End User
EMOT
Description
This AI service provides high-resolution short- and medium-term forecasts of wind power generation within EV-integrated energy communities. By combining localized wind turbine data, meteorological inputs, and grid interaction models, it enables the coordinated use of wind energy for EV charging, battery storage, and household consumption. Forecasts are used to align flexible EV charging with expected wind generation, minimizing grid imports and enhancing system-level efficiency. The service supports community-level energy balancing, demand-shaping, and optimal use of distributed renewable resources under variable wind conditions.
Core Capabilities
Predictive & Prescriptive Analytics
Business Need
As energy communities adopt local wind power and electric vehicles simultaneously, their ability to balance supply and demand becomes increasingly complex. Wind generation’s variability can lead to underutilized clean energy or grid instability if not properly forecasted. This service meets the growing need for accurate, EV-aware wind forecasting that enables predictive scheduling of EV charging and storage. It supports cost savings, carbon reduction, and improved grid interaction, empowering communities to operate with greater autonomy and flexibility while maximizing their renewable energy usage.
Key Performance Indicators
RMSE, MAE, and MAPE over different forecast horizons
Uptime and latency of forecast APIs
Forecast bias and coverage rate
Data Provided
SCADA data from wind turbines (private, internal)
Weather forecast models (e.g. ECMWF, Open Meteo – public/open)
Site metadata (turbine specs, location, altitude – internal)
TEF
TEF EV