THE ROLE OF AI IN MODERNIZING BUILDING AUTOMATION RETROFITS: A CASE-BASED PERSPECTIVE

Abstract

The modernization of building automation systems (BAS) is increasingly essential for enhancing energy efficiency, reducing operational costs, and meeting the sustainability goals of existing building infrastructure. Traditional retrofitting approaches often fall short in addressing complex, dynamic building environments due to their limited adaptability and reliance on manual control systems. This paper investigates the transformative role of Artificial Intelligence (AI) in accelerating and optimizing building automation retrofits. Leveraging a case-based methodology, we analyze three diverse implementations—in commercial, educational, and healthcare settings—where AI-driven systems were integrated into legacy BAS environments. The study explores the use of machine learning for predictive HVAC control, AI-powered energy demand forecasting, and anomaly detection for equipment fault diagnostics. Quantitative results reveal energy savings ranging from 18% to 32%, significant reductions in unscheduled maintenance, and improved occupant comfort. The research further presents an architectural framework that illustrates the integration of AI into existing building systems, incorporating edge analytics, cloud intelligence, and IoT interoperability. Finally, key implementation challenges such as legacy system compatibility, data quality, and cybersecurity are discussed, along with future directions including digital twins and autonomous building operations. The findings demonstrate that AI is not only feasible for retrofits but essential in transitioning toward intelligent, adaptive, and sustainable buildings.