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Multi-Objective Optimization of Building Energy Saving Based on the Randomness of Energy-Related Occupant Behavior

Author

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  • Zhouchen Zhang

    (Department of Civil Engineering, Ningbo University, Ningbo 315211, China)

  • Jian Yao

    (Department of Architecture, Ningbo University, Ningbo 315211, China)

  • Rongyue Zheng

    (Department of Civil Engineering, Ningbo University, Ningbo 315211, China)

Abstract

Given the escalating global energy demand driven by building energy consumption, this study is dedicated to meticulously investigating efficient energy-saving strategies in buildings, with a keen focus on the impact of occupant behavior’s randomness on energy efficiency and multi-objective optimization. The methodology encompassed a thorough analysis of various energy consumption factors, including building envelope and architectural form. We employed Latin Hypercube Sampling for in-depth sampling studies across each factor’s reasonable range. Utilizing Sobol sensitivity analysis, we pinpointed variables of high sensitivity and embarked on multi-objective optimization targeting two primary indicators: energy consumption and thermal comfort. Leveraging the NSGA-II algorithm, we adeptly identified optimal solutions, culminating in the proposition of building energy-saving strategies anchored on the Pareto frontier. Through stochastic modeling simulations of occupant behavior in window opening and air conditioning usage, a comparison was made with models that do not consider occupant behavior. It was found that incorporating occupant behavior into energy-saving designs can reduce energy consumption by up to 20.20%, while ensuring thermal comfort. This approach can achieve improved energy efficiency and indoor comfort.

Suggested Citation

  • Zhouchen Zhang & Jian Yao & Rongyue Zheng, 2024. "Multi-Objective Optimization of Building Energy Saving Based on the Randomness of Energy-Related Occupant Behavior," Sustainability, MDPI, vol. 16(5), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1935-:d:1346754
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    References listed on IDEAS

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    1. Méndez Echenagucia, Tomás & Capozzoli, Alfonso & Cascone, Ylenia & Sassone, Mario, 2015. "The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis," Applied Energy, Elsevier, vol. 154(C), pages 577-591.
    2. Yao, Jian, 2014. "Determining the energy performance of manually controlled solar shades: A stochastic model based co-simulation analysis," Applied Energy, Elsevier, vol. 127(C), pages 64-80.
    3. Yao, Jian, 2012. "Energy optimization of building design for different housing units in apartment buildings," Applied Energy, Elsevier, vol. 94(C), pages 330-337.
    4. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
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