Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts

TL;DR

This study examines how the temporal resolution of climate model wind data impacts the accuracy of long-term wind power forecasts, finding that three- to six-hourly data preserves distribution better than daily or monthly averages.

Contribution

It demonstrates that three- or six-hourly wind speed data are sufficient for multi-decadal wind power forecasting, guiding future climate model data usage.

Findings

Three- or six-hourly data preserves wind speed distribution.

Daily or monthly averages distort the distribution.

Lower temporal resolution data should be used cautiously.

Abstract

To forecast wind power generation in the scale of years to decades, outputs from climate models are often used. However, one major limitation of the data projected by these models is their coarse temporal resolution - usually not finer than three hours and sometimes as coarse as one month. Due to the non-linear relationship between wind speed and wind power, and the long forecast horizon considered, small changes in wind speed can result in big changes in projected wind power generation. Our study indicates that the distribution of observed 10min wind speed data is relatively well preserved using three- or six-hourly instantaneous values. In contrast, daily or monthly values, as well as any averages, including three-hourly averages, are almost never capable of preserving the distribution of the underlying higher resolution data. Assuming that climate models behave in a similar manner to observations, our results indicate that output at three-hourly or six-hourly temporal resolution is high enough for multi-decadal wind power generation forecasting. In contrast, wind speed projections of lower temporal resolution, or averages over any time range, should be handled with care.