Building performance monitoring: From in-situ measurement to regression-based approaches

Simple and robust data analysis methodologies are crucial to learn insights from measured data and reduce the performance gap in building stock. For this reason, continuous performance monitoring should become a more diffuse practice in order to improve our design and operation strategies for the future. The research presented aims to highlight potential links between experimental approaches for test-facilities and methods and tools used for continuous performance monitoring, at the state of the art. In particular, we explore the relation between ISO 9869:2014 method for in-situ measurement of thermal transmittance (U) and regression-based monitoring approaches, such as co-heating test and energy signature, for heat load coefficient (HLC) and solar aperture (gA) estimation. In particular, we highlight the robustness and scalability of these monitoring techniques, considering relevant issues in current integrated engineer design perspective. These issues include, among others, the necessity of limiting the number of a sensors to be installed in buildings, the possibility of employing both experimental and real operation data and, finally, the possibility to automate and perform monitoring at multiple scales, from single components, to individual buildings, to building stock and cities.