Data-Driven Energy Optimization for Intelligent Buildings with Clean Energy Integration
Keywords:
Data-driven optimization, Intelligent buildings, Renewable energy integration, Artificial intelligenceAbstract
The rapid transformation of modern buildings into intelligent energy ecosystems has created a critical need for advanced optimization approaches capable of integrating heterogeneous energy resources, renewable generation, energy storage, and dynamic consumption patterns. Traditional building energy management strategies often rely on static operational rules that cannot effectively address uncertainty arising from renewable energy intermittency, occupant behavior variations, electricity price fluctuations, and complex interactions between distributed energy resources. This research paper investigates a data-driven energy optimization framework for intelligent buildings with clean energy integration, emphasizing artificial intelligence-enabled decision-making, stochastic optimization, demand response, and coordinated energy management. The study develops a conceptual framework combining data acquisition, predictive analytics, optimization algorithms, and adaptive control mechanisms to improve building energy efficiency while increasing renewable energy utilization.
The research synthesizes existing optimization methodologies in active distribution networks, integrated energy systems, distributed generation planning, and uncertainty-aware scheduling to establish their applicability within intelligent building environments. Previous studies have demonstrated the effectiveness of chance-constrained optimization, robust optimization, stochastic scheduling, and distributionally robust approaches in addressing uncertain energy system conditions (Akhavn-Hejazi & Mohsenian-Rad, 2018; Álorc & Sun, 2017; Ning & You, 2018). Building upon these foundations, this paper proposes an integrated data-driven optimization architecture where machine learning-based forecasting supports real-time energy allocation, renewable energy coordination, storage management, and demand response decisions.
The proposed framework considers intelligent buildings as interconnected cyber-physical energy systems rather than isolated consumption units. It incorporates photovoltaic generation, battery energy storage, flexible loads, electric vehicles, and grid interaction mechanisms to achieve improved operational reliability and reduced carbon emissions. The methodology analyzes optimization objectives including energy cost minimization, renewable energy consumption maximization, peak demand reduction, and enhancement of system resilience. The findings indicate that data-driven optimization can significantly improve the adaptability of building energy management systems by transforming historical and real-time operational data into actionable control strategies.
The study further discusses practical implementation challenges, including data quality requirements, computational complexity, privacy concerns, interoperability limitations, and uncertainty management. The integration of artificial intelligence with clean energy technologies represents a significant pathway toward sustainable intelligent buildings. Recent research has highlighted the increasing role of AI-based energy optimization in smart building management and renewable integration, particularly from a project implementation and operational perspective (Philip, 2026). This paper contributes a comprehensive analytical framework for researchers, engineers, and policymakers seeking to develop next-generation intelligent building energy systems capable of achieving efficiency, sustainability, and resilience.
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