Multi-objective design optimization of building energy retrofit using building energy simulation surrogate model

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ID: 286928
2024
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Abstract
The urgent need to decarbonize existing buildings has led to an increased focus on energy retrofits as a crucial strategy for improving building performance and sustainability. While building energy simulation software offers valuable insights into the effects of various retrofit scenarios, determining the optimal energy retrofit solution remains a significant challenge due to the prohibitive computational costs associated with simulation-based optimization. This study presents an alternative approach to multi-objective design optimization of building energy retrofits involving a building energy simulation surrogate model. A case study of a religious building in Metro Manila, Philippines was used to demonstrate the proposed methodology. The methodology comprises four key steps: 1) a comprehensive building energy simulation database was created using Latin Hypercube Sampling, 2) regression models for energy consumption and thermal comfort were trained using this database, 3) these regression models were coupled with a Genetic Algorithm to perform multi-objective optimization, and 4) ranking of solutions in the Pareto front was demonstrated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Optimization results were validated with the resulting accuracy and reliability of the surrogate model-based approach being within acceptable limits. The findings of this study suggest that regression surrogate models offer a computationally efficient and effective means of optimizing building energy retrofits. By providing a practical framework for multi-objective optimization. This research contributes to the advancement of sustainable building practices and supports the broader goal of decarbonizing the built environment.
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Authors Dimaculangan, Wilbert Matthew C.
Journal Malay Journal
Year 2024
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_mecheng/28
Keywords Keywords not found

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