. Chen, Xiao & Cao, Benyi & Pouramini, Somayeh, 2023. "Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study," Energy, Elsevier, vol. 270(C).
    3. Muhumuza, Ronald & Zacharopoulos, Aggelos & Mondol, Jayanta Deb & Smyth, Mervyn & Pugsley, Adrian, 2018. "Energy consumption levels and technical approaches for supporting development of alternative energy technologies for rural sectors of developing countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 90-102.
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