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Building performance optimization for university dormitory through integration of digital gene map into multi-objective genetic algorithm

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  • Chi, Fang'ai
  • Xu, Ying

Abstract

College students spent most of their time in dormitory buildings, which account for a large proportion of the overall electricity use in university. Building performance is determined by its genes (i.e., building component characters), which can be optimized in the design stage. However, a building is comprised of many components, of which some are contradictory to each other in the building performance optimization process. In doing so, a digital gene map was proposed for the university dormitory, characterized by the binary code strings. Based on the digital gene map, the building elements can be parameterized to create some dynamic variables, to facilitate the multi-objective genetic algorithm. Via the multi-objective genetic algorithm and the data statistics tool of Design Explorer in this work, the “Pareto front” solutions can be obtained to optimize the decision-makings in the dormitory building design. For evaluations of the building performance improvement potentials for various types of study rooms with optimized solutions, we conducted the comparison studies in this work. Through comparison studies, we found that the optimized solutions from the multi-objective genetic algorithm, for the nine types of study rooms, have better compromised building performances. The methodology proposed in this work can be applicable for different types of university dormitories under various climate conditions, due to the dormitory buildings have a similar gene pool and chromosomal structure.

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  • Chi, Fang'ai & Xu, Ying, 2022. "Building performance optimization for university dormitory through integration of digital gene map into multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:appene:v:307:y:2022:i:c:s0306261921014781
    DOI: 10.1016/j.apenergy.2021.118211
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    1. Machairas, Vasileios & Tsangrassoulis, Aris & Axarli, Kleo, 2014. "Algorithms for optimization of building design: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 101-112.
    2. Chantrelle, Fanny Pernodet & Lahmidi, Hicham & Keilholz, Werner & Mankibi, Mohamed El & Michel, Pierre, 2011. "Development of a multicriteria tool for optimizing the renovation of buildings," Applied Energy, Elsevier, vol. 88(4), pages 1386-1394, April.
    3. Schwartz, Yair & Raslan, Rokia & Mumovic, Dejan, 2016. "Implementing multi objective genetic algorithm for life cycle carbon footprint and life cycle cost minimisation: A building refurbishment case study," Energy, Elsevier, vol. 97(C), pages 58-68.
    4. Stevanović, Sanja, 2013. "Optimization of passive solar design strategies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 177-196.
    5. Ma, Minda & Ma, Xin & Cai, Wei & Cai, Weiguang, 2020. "Low carbon roadmap of residential building sector in China: Historical mitigation and prospective peak," Applied Energy, Elsevier, vol. 273(C).
    6. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2019. "A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin," Applied Energy, Elsevier, vol. 241(C), pages 331-361.
    7. Dias Pereira, Luísa & Raimondo, Daniela & Corgnati, Stefano Paolo & Gameiro da Silva, Manuel, 2014. "Energy consumption in schools – A review paper," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 911-922.
    8. Xuetao Wang & Qianchuan Zhao & Yifan Wang, 2020. "A Distributed Optimization Method for Energy Saving of Parallel-Connected Pumps in HVAC Systems," Energies, MDPI, vol. 13(15), pages 1-24, July.
    9. Li, Yan & Wei, Yigang & Zhang, Xiaoling & Tao, Yuan, 2020. "Regional and provincial CO2 emission reduction task decomposition of China's 2030 carbon emission peak based on the efficiency, equity and synthesizing principles," Structural Change and Economic Dynamics, Elsevier, vol. 53(C), pages 237-256.
    10. Freitas, Jader de Sousa & Cronemberger, Joára & Soares, Raí Mariano & Amorim, Cláudia Naves David, 2020. "Modeling and assessing BIPV envelopes using parametric Rhinoceros plugins Grasshopper and Ladybug," Renewable Energy, Elsevier, vol. 160(C), pages 1468-1479.
    11. Koo, Choongwan & Hong, Taehoon & Lee, Minhyun & Kim, Jimin, 2016. "An integrated multi-objective optimization model for determining the optimal solution in implementing the rooftop photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 822-837.
    12. Li, Hangxin & Wang, Shengwei, 2019. "Coordinated optimal design of zero/low energy buildings and their energy systems based on multi-stage design optimization," Energy, Elsevier, vol. 189(C).
    13. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
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    2. Silvia Ruggiero & Marco Iannantuono & Anastasia Fotopoulou & Dimitra Papadaki & Margarita Niki Assimakopoulos & Rosa Francesca De Masi & Giuseppe Peter Vanoli & Annarita Ferrante, 2022. "Multi-Objective Optimization for Cooling and Interior Natural Lighting in Buildings for Sustainable Renovation," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    3. Fangyuan Xie & Yi Wu & Xinqi Wang & Xiling Zhou, 2024. "Optimization Strategies for the Envelope of Student Dormitories in Hot Summer and Cold Winter Regions: Multi-Criteria Assessment Method," Sustainability, MDPI, vol. 16(14), pages 1-27, July.
    4. Hou, Dan & Huang, Jiayu & Wang, Yanyu, 2023. "A comparison of approaches with different constraint handling techniques for energy-efficient building form optimization," Energy, Elsevier, vol. 277(C).

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