EV Charging and Urban Data for Norway

Columns

• EV charging reports: Contains details for 6,878 individual charging sessions from 97 user IDs, recorded between December 2018 and January 2020. Each entry includes plug-in time, plug-out time, charged energy per session, user ID, charger ID, and address.
• Hourly EV charging loads and idle capacity (per user): Describes EV charging loads and non-charging idle capacity for each user and individual charging sessions. Charging power is assumed to be 3.6 kW or 7.2 kW, with immediate charging upon plug-in. Non-charging idle time reflects potential flexibility, and synthetic idle capacity represents potential energy load during idle periods.
• Hourly EV charging loads and idle capacity (aggregated): Provides aggregated EV charging loads and idle capacity for users with either private or shared charging points (CPs). Assumed charging power is 3.6 kW or 7.2 kW, with immediate charging after plug-in.
• Hourly smart meter data from garage Bl2: Features hourly aggregated electricity use from smart meters within garage Bl2. This garage accounts for 33% of the charging sessions (2,243 sessions), with 22 out of 24 EV parking locations having AMS-meters measuring aggregated EV charging hourly.
• Local traffic density: Offers hourly traffic density counts of vehicles shorter than 5.6 metres across 5 nearby traffic locations, from December 2018 to January 2020.
• Weather Data: Includes local weather features such as temperature and rainfall for Trondheim, Norway, also from December 2018 to January 2020.

Distribution

• Dataset 1_EV charging reports.csv: 976.23 kB
• Dataset 2_Hourly EV loads - Per user.csv: 5.4 MB
• Dataset 3a_Hourly EV loads - Aggregated private.csv: 519.93 kB
• Dataset 3b_Hourly EV loads - Aggregated shared.csv: 446.18 kB
• Dataset 5_AMS data from garage Bl2.csv: 500.33 kB
• Dataset 6_Local traffic distribution.csv: 559.19 kB
• Norway_Trondheim_ExactLoc_Weather.csv: 76.7 kB The total archive size for Version 3 is 8.48 MB. All timestamps are in the Central European Time (CET) zone.

Usage

• Charging time/Session Duration Prediction: Developing models to forecast how long EV charging sessions will last.
• Energy consumption/EnergyLoad Prediction: Creating predictive models for EV energy consumption and overall energy load on the grid.
• Residential EV Charging Analysis: Investigating patterns, trends, and influences on electric vehicle charging behaviour in apartment settings.

Coverage

• Geographic: The data originates from a large housing cooperative in Trondheim, Norway.
• Time Range: The dataset covers the period from December 2018 to January 2020.
• Demographic Scope: It reflects charging activities involving 97 user IDs within a cooperative housing 1,113 apartments and 2,321 residents. 82 of these user IDs remained active throughout the data collection period.

License

Who Can Use It

• Researchers: To conduct studies on EV charging behaviour, grid impact, and energy demand forecasting.
• Data Analysts: For exploring real-world EV charging patterns, identifying correlations with weather and traffic, and developing predictive models.
• Energy Sector Professionals: To understand residential energy loads related to EVs and plan infrastructure.

Dataset Name Suggestions

• Norwegian Residential EV Charging Insights
• Trondheim Apartment EV Charging Data
• EV Charging and Urban Data for Norway
• Electric Vehicle Charging Behaviour (Norway)

Attributes