review · Buildings
Buildings account for a major share of international energy consumption and greenhouse gas emissions. This review examines how artificial intelligence can support environmental sustainability across the entire lifecycle of a building. The identified technologies can improve energy efficiency, support predictive maintenance routines, and assist with architectural design simulations. Furthermore, advanced machine learning algorithms enable data-driven evaluations, whilst digital twin systems deliver real-time operational insights to guide decision-making. Despite these benefits, multiple obstacles hinder the broader uptake of these technologies. High financial costs, data security vulnerabilities, and practical implementation difficulties represent notable barriers to adoption. Overcoming these technical and practical hurdles remains essential if artificial intelligence is to be successfully embedded into sustainable building practices.
Buildings represent a significant driver of global power consumption and carbon emissions. Understanding how artificial intelligence can be deployed, from design simulations through to continuous operations, helps industry professionals pinpoint ways to reduce environmental impacts. It also highlights critical operational hurdles, such as security and expense, that must be resolved to make green building technologies viable.
The review identifies clear application areas for building designers, facility managers, and energy management firms seeking tools for design simulation, predictive maintenance, and automated energy optimisation. However, because this is a literature review identifying persistent obstacles around high costs, data security vulnerabilities, and integration difficulties, widespread commercial implementation appears to face practical and technical constraints that require further applied development to resolve.
AI-generated from the published abstract. Always read the original work before citing.
Buildings significantly contribute to global energy consumption and greenhouse gas emissions. This systematic literature review explores the potential of artificial intelegence (AI) to enhance sustainability throughout a building’s lifecycle. The review identifies AI technologies applicable to sustainable building practices, examines their influence, and analyses implementation challenges. The findings reveal AI’s capabilities in optimising energy efficiency, enabling predictive maintenance, and aiding in design simulation. Advanced machine learning algorithms facilitate data-driven analysis, while digital twins provide real-time insights for decision-making. The review also identifies barriers to AI adoption, including cost concerns, data security risks, and implementation challenges. While AI offers innovative solutions for energy optimisation and environmentally conscious practices, addressing technical and practical challenges is crucial for its successful integration in sustainable building practices.
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DOI: 10.3390/buildings14072137
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