Intelligent Energy Planning and Optimization in Smart Buildings With Green Power Integration

Abstract

The rapid expansion of urbanization, increasing energy demand, and global efforts toward carbon neutrality have accelerated the transition from conventional building energy management approaches to intelligent and sustainable energy planning frameworks. Smart buildings integrated with renewable energy systems represent a significant pathway for improving energy efficiency, reducing environmental impacts, and enhancing operational resilience. However, the effective implementation of intelligent energy optimization requires the integration of advanced computational techniques, renewable energy coordination, demand-side management strategies, and effective project planning mechanisms. This research paper investigates an intelligent energy planning and optimization framework for smart buildings incorporating green power integration. The study develops a conceptual research model based on artificial intelligence-driven optimization, renewable energy utilization, energy efficiency improvement, and sustainable infrastructure management principles.

The research synthesizes existing studies related to net-zero energy strategies, smart green city planning, renewable electricity adoption, energy efficiency improvement, and intelligent building management. The methodology adopts a systematic analytical approach by examining technological, operational, and managerial dimensions influencing smart building energy performance. The proposed framework considers renewable generation forecasting, intelligent load management, energy consumption prediction, and adaptive optimization mechanisms as key components for achieving sustainable building operations. Previous research highlights that integration of renewable energy systems with intelligent decision-making approaches can support long-term emission reduction objectives and improve energy system flexibility (Jiyoung Kong & Cho, 2021).

The findings indicate that intelligent energy planning enables better coordination between energy generation, consumption patterns, and environmental objectives. Artificial intelligence-based optimization approaches can enhance energy utilization efficiency by continuously analyzing operational data and adjusting energy strategies according to changing conditions. The integration of renewable power sources further strengthens sustainability performance by reducing dependency on conventional energy systems. The study also identifies challenges related to technological complexity, investment requirements, interoperability issues, and policy coordination. As demonstrated in recent research, AI-based energy optimization in smart buildings provides opportunities for improving energy performance through predictive analytics, automated control, and renewable integration strategies (Philip, 2026).

This research contributes to the academic understanding of sustainable building energy management by presenting an integrated perspective combining technological intelligence and green energy planning. The proposed framework provides insights for researchers, policymakers, construction managers, and energy professionals seeking effective strategies for developing future-ready smart buildings. The study concludes that intelligent energy optimization supported by renewable integration is essential for achieving sustainable urban development and carbon-neutral infrastructure objectives.