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MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization

Author

Listed:
  • Rafael Batres

    (Tecnologico de Monterrey, School of Engineering and Sciences, Prolongación Ezequiel Montes, Santiago de Querétaro 76140, Querétaro, Mexico)

  • Yasaman Dadras

    (Department of Civil Engineering, University of Ottawa, 161 Louis-Pasteur Private, Ottawa, ON K1N 6N5, Canada)

  • Farzad Mostafazadeh

    (Department of Civil Engineering, University of Ottawa, 161 Louis-Pasteur Private, Ottawa, ON K1N 6N5, Canada)

  • Miroslava Kavgic

    (Department of Civil Engineering, University of Ottawa, 161 Louis-Pasteur Private, Ottawa, ON K1N 6N5, Canada)

Abstract

A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope measures. However, conventional optimization analyses are time-consuming and computationally expensive, especially for complex buildings and many optimization parameters. Therefore, this paper proposed a novel optimization algorithm, MEVO (metamodel-based evolutionary optimizer), developed to efficiently identify optimal retrofit solutions for building envelopes while minimizing the need for extensive simulations. The key innovation of MEVO lies in its integration of evolutionary techniques with design-of-computer experiments, machine learning, and metaheuristic optimization. This approach continuously refined a machine learning model through metaheuristic optimization, crossover, and mutation operations. Comparative assessments were conducted against four alternative metaheuristic algorithms and Bayesian optimization, demonstrating MEVO’s effectiveness in reliably finding the best solution within a reduced computation time. A hypothesis test revealed that the proposed algorithm is significantly better than Bayesian optimization in finding the best cost values. Regarding computation time, the proposed algorithm is 4–7 times faster than the particle swarm optimization algorithm and has a similar computational speed as Bayesian Optimization.

Suggested Citation

  • Rafael Batres & Yasaman Dadras & Farzad Mostafazadeh & Miroslava Kavgic, 2023. "MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization," Energies, MDPI, vol. 16(20), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7026-:d:1256926
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    References listed on IDEAS

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    1. Chegari, Badr & Tabaa, Mohamed & Simeu, Emmanuel & Moutaouakkil, Fouad & Medromi, Hicham, 2022. "An optimal surrogate-model-based approach to support comfortable and nearly zero energy buildings design," Energy, Elsevier, vol. 248(C).
    2. Razmi, Afshin & Rahbar, Morteza & Bemanian, Mohammadreza, 2022. "PCA-ANN integrated NSGA-III framework for dormitory building design optimization: Energy efficiency, daylight, and thermal comfort," Applied Energy, Elsevier, vol. 305(C).
    3. Ciardiello, Adriana & Rosso, Federica & Dell'Olmo, Jacopo & Ciancio, Virgilio & Ferrero, Marco & Salata, Ferdinando, 2020. "Multi-objective approach to the optimization of shape and envelope in building energy design," Applied Energy, Elsevier, vol. 280(C).
    4. Xiaolin Yang & Zhuoxi Chen & Yukai Zou & Fengdeng Wan, 2023. "Improving the Energy Performance and Economic Benefits of Aged Residential Buildings by Retrofitting in Hot–Humid Regions of China," Energies, MDPI, vol. 16(13), pages 1-21, June.
    5. Benjamin Kubwimana & Hamidreza Najafi, 2023. "A Novel Approach for Optimizing Building Energy Models Using Machine Learning Algorithms," Energies, MDPI, vol. 16(3), pages 1-16, January.
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