Improving sustainability in the building sector requires more efficient operation of energy-intensive systems such as Heating, Ventilation, and Air Conditioning (HVAC). We present a novel diagnostic framework for HVAC systems that integrates Answer Set Programming (ASP) with Functional Event Calculus (FEC). Our approach exploits the declarative nature of ASP for modeling and incorporates FEC to capture temporal system dynamics. We demonstrate the feasibility of our approach through a case study on a real-world heating system, where we model key components and system constraints. Our evaluation on nominal and faulty traces shows that exploiting ASP in combination with FEC can identify plausible diagnoses. Moreover, we explore the difference between static and rolling-window strategies and provide insights into runtime versus soundness on those variants. Our work provides a step toward the practical application of ASP-based temporal reasoning in building diagnostics.

Modern cyber-physical systems integrate data from many sensors as a regular part of their operations. Over the years, researchers have proposed methods ranging from statistical approaches to neural networks to achieve this sensor fusion, along with high-level paradigms such as early and late fusion. However, quality assurance of sensor fusion algorithms typically focuses on highlighting their accuracy or ability to reduce uncertainty under given conditions. This paper aims to establish a qualitative approach to testing sensor fusion. We formulate an answer set program based on desirable properties for sensor fusion and show how to apply it to test fusion algorithms. Our results indicate that our approach is effective at finding faults, but does not easily find minimal models for large inputs.