Forecast-driven trading: using weather, outages and flow data to time trades

This blog describes the main types of forecasts, how to interpret forecast deltas, and how to convert probabilistic insights into spot or forward trades while avoiding common pitfalls. Using structured forecasting helps traders systematically capture volatility and find opportunities that are invisible to those relying solely on historical prices.

Forecasting as the core edge in power trading

Power prices are mainly influenced by three factors: weather, constraints, and fuel costs. Weather affects supply and demand, constraints determine market availability, and fuel prices establish the marginal cost of generation. Each of these elements can change rapidly, and markets often include a consensus forecast in prices before the actual event.

Forecasting is most effective when traders concentrate on shifts in expected conditions rather than fixed values. The market often incorporates a “consensus view” into day-ahead and forward markets, meaning only deviations from the anticipated trajectory present profitable opportunities. Speed is crucial: a forecast delta that reaches the market faster than the market can react can be monetised. Traders keep an eye on the timing of weather model updates, grid notifications, and interconnector adjustments to capitalise on early movements.

Forecast-driven trading also enables more accurate risk management, as positions can be scaled according to forecast uncertainty. Instead of making blunt directional bets, traders can adjust exposure based on confidence bands or scenario probabilities.

How weather drives price formation

Weather forecasts are fundamental to probabilistic power trading, with several key factors directly impacting prices:

• Wind and solar output: fluctuations in wind and solar forecasts influence the merit order, changing which generators are marginal. For instance, an upward revision in wind output can suppress prices during periods of high output, while unexpected drops in solar production during midday hours may cause short-term price spikes.

• Temperature: cold snaps or heatwaves directly influence demand. Sharp temperature changes can trigger price spikes, especially when combined with low renewable output, impacting both spot and short-term forward positions.

• Hydro inflows: in regions like the Nordics and the Alps, water inflows influence the flexibility of hydro plants. Sudden increases or decreases impact both spot and forward prices, and careful monitoring of precipitation and snowmelt forecasts is essential.

Traders often combine multiple models to produce ensemble forecasts, weighting them based on past reliability and recent performance. This method helps measure forecast uncertainty, which is as crucial as the central estimate itself. Probabilistic approaches enable desks to assess a range of outcomes rather than rely on a single-point forecast.

Outage and REMIT signals that traders rely on

Outages affect the supply hierarchy and create immediate trading opportunities. Regulation on Wholesale Energy Market Integrity and Transparency (REMIT) events, such as generator maintenance or unexpected outages, requires public reporting, and traders monitor these events closely.

Key considerations include:

• Supply shifts: outages reduce available capacity, potentially creating scarcity during high-demand periods.

• Timing: REMIT notifications influence market prices before the physical event occurs. Being first to act is critical.

• Silent outages: sometimes, plants are derated without formal announcements. Traders infer these events from dispatch patterns or interconnector adjustments.

• Cascading effects: outages in one region can influence flows and prices in neighbouring markets, especially in interconnected European hubs.

Accurate outage interpretation involves cross-checking various sources and understanding local dispatch rules. The aim is to forecast the price impact before it affects the broader market.

Flow and congestion forecasting

Interconnector flows and congestion shape regional price separation. Traders monitor:

• Interconnector constraints: limitations on cross-border transmission can prevent supply from reaching high-demand regions, driving local price spikes.

• Flow-based coupling: in markets such as Central Western Europe (CWE) defines how interconnectors are utilised and how prices align.

• Dominant flows: occasionally, interconnector flows override local fundamentals, meaning regional weather or demand signals may be secondary.

• Real-time updates: continuous monitoring of interconnector utilisation and congestion forecasts is essential for intraday or short-term trading strategies.

Predicting these patterns involves analysing historical data, monitoring the grid in real-time, and using probabilistic models. Traders need to determine when to rely on flow expectations and when to focus on local supply-demand signals to optimise their positions.

Turning forecast uncertainty into positions

Forecasting does not eliminate uncertainty; it measures it. Traders utilise this insight to structure their positions.

• Ensembles and confidence bands: multiple model runs create a range of outcomes. The width of the range informs position sizing and risk allocation.

• Trading volatility: instead of betting on a single point forecast, traders capitalise on potential price swings by using delta-based strategies, which respond to forecast changes rather than absolute values.

• Backtesting delta signals: historical analysis shows how forecast revisions impacted prices, helping calibrate trading rules and risk limits.

• Scenario weighting: assigning probabilities to alternative outcomes allows traders to capture expected value in uncertain conditions.

By concentrating on changes in expected conditions, the deltas, instead of static forecasts, traders turn uncertainty into actionable trades while managing downside risk and optimising capital use.

Conclusion

Forecast-driven trading uses information as a competitive advantage. Weather models, REMIT outage data, demand forecasts, and interconnector flow predictions give a probabilistic outlook of the market. Traders who interpret these signals accurately can predict price spikes, optimise intraday and forward positions, and safeguard portfolios from adverse events.

The lesson is clear: forecasting doesn’t eliminate uncertainty, it monetises it. Traders who combine speed, structured analysis, delta-based thinking and scenario planning are best positioned to capture value in increasingly volatile power markets.