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

Rafael Batres, Yasaman Dadras, Farzad Mostafazadeh and Miroslava Kavgic ()
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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

Energies, 2023, vol. 16, issue 20, 1-24

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.

Keywords: building envelope retrofit; building performance optimization; active learning; surrogate-based optimization; model-based optimization; metamodel; energy efficiency (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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