Spatio-Temporal Modelling of Electric Vehicle Charging Demand

Description This repository contains the comprehensive datasets supporting the research paper: "Spatio-Temporal Modelling of Electric Vehicle Charging Demand" The data provided here enables the replication of the spatio-temporal analysis and demand forecasting models presented in the study. Datasets The data is organized into the following subfolders for clarity: Sessions_from_ChargePlaceScotland: Standardized Excel files prepared for preprocessing. glasgow datasets: Raw datasets specifically focused on the Glasgow region, serving as the primary case study for our model validation. infos_cpids: Metadata and master files for Charge Point Identifiers (CPIDs). This includes technical specifications, power ratings, and connector types for each charging station. Master file scotland dataset: The core longitudinal dataset for Scotland, covering the period from 2022 to 2025. It serves as the foundational data for the temporal demand analysis. Meteo dataset: Historical weather-related data used as external covariates (e.g., temperature, precipitation) to enhance the predictive accuracy of the charging demand models. shapefile for maps: Geographic spatial boundaries used for map visualizations, geographic indexing, and regional aggregation within the study area. Usage Note: For detailed methodology on how these datasets were pre-processed and integrated into the spatio-temporal model, please refer to the associated paper and the git repository of the work.