End User
EMOT
Description
This AI-powered service forecasts electricity demand in energy communities with a strong focus on EV charging behavior. By integrating smart meter data, EV usage patterns, household profiles, and external variables like weather and tariffs, the service predicts aggregate and disaggregated demand across time horizons. The forecasts support smart scheduling of EV charging, grid impact mitigation, and alignment with local renewable generation. Designed for both operational and planning use, this tool enables energy communities to maximize self-consumption, reduce peak loads, and coordinate flexibility offerings from EV fleets.
Core Capabilities
Predictive & Prescriptive Analytics
Business Need
The rapid growth of EV adoption in residential settings introduces significant uncertainty into local energy demand profiles. Traditional forecasting methods fail to capture the dynamic and context-dependent nature of EV charging. This service addresses the need for accurate, EV-aware demand prediction to support infrastructure planning, demand-side management, and local flexibility market participation. It helps DSOs, aggregators, and community energy managers to reduce peak loads, align EV charging with renewable generation, and avoid transformer overloading or costly grid reinforcements.
Key Performance Indicators
RMSE and MAE of forecasts
Forecast latency
Uptime of service APIs
Data Provided
Smart meter and EVSE data (private, internal)
User demographic and behavioral data (anonymized, internal)
Weather forecast and traffic data (Open Meteo, public APIs)
TEF
TEF EV