Predicting missing Energy Performance Certificates: Spatial interpolation of mixture distributions

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-02-09 DOI:10.1016/j.egyai.2024.100339
Marc Grossouvre , Didier Rullière , Jonathan Villot
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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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预测丢失的能源性能证书:混合分布的空间插值
在全国范围内实现大规模节能改造目标需要大量的公共财政投入。为了确定目标建筑,决策者需要全面了解能源性能。能源性能证书(EPC)提供的是空间区域的信息,如地块或建筑物的占地面积,而没有具体说明确切的位置。它们只覆盖了一小部分住宅。本文证明,从观测到的 EPCs 中学习,以预测建筑物层面上缺失的 EPCs,可视为一个空间插值问题,输入和输出变量都存在不确定性。克里金方法适用于在随机位置观测到的随机场,以确定最佳线性无偏预测器 (BLUP)。虽然失去了高斯设置,但条件矩仍然可以导出。即使观测数据缺失,也可以使用协变量。我们以法国某城市的具体案例研究为例,介绍了模拟数据和真实数据的应用。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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