End User
EMOT
Description
The EV-User Charging and Usage Profiles Prediction service estimates when EV users are likely to charge, where charging activity is expected to occur, and how much energy is likely to be consumed during future sessions. By incorporating behavioural, spatial, and contextual factors, the service provides actionable forecasts that support infrastructure planning, congestion mitigation, smart charging strategy design, and flexible demand coordination.
Core Capabilities
Predictive & Prescriptive Analytics
Business Need
EV charging patterns are highly heterogeneous and depend on behavioural, spatial, and contextual factors including preferred charging locations, mobility habits, weather, traffic, and local events. DSOs and aggregators need forward-looking charging demand information to anticipate peaks, design tariff incentives, schedule maintenance, and manage network constraints. The service transforms historical charging data into actionable forecasts for both day-to-day operations and medium-term planning.
Key Performance Indicators
MAE and RMSE between forecasted and actual charging demand
Charging event detection accuracy
Behavioural segment characterisation quality
API response time and operational uptime
Data Provided
Time-indexed charging profile forecasts at requested spatial and temporal granularity
Charging event probability estimates and behavioural segmentation insights
Forecast metadata: issue time, horizon, model version, input data sources
Inputs: historical charging session records, anonymised behavioural profiles, weather, traffic, calendar events
TEF
TEF EV