Abstract
AbstractSub‐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.
Highlights
As power systems across the world transition towards low carbon electricity generation, they are becoming increasingly dependent on weather
More skill is seen in the grid-point forecasts in Northern and Eastern Europe, this could be due to the weaker relationship between temperature and demand in winter for the Southern European countries, or due to the generally lower amount of installed wind capacity compared with Central and Northern Europe
Focusing first on week 0, we see that the gridpoint forecast method clearly outperforms the two pattern-based methods, using weather regimes (WRs) and targeted circulation type (TCT)
Summary
As power systems across the world transition towards low carbon electricity generation, they are becoming increasingly dependent on weather. High quality weather forecasts are becoming increasingly important for decision-making days to weeks ahead to maintain a secure and reliable energy system. Examples of these decisions include: managing reserve generation margins, maintenance scheduling, hydropower scheduling, anticipating winter heating demand requirements and cooling water requirements for conventional generation (White et al, 2017). Each of these decisions rely on accurate forecasts of demand, wind power, solar power or hydro generation, and the consequences of misestimation are exacerbated in periods of unusually high. This can lead to large cost savings and enhanced profits for energy companies
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