An optimisation algorithm for matching large scale databases on customers for improved characterisation of electricity consumption

Abstract : This paper presents a method that permits to match customer information from the French DSO Enedis and housing information from the French population census institute INSEE. Our method allows having a list of housings linked to each customer in order to add household and building information to customers. We show with our method improvements in predictions of aggregated load curve indicators compared to the traditional method that averages socio demographic indicators from housing information of the zone covered by measurements. Our results indicate that the proposed algorithm is able to capture efficiently the information of housings in some feeders. This permits to combine the databases of the DSO with external databases that exist from census or other processes. Enriching the information at the level of clients through the proposed automated way is a cost effective approach given the number of customers served by a DSO. This enhanced information can be then the basis to model, analyse and simulate demand in a bottom up approach which can be useful for planning purposes of the distribution networks.
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  • HAL Id : hal-01407901, version 1

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Thibaut Barbier, Robin Girard, Nicolas Kong, François-Pascal Neirac, Georges Kariniotakis, et al.. An optimisation algorithm for matching large scale databases on customers for improved characterisation of electricity consumption. MedPower 2016 - The 10th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, Nov 2016, Belgrade, Serbia. ⟨hal-01407901⟩

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