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In this paper, we propose a practical Bayesian framework for the calibrationand validation of a computer code, and apply it to a case study concerning theenergy consumption forecasting of a building. Validation allows to quantifyforecasting uncertainties in view of the code's final use. Here we explore thesituation where an energy provider promotes new energy contracts forresidential buildings, tailored to each customer's needs, and including aguarantee of energy performance.  Based on power field measurements, collected from an experimental buildingcell over a certain time period, the code is calibrated, effectively reducingthe epistemic uncertainty affecting some code parameters (here albedo, thermalbridge factor and convective coefficient). Validation is conducted by testingthe goodness of fit of the code with respect to field measures, and then bypropagating the a posteriori parametric uncertainty through the code, yieldingprobabilistic forecasts of the average electric power delivered inside the cellover a given time period.  To illustrate the benefits of the proposed Bayesian validation framework, weaddress the decision problem for an energy supplier offering a new type ofcontract, wherein the customer pays a fixed fee chosen in advance, based on anoverall energy consumption forecast. According to Bayesian decision theory, weshow how to choose such a fee optimally from the point of view of the supplier,in order to balance short-terms benefits with customer loyalty.

Validation of a computer code for the energy consumption of a building, with application to optimal electric bill pricing