🤖 AI Summary
This study addresses the critical gap in existing Software Engineering for AI (SE4AI) methodologies regarding the irreversible physical consequences of failures in AI-driven building operations. To overcome this limitation, we propose a novel SE4AI paradigm specifically tailored for systems with physical implications. Through interdisciplinary research and case analysis, this work establishes a foundational software engineering framework and best practice guidelines applicable to physical systems. By rectifying the oversight of traditional methods concerning energy consumption and equipment degradation, this research provides essential theoretical support and engineering standards for the safe and reliable deployment of AI in building automation. Ultimately, these contributions advance the innovation and development of software engineering methodologies within this domain, ensuring robust integration of intelligent technologies in critical infrastructure.
📝 Abstract
Building operations are energy-inefficient. Artificial Intelligence (AI)-driven control systems promise benefits through optimization and predictive control, but deploying them in real buildings reveals a significant software engineering (SE) challenge. SE for AI practices assume digital environments where failures mean poor user experience. Buildings are different. A bad control decision wastes energy irreversibly, violates occupant comfort, or accelerates equipment wear. Although actual safety-critical failures are rare, as real building automation systems are inherently fault-tolerant, the physical and lasting nature of even minor failures fundamentally changes SE4AI requirements. Rooted in two interdisciplinary research projects in civil engineering and computer science that target the AI-driven optimization of building operations, we identify the missing perspectives in SE4AI that currently stymie the successful deployment of AI-based systems for building operations. We further share lessons learned and best practices, and discuss broader implications for engineering AI-driven building operations and cyber-physical systems more generally. Our work proposes a foundation for SE4AI in systems where failure has physical consequences - one the research agenda below will need to validate.