BAYREB

The BAYREB project proposes investigating the theoretical potential of Bayesian inference applied to the diagnostics of existing buildings before their refurbishment. The largest potential for energy savings in the building sector lies in the renovation of the existing building stock. As a result, an increasing amount of research is being dedicated to encourage decision makers. Guaranteeing the performance of a building after renovation would be an efficient incitation, but is still difficult to perform in practice. A necessary condition for a cost-effective refurbishment, adapted to each specific building, is to perform detailed diagnostics of its performance prior to picking solutions: for instance, to estimate which proportion of the energy consumption is caused by air leakage, by transfer through the envelope, or by a dysfunction of the heating systems. The solution for establishing such diagnostics is to implement inverse techniques, which are able to automatically learn from in-situ measurement data in order to construct a realistic representation of the characteristics of a building. The importance of the energy audit of existing buildings has already motivated several projects which underlined the difficulty of applying inverse methods for the identification of building properties. The overall observation of these projects is that a reliable energy audit requires a rigorous theoretical basis in order to explore its full capabilities. Moreover, previous works on the topic of building energy performance characterisation meet two main problems: - The most common inverse methods in engineering are deterministic and do not ensure finding the global optimum in the search space. Moreover, they often only offer point estimates of the sought properties: confidence intervals on identified parameters can only be obtained by a separate forward sensitivity analysis. - When applied to building physics, inverse methods always rely on a simplified modelling of the buildings (RC models) and may exceedingly overlook important influences on the energy performance: occupant behaviour, HVAC control strategies and coupled influences of heat, air and moisture. While always keeping in mind the realities of the energy performance monitoring of buildings, the BAYREB project will attempt to go closer to the fundamentals of inverse techniques, in order to apply them for this purpose. The novelty of the BAYREB proposal compared to previous projects is the specific choice of the Bayesian statistical framework, and fundamental research on the capabilities and limitations of inverse methods applied to building physics. The principle of Bayesian inference is to draw conclusions from incomplete observations of a system, and update knowledge as more data is available. Given a set of records (sensor data and inaccuracy), and some prior assumption on the model structure (expert knowledge), one can compute the probability of their causes (energy audit, envelope properties…) by means of conditional probability and the Bayes theorem. The particularity of Bayesian inference, as opposed to other inverse methods, is that it inherently performs an inverse propagation of uncertainty: missing data, sensor inaccuracy, or simplifying model assumptions, will have a direct effect on the assessment of building characteristics and their confidence intervals. A second advantage is the fact that it can be applied to any class of mathematical function, from white-box to black-box models, as it does not require the computation of sensitivity gradients. The ambition of the BAYREB project, compared to related work on building parameter identification, is therefore twofold: to address the problem in a fully stochastic manner which will account for all model and measurement uncertainty; and to not force end users to a given building simulation software.