Validation of a Computer Code for the Energy Consumption of a Building, with Application to Optimal Electric Bill Pricing

Merlin Keller, Guillaume Damblin, Alberto Pasanisi, Mathieu Schumann, Pierre Barbillon, Fabrizio Ruggeri () and Eric Parent
Additional contact information
Merlin Keller: Électricité de France, 78401 Chatou, France
Guillaume Damblin: CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Alberto Pasanisi: Edison, 20121 Milano, Italy
Mathieu Schumann: Électricité de France, 91120 Palaiseau, France
Pierre Barbillon: Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120 Palaiseau, France
Fabrizio Ruggeri: CNR IMATI, Via Alfonso Corti 12, 20133 Milano, Italy
Eric Parent: Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120 Palaiseau, France

Econometrics, 2022, vol. 10, issue 4, 1-24

Abstract: In this paper, we present a case study aimed at determining a billing plan that ensures customer loyalty and provides a profit for the energy company, whose point of view is taken in the paper. The energy provider promotes new contracts for residential buildings, in which customers pay a fixed rate chosen in advance, based on an overall energy consumption forecast. For such a purpose, we consider a practical Bayesian framework for the calibration and validation of a computer code used to forecast the energy consumption of a building. On the basis of power field measurements, collected from an experimental building cell in a given period of time, the code is calibrated, effectively reducing the epistemic uncertainty affecting the most relevant parameters of the code (albedo, thermal bridge factor, and convective coefficient). The validation is carried out by testing the goodness of fit of the code with respect to the field measurements, and then propagating the posterior parametric uncertainty through the code, obtaining probabilistic forecasts of the average electrical power delivered inside the cell in a given period of time. Finally, Bayesian decision-making methods are used to choose the optimal fixed rate (for the energy provider) of the contract, in order to balance short-term benefits with customer retention. We identify three significant contributions of the paper. First of all, the case study data were never analyzed from a Bayesian viewpoint, which is relevant here not only for estimating the parameters but also for properly assessing the uncertainty about the forecasts. Furthermore, the study of optimal policies for energy providers in this framework is new, to the best of our knowledge. Finally, we propose Bayesian posterior predictive p -value for validation.

Keywords: uncertainty quantification; Bayesian analysis; energy contracts (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:10:y:2022:i:4:p:34-:d:988008

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