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As ever more offshore sites are being investigated for the installation of wind farms, there is a need for accurate estimates of the long‐term mean wind speeds at these sites. The cost of installing masts at offshore sites is high compared with onshore sites. In the short term this cost may be difficult to avoid. However, if a developer could get an estimate of the expected long‐term mean wind speed at a potential offshore site using available onshore data sets, this could at least inform the choice of site for more advanced monitoring. With this in mind we present the results of using a number of simple standard analyses to infer the wind speed at three UK offshore masts and a mast and lighthouse off the coast of Ireland. Onshore surface wind measurements, upper air measurements, numerical weather prediction model output pressure data on a regular grid, wind speed output from two numerical weather prediction models and reanalysis data are transformed to the sites of interest using relatively simple methods neglecting the effect of topography or a roughness change‐induced internal boundary layer and assuming neutral atmospheric stability. The predicted wind speeds are compared with actual measurements at the offshore masts and any discrepancies are assessed and discussed. Copyright © 2005 John Wiley & Sons, Ltd.

Wind integration studies often focus on the capacity value of wind power without considering Unit Commitment and Economic Dispatch or resolving requirements for ancillary services. Here, a novel method for simulating wind power time series with sufficient temporal span to support capacity studies and temporal resolution to support UCED studies is developed. Wind speed time series (WSTS), with 6 h temporal and 0.7 × 0.7 degree spatial resolutions, are extracted from the ECMWF-interim reanalysis, interpolated, scaled, and imputed so that they are representative of a point wind speed measurement with a 5 min temporal resolution. Imputation is made using a wavelet multi-resolution analysis approach that ensures temporally consistent correlations while accounting for heteroskedasticity. WSTS are transformed to power using wind power plant power curves, lowpass filters, and a Markov Chain model of operational efficiencies. The wind power model is validated using a set of measurements made at wind power plants (WPPs) in New Zealand and used to simulate power time series for 2 GW portfolios of WPPs representing compact, disperse, diverse, and business-as-usual portfolios. Metrics for dependability, variability, and predictability are applied to quantify the benefits of spatial diversification.

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