Measurement and Verification (M&V) aims to quantify savings achieved as part of energy efficiency and energy management projects. M&V depends heavily on metered energy data, modelling parameters and uncertainties that govern the energy system under consideration. M&V therefore requires a stringent handle on the inherent uncertainties in the calculated savings. The Bayesian framework of data analysis in the form of non-parametric, nonlinear Gaussian Process (GP) regression provides a mechanism by which these uncertainties can be quantified thoroughly, and is therefore an attractive alternative to the more traditional frequentist approach. It is important to select appropriate kernels to construct the prior when performing GP regression. This paper aims to construct a guideline for a practical GP regression within the energy M&V framework. It does not attempt to quantify energy losses or savings, but rather presents a case study that could act as a road map for energy managers and M&V professionals to apply the GP regression as a Bayesian alternative to base-line adjustment. Special attention will be given to the selection of appropriate kernels for the application of baseline adjustment and energy savings quantification in a model-independent manner.
Cet article présente une évaluation de l'importance économique des filières de produits alimentaires sous signes officiels de qualité, pour la France et l'Union européenne. Pour la France, les vins sont étudiés de 1970 à 1997 et les autres produits de 1991 à 1997. L'estimation des volumes dans l'Union européenne concerne les vins de qualité produits dans une région déterminée et les appellations d'origine protégée fromagères, en 1997. Cette étude comparative met en évidence des stratégies de développement différentes suivant les produits et les pays.
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