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<p>This systematic review and meta-analysis critically evaluates artificial intelligence (AI) applications for energy optimization in smart buildings through comprehensive analysis of 126 peer-reviewed studies (2010–2024) from four major databases. We present a novel taxonomic framework categorizing AI implementations into five distinct approaches: predictive systems, adaptive control, pattern recognition, hybrid ensemble methods, and edge AI implementations. Our meta-analysis reveals significant performance variations: reinforcement learning achieves highest energy savings (22.3% ± 8.4%, 95% CI: 20.2–24.4%, I<sup>2</sup> = 73%), followed by hybrid methods (28.1% ± 12.3%, 95% CI: 23.4–32.8%, I<sup>2</sup> = 81%) and supervised learning (14.7% ± 5.2%, 95% CI: 12.9–16.5%, I<sup>2</sup> = 45%). However, substantial heterogeneity exists across building types and climate zones. Critical findings include limited real-world deployment (18% academic literature, 26% including industry reports), predominant focus on office buildings (78%) and temperate climates (67%), and insufficient multi-system integration (76% single-system studies). Economic analysis indicates ROI periods of 2.1–5.8&#xa0;years (median 3.4&#xa0;years) with implementation costs varying from $8,000-$47,000 per facility. We identify five persistent research gaps and propose a prioritized research agenda addressing implementation barriers, standardization needs, and occupant-centric optimization. This study provides the first comprehensive benchmarking framework for AI building energy systems and establishes evidence-based guidelines for practical deployment.</p> Graphical Abstract <p></p>

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Artificial intelligence for energy optimization in smart buildings: A systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月5日

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  • Lakpriya Udayanga Gunasena Ekanayaka Gunasinghalge,
  • Ammar Alazab,
  • Md. Alamin Talukder

摘要

This systematic review and meta-analysis critically evaluates artificial intelligence (AI) applications for energy optimization in smart buildings through comprehensive analysis of 126 peer-reviewed studies (2010–2024) from four major databases. We present a novel taxonomic framework categorizing AI implementations into five distinct approaches: predictive systems, adaptive control, pattern recognition, hybrid ensemble methods, and edge AI implementations. Our meta-analysis reveals significant performance variations: reinforcement learning achieves highest energy savings (22.3% ± 8.4%, 95% CI: 20.2–24.4%, I2 = 73%), followed by hybrid methods (28.1% ± 12.3%, 95% CI: 23.4–32.8%, I2 = 81%) and supervised learning (14.7% ± 5.2%, 95% CI: 12.9–16.5%, I2 = 45%). However, substantial heterogeneity exists across building types and climate zones. Critical findings include limited real-world deployment (18% academic literature, 26% including industry reports), predominant focus on office buildings (78%) and temperate climates (67%), and insufficient multi-system integration (76% single-system studies). Economic analysis indicates ROI periods of 2.1–5.8 years (median 3.4 years) with implementation costs varying from $8,000-$47,000 per facility. We identify five persistent research gaps and propose a prioritized research agenda addressing implementation barriers, standardization needs, and occupant-centric optimization. This study provides the first comprehensive benchmarking framework for AI building energy systems and establishes evidence-based guidelines for practical deployment.

Graphical Abstract