Accurate forecasting of weather variables -including humidity, temperature, dew point, cloud cover, and wind speed and direction- is critical for improving predictions of both renewable energy generation and electricity demand, and for managing emerging challenges associated with the rapid growth of data centers energy needs. In this project, we analyze an extensive dataset of weather forecasts and observed conditions to systematically characterize forecast errors across geographic locations, forecast lead times, hours of the day, and seasons. This characterization enables the generation of realistic weather scenarios to support more robust scheduling and operational decision-making in electric power systems.
Project Lead: Dalia Patino-Echeverri
Project Manager: TBD
