Artificial Intelligence in Tropical Building Performance: A Systematic Literature Review
DOI:
https://doi.org/10.11113/ijbes.v13.n2.1627Keywords:
Artificial Intelligence, Building Performance, Tropical Climate, Energy Efficiency, Indoor Environmental QualityAbstract
This Systematic Literature Review (SLR) explores the integration of Artificial Intelligence (AI) technologies in building performance research within tropical climates, which are characterized by high temperatures, humidity, and solar radiation. These environmental conditions present challenges for achieving energy efficiency and occupant comfort. The review, guided by the PRISMA framework and structured using the PICO model, synthesizes findings from 56 peer-reviewed articles published between 2015 and 2025. It aims to identify the types of AI technologies employed, their specific applications, and the challenges and opportunities associated with their implementation in tropical building contexts. The analysis reveals increasing adoption of AI in domains such as indoor environmental quality (IEQ), energy consumption, HVAC optimization, and daylighting. Techniques such as machine learning, deep learning, neural networks, and expert systems are commonly used for predictive modelling, simulation, and real-time control. AI has demonstrated significant potential in enhancing building performance, enabling more adaptive and efficient systems. However, the review also identifies several limitations. These include the scarcity of high-quality, localized data in tropical regions, limited generalizability of AI models across diverse building types, and the lack of integration with real-time building management systems. Daylighting and passive design strategies remain underexplored, and there is a need for more occupant-centric approaches. To address these gaps, future research should focus on hybrid modelling techniques, explainable AI, and the development of open-access datasets. Collaboration among researchers and policymakers can translate AI research into context-specific tools and standards supporting SDGs 7, 11, and 13 for sustainable tropical buildings.
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