Precision matters: How day-ahead forecasts shape energy trading

The energy sector is in constant flux, driven by market liberalization and the rapid expansion of intermittent renewable sources. For energy traders, this volatility presents both a challenge and an opportunity. Their success hinges on making the right bets on supply and demand a full day in advance. But in a market where a single unexpected cloud or a sudden shift in wind speed can erase profits, a generic forecast isn't enough. The future of energy trading is being defined by a new standard: the high-precision day-ahead forecast.

The problem with uncertainty

Weather is one of the key variables in the renewable energy equation. For a wind farm, the strength and direction of the wind directly determine its output. For a solar park, it's the amount of sunlight. On the demand side, temperature extremes drive consumption. A cold front arriving earlier than expected can cause a spike in heating demand, while a heatwave sends air conditioner use soaring.

These weather-driven fluctuations create significant imbalances between energy supply and demand. In a day-ahead market, where commitments are made a day before delivery, a mismatch between a trader's forecast and the actual outcome can be very costly. If a company overestimates wind production, it might have to buy expensive electricity on the intra-day or balancing markets to cover the shortfall. Conversely, underestimating production means a missed opportunity to sell power at a favorable price.

This is where coarse resolution meteorological forecasts fall short. They provide a general outlook, but they lack the granular detail—the high spatial and temporal resolution—needed for a competitive edge. And the result often leads to significant forecast errors and market inefficiencies.

From general to local: The evolution of forecasting

The transition from broad to precise forecasting is powered by advanced numerical weather prediction (NWP) models. Specifically, the category of models that are able to resolve the atmosphere at a resolution of 1 km or less are called convection-permitting models and include mesoscale and hectometric models. They run on supercomputers and generate localized forecasts to predict conditions for a single wind farm or solar park at an hourly or even sub-hourly level.

This level of detail moves the industry away from simple predictions and toward a more deterministic view of future conditions. For a trader, this means knowing not just that it will be windy, but that a specific wind farm will likely produce e.g. 200 megawatts of power at 3:00 p.m. the next day. This precision allows for more informed and confident trading decisions.

Impact on day-ahead trading

The effects of these high-precision forecasts are immediately apparent in the day-ahead markets.

• Reducing imbalance costs: Accurate forecasts allow traders to submit more realistic and reliable bids. By having a clear picture of future renewable generation, they can minimize the difference between their committed supply and actual output, directly reducing or even eliminating the need to buy costly balancing power. This is a direct saving that improves profitability.
• Optimizing bidding strategies: With a higher degree of certainty about future supply, traders can develop sophisticated bidding strategies. They can offer power at a price that is competitive but also profitable. For example, if a forecast predicts high wind generation and, therefore, a potential oversupply, a trader can adjust their bid to secure a sale rather than holding out for a higher price that the market won't support.
• Improving grid stability: It isn't just traders who benefit. Grid operators, who are responsible for maintaining a stable balance between supply and demand, can use these same high-resolution forecasts. By knowing what to expect from renewable sources, they can make more informed decisions about dispatching conventional power plants and managing the overall flow of electricity. This reduces the risk of grid congestion or even blackouts.
• Enhancing market transparency: As more market participants adopt these advanced forecasting methods, the market as a whole becomes more efficient. Prices become a better reflection of the true supply and demand dynamics, reducing speculation and promoting a fairer, more transparent trading environment.

The path forward

While the benefits are clear, the challenge of a a "perfect" forecasting remains. Hectometric and mesoscale models have limitations and are still actively developed. Some recent advancements in weather forecasting research focus on combining machine learning with NWP models to improve forecast accuracy. This hybrid approach leverages AI/ML to analyze historical data, identify patterns in forecast errors, and continuously refine the predictive models. This creates a system that not only predicts the weather but also learns to predict its own prediction errors. As the share of renewable energy continues to grow, this level of precision will be crucial, ensuring that the energy grid remains stable and markets stay efficient in a world increasingly driven by the weather.