This study assesses the performance of a multivariate multi‐step charging load prediction approach based on the long short‐term memory (LSTM) and commercial charging data. The major contribution of this study is to provide a comparison of load prediction between various types of charging sites. Real charging data from shopping centres, residential, public, and workplace charging sites are gathered. Altogether, the data consists of 50,504 charging events measured at 37 different charging sites in Finland between January 2019 and January 2020. A forecast of the aggregated charging load is performed in 15‐min resolution for each type of charging site. The second contribution of the work is the extended short‐term forecast horizon. A multi‐step prediction of either four (i.e., one hour) or 96 (i.e., 24 h) time steps is carried out, enabling a comparison of both horizons. The findings reveal that all charging sites exhibit distinct charging characteristics, which affects the forecasting accuracy and suggests a differentiated analysis of the different charging categories. Furthermore, the results indicate that the forecasting accuracy strongly correlates with the forecast horizon. The 4‐time step prediction yields considerably superior results compared with the 96‐time step forecast.

The uptake of electric vehicles (EVs) may pose a challenge to power distribution networks (PDNs). While smart charging can be deployed to relieve stress on the grid, user-centric smart charging strategies could also exacerbate peak power demand due to synchronization when optimizing charging with regard to different objectives, such as charging costs. In this paper, we assess the charging demand emerging from a large fleet of EVs, with models for the decision to charge and distribution of the steady-state state-of-charge (SoC). These are applied to the municipality of Frederiksberg, Denmark, using data from the Danish national travel survey. Home and workplace charging are mapped to the urban 10 kV medium voltage PDN of Frederiksberg considering different charging behaviors and degrees of synchronization. Results indicate that the likelihood of severe congestion in the power distribution network is low and that it can be attributed to rare scenarios in which high synchronization is observed, particularly when maintaining the normal steady-state demand. Despite the low likelihood, preventive measures should be devised to mitigate such scenarios, especially if additional high-power consumers are connected.