Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data

Simon Rouchier (1)

1 LOCIE - LabOratoire proCédés énergIe bâtimEnt

Building physics researchers have benefitted from elements of statistical learning and time series analysis to improve their ability to construct knowledge from data. What is referred to here as inverse problems are actually a very broad field that encompasses any study where data is gathered and mined for information. The purpose of the present article is twofold. First, it is a tutorial on the formalism of inverse problems in building physics and the most common ways to solve them. Then, it provides an overview of tools and methods that can either be used to assess or the reliability of inverse problem results, prevent erroneous interpretation of data, and optimise information gained by experiments. It provides an introduction, along with useful references, to the topics of estimation error assessment, regularisation, identifiability analysis, residual analysis, model selection and optimal experiment design. These concepts are presented in the context of building simulation and energy performance assessment: a simple RC model is used as a running example to illustrate each chapter.

Mots clés: model selection; optimal experiment design; inverse problems; identifiability; validation; likelihood

Domaines: Construction durable; Applications [stat.AP]

Simon Rouchier. Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data. Energy and Buildings, 2018, 166, pp.178-195. ⟨10.1016/j.enbuild.2018.02.009⟩. ⟨hal-01739623⟩