Hourly Price Calendar

Wind Power Outlook

Solar Power Outlook

Consumption Outlook

Residual Load Outlook

Residual Load = Load - Wind - Solar

What's Driving Prices

The strongest signals for the day-ahead forecast and how they relate to price. Factors are ranked by their historical price correlation weighted by how far they currently deviate from the seasonal norm.

How the Forecast Works

The forecasts on this site are generated with an XGBoost-based machine learning pricing model (gradient-boosted decision trees) that estimates hourly electricity prices from weather, generation, demand, and market features. Each hour is evaluated independently based on its expected conditions, which lets the model react to changes in wind, imports, generation availability, solar output, and seasonal demand.

The price model is trained on historical data starting from 2023. Weather data is based on ECMWF, using historical weather together with forward forecasts for each area. The site first produces separate wind, solar, and load forecasts, which are then fed into the downstream price model.

Key Inputs Considered by the Model

• Area-specific weather history and forecasts based on ECMWF
• Regional wind context from neighboring countries
• Forecast load / consumption for the price area
• Renewable generation availability, including wind and solar, plus nuclear where applicable
• Cross-border transfer capacities for the forecast area
• Hydrology state features for Finland and Norway
• Calendar effects such as year, weekday, hour, and holidays, plus prior-week price context

Wind Forecasting

Recent ENTSO-E actuals are used to anchor the wind picture. The raw wind-power model is built from area-specific weather features, historical production, and installed capacity. Finland remains a special case on the power side: Fingrid real-time data and Fingrid's operational wind-power forecast are used directly for the nearest forecast window where available, with model inference filling remaining gaps beyond that.

Load Forecasting

Recent ENTSO-E load actuals are used first whenever they are available. For Finland, Fingrid's operational consumption forecast is used directly where applicable. For other areas, the nearest future window uses the ENTSO-E day-ahead load forecast. Beyond that, a local XGBoost load model extends the horizon using calendar, weather, holiday, and recent load-history features.

Model Logic and Interpretation

The model learns statistical relationships between explanatory variables and price outcomes. High wind or solar, mild temperatures, and stronger import capacity are often associated with lower prices, while low renewable output, cold weather, tighter hydro conditions, stronger demand, or reduced import capacity tend to push prices higher.

Geographic Scope and Development

The current implementation covers:

• Austria (AT)
• Belgium (BE)
• Czechia (CZ)
• Denmark (DK1, DK2)
• Estonia (EE)
• Finland (FI)
• France (FR)
• Germany (DE)
• Netherlands (NL)
• Norway (NO1–NO5)
• Poland (PL)
• Sweden (SE1–SE4)

Limitations of the Forecast

The model only considers variables included in its training data. It does not directly account for unexpected geopolitical events or rare structural shocks with limited historical precedent. Extreme price events, both spikes and negative prices, are also difficult for the model to predict reliably due to their limited historical occurrence and representation in the training data.

The model includes hydrology-state features for Finland and Norway, but it does not explicitly model hydro dispatch decisions or full hydro-system dynamics. Swedish hydrology is not currently included.

Treat forecasts as data-driven estimates, not future outcomes.