Day-Ahead Forecast

The forecast must be submitted to the market operator before a strict gate closure time, typically between 10:00 AM and 12:00 PM local time on the day prior to delivery. This deadline allows the system operator to run the Security-Constrained Unit Commitment (SCUC) algorithm. Late submissions are rejected, resulting in financial penalties or reliance on expensive balancing market purchases. The forecast horizon spans the 24 hours of the following calendar day, from midnight to midnight.

Unlike intraday forecasts that operate at 5-15 minute resolutions, day-ahead forecasts produce hourly average power values (MWh/h or MW). Each of the 24 hourly blocks represents the mean expected generation for that period. This resolution aligns with the European Power Exchange (EPEX SPOT) and other major market clearing engines. The hourly granularity smooths transient ramp events, requiring separate ramp rate forecasting for intraday adjustments.

Traditional day-ahead forecasts provide a single deterministic point estimate for each hour, representing the expected value or median generation. Advanced market participants increasingly demand probabilistic forecasts that output quantiles (e.g., P10, P50, P90) for each hour. These distributions enable risk-constrained bidding strategies, where a conservative bid might use the P10 value to minimize imbalance penalties, while an aggressive bid might target the P50.

Day-ahead forecasts are fundamentally driven by Numerical Weather Prediction (NWP) models, as sky-camera and satellite-based Cloud Motion Vector (CMV) techniques lose skill beyond 6 hours. Key global models include the ECMWF Integrated Forecasting System (IFS) and NOAA's Global Forecast System (GFS). The forecast accuracy is highly sensitive to the NWP initialization time; a model run at 00 UTC provides a different trajectory than one at 06 UTC, necessitating multi-model ensemble blending to reduce systematic bias.

The day-ahead forecast forms the basis of a contractual nomination to sell energy. Deviations between the forecasted schedule and actual metered generation are settled in the real-time balancing market at penalty prices. A systematic over-forecast bias leads to purchasing expensive deficit energy, while under-forecasting results in selling surplus at lower imbalance prices. The Forecast Skill Score relative to a persistence baseline directly correlates with the portfolio's profit-and-loss statement.

Forecasts are often generated for individual Connection Points (CPs) but submitted as an aggregated portfolio schedule. The portfolio effect describes the statistical smoothing of forecast errors when summing predictions across a geographically dispersed fleet of wind farms or solar parks. A 100 MW error at a single site might reduce to a 40 MW error when aggregated over a 500 km region, as local weather errors decorrelate. This spatial diversification is a critical risk mitigation strategy.