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Here, we develop a data-centric approach enabling to analyse whichactivities, function, and characteristics of the environment surrounding theslow charging infrastructure impact the distribution of the electricityconsumed at slow charging infrastructure. To gain a basic insight, we analysedthe probabilistic distribution of energy consumption and its relation toindicators characterizing charging events. We collected geospatial datasets andutilizing statistical methods for data pre-processing, we prepared featuresmodelling the spatial context in which the charging infrastructure operates. Toenhance the statistical reliability of results, we applied the bootstrap methodtogether with the Lasso method that combines regression with variable selectionability. We evaluate the statistical distributions of the selected regressioncoefficients. We identified the most influential features correlated withenergy consumption, indicating that the spatial context of the charginginfrastructure affects its utilization pattern. Many of these features arerelated to the economic prosperity of residents. Application of the methodologyto a specific class of charging infrastructure enables the differentiation ofselected features, e.g. by the used rollout strategy. Overall, the paperdemonstrates the application of statistical methodologies to energy data andprovides insights on factors potentially shaping the energy consumption thatcould be utilized when developing models to inform charging infrastructuredeployment and planning of power grids.

Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data