Uncertainty Flagging Power Forecast
For the power market, the most challenging parameters to predict accurately are wind speed and solar radiation. For instance, the variability of cloud formations on a sunny day leads to rapid local changes that an NWP (Numerical Weather Prediction) cannot perfectly predict.
For wind power production, many are familiar with the power curve and the NMAE (normalized mean absolute error) in weather models (NWP) of about 1.5 m/s in Northern Europe for the next day. A small error in the forecast on the steep part of the curve can easily lead to a 30% error in energy forecasts.
To address these challenges, we combine different weather models and use the best ones available for day-ahead and intraday markets, employing advanced machine learning and AI techniques. It is also well known that the best NWP model is the ECMWF, on which the energy market closely relies. Any errors or large changes in this model lead to high volatility, affecting both the physical and financial energy markets.
At ConWX, we use all the information available from the NWPs to assist our clients in a very volatile power market. We do this with the Uncertainty Flagging Power Forecast, highlighting the hours with a high risk of volatility and the alternative direction of production.
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We can never achieve 100% accuracy in forecasting, but by working to highlight when risks are present, our users can effectively navigate the volatile power market. For our clients, who require minute-based forecasts, we utilize measurements from around the farm and identify periods of extremely low predictability in various weather regimes.
At ConWX, we use multiple numerical weather predictions (NWP) data to calculate the optimal power forecasting for wind and solar. This is the industry standard, but our analysis also shows a very high value in choosing the right mix and having unique NWP input for power forecasting.
