Methodology

How the Forecast Works

The forecasts on this site are generated using machine learning models trained on historical electricity market and weather data.

The forecasting process consists of two stages. First, separate models are used to forecast key market fundamentals such as wind generation, solar generation, and electricity demand. These forecasts are then used as inputs to a downstream price model that estimates hourly day-ahead electricity prices for each bidding zone.

The price model is based on gradient-boosted decision trees and evaluates each forecast hour independently, allowing it to adapt to changing market conditions across the forecast horizon.

Historical training data currently extends back to 2023. Weather inputs are based on ECMWF forecasts and historical weather observations, combined with market data from ENTSO-E and other publicly available sources.

Key Inputs

The price model uses a combination of forecast fundamentals, market data, and calendar information, including:

• Forecast electricity demand
• Forecast wind and solar generation
• Regional renewable generation conditions in neighboring countries
• Nuclear generation where applicable
• Hydrology indicators for Finland, Norway, and Sweden
• Calendar effects such as season, weekday, hour, and public holidays
• Recent market conditions, including historical price patterns

What the Model Learns

The model identifies patterns that have historically influenced electricity prices.

For example, strong wind generation, high solar output, or mild temperatures are often associated with lower prices. Conversely, colder weather, lower renewable generation, tighter hydro conditions, and stronger electricity demand have historically tended to increase prices.

Rather than attempting to explicitly simulate the physical operation of the power system, the model learns these relationships directly from historical data.

Geographic Coverage

Forecasts are currently available for:

• 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

Like any forecasting approach, the model has limitations.

The forecasts are based on publicly available data and therefore do not explicitly incorporate every factor that can influence electricity prices. Examples include fuel market developments, emissions prices, market participant behavior, power plant outages, and cross-border transmission availability and flows.

In addition, the forecasting approach is based on machine learning rather than detailed market optimization. While machine learning can capture complex relationships within historical data, it does not explicitly model generation dispatch, transmission constraints, bidding behavior, or other physical and economic mechanisms that influence market outcomes.

Extreme price spikes, negative-price events, and unusual market situations can therefore be difficult to predict reliably, particularly when similar events are rare in the historical training data.

The model includes hydrology-related features for Finland, Norway, and Sweden, but it does not explicitly model hydro reservoir operations or hydro dispatch decisions.

Forecasts should be treated as data-driven estimates rather than predictions of future outcomes.