Our short-term forecasts for wind, solar, demand, and prices rely on two external weather data providers:

Both providers run four forecasts per day — 00, 06, 12, and 18 — across two model types :

For more details on our raw weather data coverage, see here

Our price forecasting combines machine learning and time series analysis, with a strong foundation in fundamental market drivers such as production, consumption, fuel prices, and interconnector capacity.

To ensure transparency and avoid the “black box effect”, our models operate in two sequential steps:

We first convert raw weather data (temperature, wind speed, solar radiation, cloud cover, etc.) into forecasts of demand, wind production, solar production

This stage captures the physical supply-demand fundamentals that will later influence price formation.

The outputs in MW from the fundamental models are then fed into our machine learning-based price models to forecast hourly day-ahead electricity prices (€ / MWh). This approach combines fundamental analysis with historical market behavior, effectively capturing the complex interplay of physical system dynamics and structural price signals by combining several key parameters, such as:

From the changes of these different variables, our model is able to predict for each of the future hours a balance between supply and demand on the merit order for the wholesale market and the bid curves for the reserve prices as it has been studied historically.

We use a single coupled machine learning model that predicts all 40+ interconnected bidding-zone prices simultaneously - this structure helps ensure greater coherence across the European day-ahead market. Our model is retrained daily, using an expanding historical window from 2015 to the most recent actuals.

Once finalized, forecast files are not modified further and are made available via FTP and API access.

We ingests weather runs from ECMWF and NOAA as soon as files are released.

Delivery times vary by model and run. The times below represent the median, 90th percentile (p90), and 95th percentile (p95) of the availability of the full forecast (i.e. when the last forecasted hour becomes available).

Notes:

API

FTP

For FTP access, connect to:
​Host: calc1.cor-e.fr
​Port (FTP): 21
​Port (SFTP): 2222

Directory paths :

File naming conventions :

For Ensemble forecasts, two files are published for each run: