Explainable AI (XAI) has become a major topic in Artificial Intelligence since the mid 2010s. While smart home explainability promises to improve user experience and trust, it is mostly left outside the scope of current AI research. We identify three main challenges that may cause this delay. First, smart device heterogeneity hinders the development of a system-wide vocabulary and communication medium required for end-to-end explanation. Second, smart home runtime changes – e.g. dynamic component additions, deletions and updates – require corresponding explanatory updates. Third, explanation context runtime changes: word meanings may vary with the end-user and the passing seasons – e.g. the notion of cold may vary, depending on the month. To tackle these challenges, we propose a generic, modular XAI reference architecture featuring: i) Local Explanatory Components (LECs) that provide resource-specific explanatory expertise and support runtime extensions; ii) mapping capabilities that allow LECs to translate resource-specific monitoring variables into resource-independent abstractions – predicates and events – which can then be used for generic inter-LEC communication; iii) a generic central component, called Spotlight, that coordinates LECs to generate system-wide explanations. We validate our proposal via a cyber-physical prototype of self-explainable smart home, implemented via a physical home maquette equipped with GrovePi sensors. We show how our prototype can handle several realistic scenarios highlighting the main issues identified above. This provides an initial stepping-stone towards a fully self-explanatory smart home solution. The genericity of our proposal opens the way for transferring it to similar application domains.