Research on Intelligent Algorithm Optimization and Energy Demand for Green Energy Building Intelligent Power System

This paper proposes a novel hybrid intelligent optimization framework that integrates machine learning (ML) techniques and heuristic algorithms for energy demand forecasting and distribution optimization in green building power systems. Unlike traditional energy management systems, this framework simultaneously addresses real-time adaptability, multi-source energy input, and operational scalability. The methodology is underpinned by a three-layer system architecture—data processing, optimization, and control— designed for efficient and dynamic energy distribution using MATLAB simulations. Empirical data from a pilot green building validates the framework's effectiveness, demonstrating a 30% reduction in energy consumption and a 38.46% reduction in energy cost. The results highlight the model's potential in advancing intelligent energy systems.