End User
EMOT
Description
The EV-Driven Demand Forecasting service predicts both aggregate and disaggregated electricity demand in residential energy communities with significant EV adoption, explicitly accounting for EV charging behaviour, household consumption patterns, and external contextual factors. The service supports operational planning, demand response strategy design, and self-consumption optimisation for DSOs, aggregators, and community managers.
Core Capabilities
Predictive & Prescriptive Analytics
Business Need
As EV adoption increases in energy communities, conventional household demand patterns are significantly altered by charging sessions that introduce additional peaks, variability, and flexibility opportunities. DSOs, aggregators, and community managers need demand forecasting tools that explicitly distinguish between base household load and EV-driven demand to enable effective peak management, flexible demand coordination, and infrastructure planning.
Key Performance Indicators
MAE and RMSE for aggregate and disaggregated demand forecasts
Peak demand prediction accuracy
Self-consumption forecast accuracy
Skill score relative to persistence and seasonal average baselines
Data Provided
Time-indexed community demand forecasts with disaggregated components (base load, EV charging)
Peak demand predictions, self-consumption indicators, and uncertainty intervals
Scenario-based forecasts for alternative charging strategies or tariff structures
Inputs: 15-min smart meter data, EV charging session records, household attributes, weather forecasts, tariff structures
TEF
TEF EV