Weather, renewables and volatility: forecasting for profit

Short-term traders, data scientists, and portfolio analysts are often most interested in the best methods for forecasting profits. This blog takes a look at some techniques that can assist with profit forecasting, including bridge meteorology, analytics, and trading execution, in practical terms.

Why weather forecasting drives trading success

Weather forecasting tracks an uncontrollable force, the weather, and so the unpredictability of this type of forecasting creates both risk and opportunity for traders.

Correlation between weather and intraday volatility

Some research shows there is a link between weather and intraday volatility. When the weather type associated with certain types of fuel generation (for example, sunlight) isn’t present, trading can become sluggish. On cloudy days, the intraday market can be more volatile in the solar sector. 

Impact of forecast error on imbalance costs

Errors in weather forecasting can cause energy imbalance costs to spike because the amount of power generated can not be accurately predicted. This means the supply may not be able to keep up with demand because the amount generated is less than the forecast. Improving weather forecasting accuracy can therefore make energy trading more profitable.

Data inputs for generation and price models

We can use weather data to help predict the output of renewable plants to fulfil expected energy demand, but which data is most useful, and how should it be utilised? 

Meteorological data sources 

There are various meteorological data sources traders can consider, with regional options depending on where they operate. UK traders can benefit from a UK-specific source, the Met Office, which provides regional weather data.  Icosahedral Nonhydrostatic (ICON) is most used by German traders but provides a global overview. Considered one of the most accurate meteorological data sources, energy companies use the European Centre for Medium-Range Weather Forecasts (ECMWF), but its data is open to the public. 

Historical vs. real-time datasets and model blending

Utilising different types of data can give traders the benefit of hindsight and also immediate feedback on how the weather is behaving currently. Historical data provides tried-and-tested analysis with evidence of past outcomes. Real-time data, on the other hand, provides a current lay of the land and allows dynamic trading to take advantage of last-minute profits.

Managing uncertainty through probabilistic forecasting

Probabilistic forecasts provide a range of hypothetical outcomes for weather forecasting, helping manage trading uncertainty. 

Ensemble forecasts, confidence intervals, and scenario planning

Various types of probabilistic forecasting exist, with some more suitable for predicting the volatility of renewable energy. Ensemble forecasts run continuous prediction models to help determine the impact of various weather scenarios on energy production. Confidence intervals, on the other hand, use a specific range of uncertainty around an expected point, while scenario planning develops specific, realistic scenarios that might occur in energy production and weather forecasting, including best- and worst-case scenarios. 

Quantifying error margins and risk thresholds

One step beyond modelling best- and worst-case scenarios is to factor in specific error margins. This helps position the expected energy generated not only by considering fluctuations in weather behaviour, but also by accounting for forecast error. Setting risk thresholds can turn these levels of risk into potential decisions to be made, factoring in expected loss, failed alarm systems, or warnings during extreme weather events, or setting thresholds for taking action.

Translating forecasts into trading signals

Experienced traders can turn signals into insights: the key is understanding how and why the market moves. 

Linking forecast deltas to price movements

The reference to ‘delta’ applies to ‘surprise’ and applies to the difference between a market forecast and the actual data released. Price movement occurs when the market forecast differs from the expected amount of energy generated. 

Automation and model-driven bidding

This method of trading leverages artificial intelligence (AI) by sending trading signals to a trading bot to automate bidding and execution. The bot places orders more quickly and more accurately than its human counterparts. 

Backtesting and performance metrics

Forecasts must be transformed into clear trading signals, but to get to this point, strategies should be backtested to see how a trading decision might have performed if executed historically, using historical data. Entry and exit prices should be considered, and the result analysed and adjusted before making a concrete decision. 

Building a data-driven forecasting culture

For grid stability in a renewable-dominated future, incorporating data into the sector is crucial. We predict a period of continuous learning from forecasting vs. actual analysis, with machine learning integrated across all levels. It’s key that meteorologists are integrated with traders to leverage the trader’s knowledge of the market and the meteorologist's knowledge and specialist weather data. 

In the future, the most profitable trading desks will turn uncertainty into an asset by treating forecasting as a competitive advantage.