Journal Article

Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

Bloomfield, Hannah C., Charlton‐Perez, Andrew, Gonzalez, Paula L. M., Brayshaw, David J.

2021

Overview

Sub‐seasonal forecasts are becoming more widely used in the energy sector to inform high‐impact, weather‐dependent decisions. Using pattern‐based methods (such as weather regimes) is also becoming commonplace, although until now an assessment of how pattern‐based methods perform compared with gridded model output has not been completed. We compare four methods to predict weekly‐mean anomalies of electricity demand and demand‐net‐wind across 28 European countries. At short lead times (days 0–10) grid‐point forecasts have higher skill than pattern‐based methods across multiple metrics. However, at extended lead times (day 12+) pattern‐based methods can show greater skill than grid‐point forecasts. All methods have relatively low skill at weekly‐mean national impact forecasts beyond day 12, particularly for probabilistic skill metrics. We therefore develop a method of pattern‐based conditioning, which is able to provide windows of opportunity for prediction at extended lead times: when at least 50% of the ensemble members of a forecast agree on a specific pattern, skill increases significantly. The conditioning is valuable for users interested in particular thresholds for decision‐making, as it combines the dynamical robustness in the large‐scale flow conditions from the pattern‐based methods with local information present in the grid‐point forecasts. At short lead times (days 0–10) grid‐point forecasts have higher skill than pattern‐based methods (e.g., weather regimes or targeted circulation types) across multiple metrics. However, at extended lead times (day 12+) pattern‐based methods can show greater skill. All methods have relatively low skill beyond day 12. We therefore develop a method of pattern‐based conditioning, which is able to provide windows of opportunity for prediction: when >50% of the pattern forecasts are in agreement skill increases significantly. AI ha

• Publisher: John Wiley & Sons, Ltd,John Wiley & Sons, Inc,Wiley
• Subject: Anomalies / Comparative analysis / Conditioning / Decision making / demand / Electric power demand / Energy industry / Ensemble forecasting / forecasting / Methods / pattern forecast / power system / Seasonal forecasting / sub‐seasonal / Weather / weather regimes / Weekly / wind power