Software Engineering for AI-driven Building Operation
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 85 references that are in the map. First posted on arXiv 17 Aug 2026.
Builds on
24 of 85 references.
References · 24
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2020Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challengescited by 120
- 2021Software Engineering for AI-Based Systems: A Surveycited by 68
- 2021Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Processcited by 56
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- 2019Requirements Engineering Challenges in Building AI-Based Complex Systemscited by 21
- 2019Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximationcited by 14
- 2022Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Studycited by 14
- 2023The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practicecited by 14
12 more
- 2021Adapting Software Architectures to Machine Learning Challengescited by 11
- 2020Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systemscited by 10
- 2019Engineering Reliable Deep Learning Systemscited by 6
- 2021On Misbehaviour and Fault Tolerance in Machine Learning Systemscited by 6
- 2024A Large-Scale Study of Model Integration in ML-Enabled Software Systemscited by 6
- 2023Multi-zone HVAC Control with Model-Based Deep Reinforcement Learningcited by 5
- 2023Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repositorycited by 4
- 2024A Survey of AI Reliancecited by 4
- 2024Ventilation and Temperature Control for Energy-efficient and Healthy Buildings: A Differentiable PDE Approachcited by 3
- 2021Data Management for Building Information Modelling in a Real-Time Adaptive City Platformcited by 1
- 2023Data-driven HVAC Control Using Symbolic Regression: Design and Implementationcited by 1
- 2025Digital Twins for Software Engineering Processescited by 1
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