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To forecast wind power generation in the scale of years to decades, outputsfrom climate models are often used. However, one major limitation of the dataprojected by these models is their coarse temporal resolution - usually notfiner than three hours and sometimes as coarse as one month. Due to thenon-linear relationship between wind speed and wind power, and the longforecast horizon considered, small changes in wind speed can result in bigchanges in projected wind power generation. Our study indicates that thedistribution of observed 10min wind speed data is relatively well preservedusing three- or six-hourly instantaneous values. In contrast, daily or monthlyvalues, as well as any averages, including three-hourly averages, are almostnever capable of preserving the distribution of the underlying higherresolution data. Assuming that climate models behave in a similar manner toobservations, our results indicate that output at three-hourly or six-hourlytemporal resolution is high enough for multi-decadal wind power generationforecasting. In contrast, wind speed projections of lower temporal resolution,or averages over any time range, should be handled with care.

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