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<p>Buildings consume about 36% of global energy and contribute nearly 40% of CO? emissions, making them central to the challenges of energy and climate. Artificial intelligence (AI) offers transformative pathways to improve forecast accuracy, optimize consumption, and support low carbon transitions. Oriented by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review systematically screened the literature from 2020 to July 2025, with selective inclusion of previous foundational studies. In total, 268 publications were reviewed and 70 analyzed in depth. The synthesis covers three domains: (i) energy forecasting, with machine learning (ML) and deep learning (DL) improving demand and renewable generation prediction; (ii) optimization, with AI improving Heating, Ventilation, and Air Conditioning (HVAC) control, renewable scheduling, storage management, and smart grid operations; and (iii) energy efficiency, with AI–Internet of Things (IoT) frameworks enabling predictive control, fault detection, and Net Zero Energy Building (NZEB) strategies. Reported impacts include energy savings for HVAC of up to 37%, solar scheduling that reduces costs by 35%, and AI - IoT integration that reduces emissions by 21%. Publication trends show rapid growth since 2020, reflecting accelerated technological progress. The remaining challenges include data fragmentation, interoperability, high computational demand, and cybersecurity risks. In general, the findings highlight AI as a key enabler of resilient, efficient and climate-adaptive building energy systems.</p>

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Harnessing Artificial Intelligence to improve building performance and energy use: innovations, challenges, and future perspectives

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月12日

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  • Tegenu Argaw Woldegiyorgis,
  • Hong Xian Li,
  • Eninges Asmare,
  • Abera Debebe Assamnew,
  • Fekadu Chekol Admassu,
  • Gezahegn Assefa Desalegn,
  • Solomon Kebede Asefa,
  • Sentayehu Yigzaw Mossie

摘要

Buildings consume about 36% of global energy and contribute nearly 40% of CO? emissions, making them central to the challenges of energy and climate. Artificial intelligence (AI) offers transformative pathways to improve forecast accuracy, optimize consumption, and support low carbon transitions. Oriented by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review systematically screened the literature from 2020 to July 2025, with selective inclusion of previous foundational studies. In total, 268 publications were reviewed and 70 analyzed in depth. The synthesis covers three domains: (i) energy forecasting, with machine learning (ML) and deep learning (DL) improving demand and renewable generation prediction; (ii) optimization, with AI improving Heating, Ventilation, and Air Conditioning (HVAC) control, renewable scheduling, storage management, and smart grid operations; and (iii) energy efficiency, with AI–Internet of Things (IoT) frameworks enabling predictive control, fault detection, and Net Zero Energy Building (NZEB) strategies. Reported impacts include energy savings for HVAC of up to 37%, solar scheduling that reduces costs by 35%, and AI - IoT integration that reduces emissions by 21%. Publication trends show rapid growth since 2020, reflecting accelerated technological progress. The remaining challenges include data fragmentation, interoperability, high computational demand, and cybersecurity risks. In general, the findings highlight AI as a key enabler of resilient, efficient and climate-adaptive building energy systems.