LAUSR

Here, we develop a data-centric approach to analyse which activities, functions, and characteristics of the environment surrounding the slow charging infrastructure impact the distribution of the electricity consumed at slow charging infrastructure. We analysed the probability distribution of energy consumption and its relation to indicators characterising charging events to gain basic insights. The energy consumption can be satisfactorily modelled by a transformed beta distribution and the number of charging transactions is the driving factor among the characteristics constituting the energy consumption. We collected geospatial datasets and prepared a large number of candidate features modelling the spatial context in which the charging infrastructure operates. Using statistical methods, we identified and interpreted a relatively small subset of the most influential features correlated with energy consumption. The majority of these features are related to the economic prosperity of residents. Residents and businesses with high (low) income, situated nearby charging infrastructure, are linked to a positive (negative) impact on energy consumption. Similarly, charging infrastructure located close to expensive newly built housing shows higher energy consumption. The largest adverse impact has the high concentration of residents receiving social assistance. By applying the methodology to a specific charging infrastructure class, e.g. determined by the used rollout strategy, we differentiated the selected features. Business types, working sector of residents and public venues in the proximity are linked to higher consumption of energy at charging infrastructure deployed strategically. Characteristics linked with the age structure of the population are linked to the energy consumption at charging infrastructure placed based on the demand. Data collection and data processing are among the most time-consuming activities. The paper provides valuable insights into which data to collect and use as features when developing prediction models to inform charging infrastructure deployment and planning of power grids.