发表机构
University of Innsbruck; Bartenbach - The Lighting Innovators; University of Applied Sciences Burgenland（因斯布鲁克大学; 巴滕巴赫照明创新公司; 布尔根兰应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对AI驱动建筑运行部署面临的软件工程挑战，结合跨学科项目识别缺失视角，分享经验与最佳实践，为故障有物理后果的系统的SE4AI奠定基础。
AI 中文摘要
建筑运行的能源效率低下。人工智能（AI）驱动的控制系统有望通过优化和预测控制带来收益，但将其部署到实际建筑中会暴露出重大的软件工程（SE）挑战。面向AI的软件工程实践假定的数字环境中，故障仅意味着用户体验不佳，而建筑领域则不同。错误的控制决策会不可逆地浪费能源、破坏居住者的舒适度或加速设备损耗。尽管实际的安全关键型故障很少发生，因为实际建筑自动化系统本身具有容错能力，但即使是轻微故障所具有的物理性和持久性，也从根本上改变了面向AI的软件工程（SE4AI）的要求。本研究源于土木工程与计算机科学领域的两个跨学科研究项目，这些项目旨在实现建筑运行的AI驱动优化，我们识别出了SE4AI中目前阻碍基于AI的建筑运行系统成功部署的缺失视角。我们还分享了经验教训和最佳实践，并探讨了其对AI驱动建筑运行及更广泛的网络物理系统工程的更深远影响。本研究为故障具有物理后果的系统中的SE4AI奠定了基础，这是后续研究议程需要验证的内容。
英文摘要
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.
