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An Energy Performance Contracting (EPC) is a financing agreement offered by general contractors that enables cost savings from reduced energy consumption to building owners. To create such an offer, the contractor has to provide an energy consumption threshold and a measurement plan. This article aims to draw some recommendations to choose an appropriate approach to provide the information necessary to create the contract, regarding computation time budget, expected accuracy and type of information provided. To get these results, we couple thermal simulations to various uncertainty and sensitivity methods. We first compare screening and differential sensitivity to reduce the number of inputs of the statistical study. Then, we analyze various uncertainty analysis methods to set an appropriate energy consumption threshold, considering the input uncertainties and the study context (Quadratic combination, directional and importance sampling and reliability methods). Sensitivity analyses in various input spaces are then carried out to identify the most critical contributors to energy levels to create the measurement plan. Finally, two metamodeling approaches are tested to reduce the overall computational time: Kriging and sparse polynomial chaos. These methods are tested and compared on a 4000 m² office building in Nantes, France. The resulting recommendations can be applied to any building, depending on the model regularity, the number of uncertain parameters and the objective of the study. [ABSTRACT FROM AUTHOR]

Highlights: [•] Four gray box models are compared on their prediction ability. [•] Power and temperature prediction fitting exceed 84% in heating and cooling mode. [•] A novel R6C2 model presents the best results with a validated identification method. [•] A sensibility analysis allows to validate the R6C2 model architecture. [ABSTRACT FROM AUTHOR]

Abstract: Nuclear steam generators are subject to clogging of their internal parts which causes safety issues. Diagnosis methodologies are needed to optimize maintenance operations. Clogging alters the dynamic behaviour of steam generators and particularly the response of the wide range level (WRL – a pressure measurement) to power transients. A numerical model of this phenomenon has previously been developed. Its input variables describe the spatial distribution of clogging and its output is a discretization of the WRL dynamic response. The objective of the present study is to characterize the information about the clogging state of a steam generator that can be inferred from the observation of its WRL response. A methodology based on several statistical techniques is implemented to answer that question. Principal component analysis reveals that clogging alters the WRL response mainly in two distinct ways. Accordingly, the output can be summarized into a vector of dimension 2. A sensitivity analysis is carried out to rank the input variables by magnitude of influence. It has shown that they can be divided into two groups corresponding to the two sides of the steam generator. Finally, sliced inverse regression is used to reduce the input dimension from 16 to 2. A sampling issue that arises when the input dimension is high is addressed. The simplification of the original problem yields a diagnosis methodology based on response surface techniques. [Copyright &y& Elsevier]