## 1 Introduction  
The rapid adoption of electric vehicles (EVs) is a key strategy for reducing greenhouse gas emissions and dependence on fossil fuels in the transportation sector. EVs have the potential to significantly decrease the environmental impact of transportation, which currently accounts for a substantial portion of global carbon emissions [1]. However, the growing number of EVs presents challenges for electricity grids, as charging infrastructure expands and demand for vehicle charging increases [2]. The additional load from EV charging can strain electrical distribution networks, potentially leading to issues such as voltage deviations, transformer overloading, and power quality problems [3]. To ensure reliable operation and plan necessary grid reinforcements, it is critical to accurately predict the impact of EV charging on electricity networks [4]. Grid impact modelling plays a crucial role in understanding the effects of EV charging on the power system, allowing utilities and policymakers to make informed decisions about infrastructure investments, charging strategies, and regulatory frameworks [5]. These models simulate the behavior of EV drivers, the distribution of charging points, and the resulting load on the grid, helping to identify potential bottlenecks and optimize charging patterns to minimize negative impacts [6]. However, the accuracy of grid impact models depends heavily on the quality and granularity of the input data [7]. Many existing models rely on assumptions or aggregated data about EV charging behavior, which may not capture the real-world variability and complexity of charging patterns [8]. This can lead to inaccurate predictions and suboptimal planning decisions [9]. To improve the reliability of grid impact models, it is essential to validate them against real-world data from actual EV charging infrastructure [10]. Analyzing monitored data from EV charging stations can provide valuable insights into actual charging behavior, energy consumption, and infrastructure utilization [11]. This information can be used to refine model assumptions, calibrate simulation parameters, and verify the accuracy of model predictions [12]. By comparing model results with empirical data, researchers can identify discrepancies and improve the representativeness of their models [13]. This iterative process of model validation and refinement is crucial for developing robust and reliable tools for assessing the impact of EV charging on electricity grids [14]. The charging behavior of EVs has already been the subject of several studies and has been investigated using various data sources and methodological approaches. The analysis of the electrical energy required from the grid by the charging points has been studied in the literature through two main approaches: the first through the analysis of real data from the charging points (through private or public data), and the second through a simulative approach, in which different tools are used to calculate the energy demand based on specific input data. Regarding the first approach, the usage behavior of electric vehicle drivers at public charging stations (CSs) was investigated for the first time based on a comprehensive data set by [PERSON] et al. [15], who investigated the Amsterdam metropolitan region from April 2012 to April 2013, analyzing the number of charging events and the spatial distribution of the energy used, thus being at an early stage of electric mobility and only at the urban level. These results were also integrated by [PERSON] et al. as a comparison within a benchmark analysis [16] and for an analysis considering the introduction of specific parking spaces for resharging vehicles, available only at certain times of the day [17]. These studies, contextualized at a time of early development of electric mobility, focused on the relationship between spatial and temporal variables and the charging behavior of vehicles but only considered a limited number of decision-making variables, and only at the urban level.