Cited by 70Building Energy and Comfort OptimizationAdvanced Multi-Objective Optimization AlgorithmsBuilding energy efficiency and sustainability

AI-driven design optimization for sustainable buildings: a systematic review

Open access
Manmatharasan, Piragash, Bitsuamlak, Girma, Grolinger, Katarina·2025·Energy and Buildings

Abstract

Buildings are major contributors to global carbon emissions, accounting for a substantial portion of energy consumption and environmental impact. This situation presents a critical opportunity for energy conservation through strategic interventions in both building design and operational phases . Artificial Intelligence (AI) has emerged as a transformative approach in this context, enhancing the efficiency and precision of energy management efforts. In the operational phase, AI is extensively utilized as smart controllers for Heating, Ventilation, and Air Conditioning (HVAC) systems and passive energy gains, as well as for fault detection. In the design phase, AI is pivotal as a surrogate model , enabling rapid and accurate evaluation of design options and allowing designers to optimize building performance with minimal computational resources. As the early-stage optimization is more cost-effective than post-construction modifications, design phase optimization has a great potential. Consequently, this paper examines recent advancements in surrogate-assisted design optimization for sustainable buildings, providing a comprehensive overview of the entire optimization process, from data preparation and surrogate model training to final optimization. The review categorizes studies based on experimental approaches and methodologies, identifying trends, gaps, and opportunities in the field. Notably, it highlights how modern AI techniques can incorporate previously unexplored dimensions into surrogate-assisted optimization, broadening the scope and potential of surrogate models. Therefore, this study provides guidance for future research and practical applications of AI-driven strategies in sustainable building practices.

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Crossref · 10.1016/j.enbuild.2025.115440

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  • Generative design algorithms can suggest new architectural shapes that meet specific performance needs such as maximization of daylight or reduction of steel.

    “Generative design algorithms, utilizing deep learning or genetic algorithms, can suggest new architectural shapes that meet specific performance needs such as maximization of daylight or reduction of steel (Abioye et al., 2021; Manmatharasan et al., 2025).”

    From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Manmatharasan et al., 2025

  • In sustainable building design, AI can examine building form and materials combination to reduce energy usage or carbon use.

    “For instance, in sustainable buildingdesign, AI can examine building form and materials combination to reduce energy usage or carbon usea field comprehensively reviewed by Manmatharasan et al. (2025).”

    From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Manmatharasan et al., 2025

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Authors
Manmatharasan, P., Bitsuamlak, G., & Grolinger, K.
Affiliations
Western University (CA)
Corresponding author
Grolinger, Katarina
Venue
Energy and Buildings
Publisher
Elsevier BV
Published
2025-04
Volume
332
Pages
115440
ISSN
0378-7788 (ISSN-L)
OpenAlex
W4407352559
Cited by
63
References
123
Citations by year
2026: 36 · 2025: 27
FWCI
7.05
Type
journal article
Language
en
Open access
Locations
Energy and Buildings (publishedVersion, cc-by-nc)
Funders
Environment Canada (EDF-CA-2021i018)
Topics
  • Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
  • Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
Keywords
artificial intelligence, building design optimization, sustainable construction, surrogate models, building energy performance, HVAC control, fault detection, building energy consumption, CO2 emissions

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