An Adaptive Artificial Intelligence System for Energy Efficiency Optimization in Smart Buildings
Keywords:
adaptive artificial intelligence; smart buildings; energy efficiency; reinforcement learning; digital twins; edge computing; interoperability; policy governanceAbstract
The building sector remains one of the largest contributors to global energy consumption and greenhouse gas emissions, creating an urgent need for intelligent, adaptive optimization strategies that transcend static rule-based and manual control paradigms. This paper proposes a system-level artificial intelligence architecture for energy efficiency in smart buildings, designed to continuously learn, adapt, and operate under real-world constraints. The architecture integrates reinforcement learning, digital twins, edge-cloud orchestration, and federated knowledge sharing to manage the complex, time-varying dynamics of building energy systems. Emphasis is placed on structural trade-offs between centralization and decentralization, model accuracy and computational cost, and responsiveness and privacy. The system’s adaptive learning pipeline leverages online model updating, transfer learning, and meta-learning to maintain performance under occupancy shifts, equipment degradation, and climatic variability. Comprehensive attention is given to data interoperability, metadata standardization, data governance, and the integration of differential privacy to protect occupant information. Governance and fairness dimensions are explored by analyzing how control policies can unevenly distribute thermal comfort and energy costs, and by discussing transparency, stakeholder engagement, and alignment with AI regulatory frameworks. Deployment considerations include cyber-physical resilience, lifecycle carbon accounting of AI hardware, and mitigation of energy rebound effects. The paper provides a multidisciplinary synthesis of recent advances and identifies structural, regulatory, and sustainability challenges that must be resolved to realize adaptive AI systems in large-scale building portfolios.
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