AI-Based Energy Optimization in Smart Buildings with Renewable Energy Integration: A Construction Project Management Perspective

Smart buildings are increasingly being promoted as a solution to rising global energy demand, yet many of them still suffer from inefficiencies in energy usage due to poor forecasting, suboptimal control strategies, and limited coordination between energy generation and consumption systems. At the same time, the integration of renewable energy sources such as solar, wind, and hybrid storage systems has introduced additional complexity because of their intermittent and unpredictable nature. In this context, artificial intelligence offers promising capabilities for improving energy optimization through demand prediction, adaptive control, and intelligent scheduling of energy resources. However, most existing studies focus either on AI-based energy management or renewable integration in isolation, with limited attention given to how these systems can be effectively incorporated into construction project management processes. This gap is particularly important during the design and planning phases, where early decisions significantly influence long-term building performance. This paper proposes a conceptual framework that integrates AI-driven energy optimization with renewable energy systems from a construction lifecycle perspective. The framework emphasizes data-driven decision support, lifecycle energy planning, and sustainability-aware project management. The key contribution lies in connecting energy modeling, AI techniques, and construction project decision-making into a unified approach aimed at improving both operational efficiency and environmental performance of smart buildings.

AI, Smart Buildings, Energy Optimization, Renewable Energy, Construction Project Management, IoT, Machine Learning, Sustainability, Energy Management Systems

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