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.