Ensuring Future-Proof BAS: A Comparative Analysis of AI-Driven Commissioning Techniques and Best Practices Toward Net-Zero Consumption

Dhruv Shah

Abstract: The evolution of Building Automation Systems (BAS) plays a critical role in achieving net-zero energy (NZE) performance in commercial and institutional buildings. However, traditional commissioning approaches struggle to adapt to modern energy ecosystems' complex, dynamic demands. This paper explores the emergence of AI-driven commissioning techniques as a transformative solution for ensuring continuous optimization, self-learning fault detection, and predictive energy management. By leveraging advanced machine learning algorithms, AI-powered commissioning enhances real-time system tuning, proactively identifies inefficiencies, and integrates with renewable energy sources for adaptive load balancing. This study identifies best practices, implementation challenges, and key performance benchmarks through a comparative analysis of AI-driven strategies versus conventional methods. Additionally, it discusses how AI-enhanced commissioning can future-proof BAS by ensuring resilience, interoperability, and alignment with evolving energy regulations. The findings highlight the potential of intelligent commissioning frameworks to drive net-zero operational efficiencies, reduce energy waste, and maximize building performance in a rapidly evolving smart-grid ecosystem.

Keywords: AI-driven commissioning, net-zero energy, building automation systems, machine learning, predictive energy management