An address-matching algorithm for household-scale databases to enhance electricity demand characterization

Abstract : This paper participates in the challenging data science opportunity offered by the growing number of databases made available to public institutions. It presents an innovative method to match household-scale databases using address information. The developed algorithm authorizes different matching qualities, depending on the reliability of the link between the paired elements. This work was carried out in collaboration with the French DSO Enedis, which provided valuable customer information that was matched with a national database describing dwellings. The matching algorithm performances are analyzed, and adjustments are proposed to improve the matching quality in urban, suburban and rural contexts. Lastly, two basic characterization analyses were made to highlight the potential of these consolidated databases.
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Communication dans un congrès
11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2018), Nov 2018, Dubrovnik (Cavtat), Croatia. 〈http://www.medpower2018.com/〉
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Antoine Rogeau, Robin Girard, Georges Kariniotakis, Nicolas Kong. An address-matching algorithm for household-scale databases to enhance electricity demand characterization. 11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2018), Nov 2018, Dubrovnik (Cavtat), Croatia. 〈http://www.medpower2018.com/〉. 〈hal-01948608〉

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