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Journal Articles Energy and IA Year : 2024

Predicting missing Energy Performance Certificates: Spatial interpolation of mixture distributions

Prédire les DPE manquants : Interpolation spatiale de distributions de mélanges

Abstract

Mass renovation goals aimed at energy savings on a national scale require a significant level of public financial commitment. To identify target buildings, decision-makers need a thorough understanding of energy performance. Energy Performance Certificates (EPC) provide information about areas of space, such as land plots or a building’s footprint, without specifying exact locations. They cover only a fraction of dwellings. This paper demonstrates that learning from observed EPCs to predict missing ones at the building level can be viewed as a spatial interpolation problem with uncertainty both on input and output variables. The Kriging methodology is applied to random fields observed at random locations to determine the Best Linear Unbiased Predictor (BLUP). Although the Gaussian setting is lost, conditional moments can still be derived. covariates are admissible, even with missing observations. We present applications using both simulated and real data, with a specific case study of a city in France serving as an example.
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Dates and versions

hal-03276127 , version 1 (01-07-2021)
hal-03276127 , version 2 (25-08-2022)
hal-03276127 , version 3 (07-03-2023)
hal-03276127 , version 4 (17-01-2024)

Licence

Attribution - NonCommercial

Identifiers

Cite

Marc Grossouvre, Didier Rullière, Jonathan Villot. Predicting missing Energy Performance Certificates: Spatial interpolation of mixture distributions. Energy and IA, In press, 16, pp.100339. ⟨10.1016/j.egyai.2024.100339⟩. ⟨hal-03276127v4⟩
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