Abstract
Abstract. Like almost all measurement datasets, wind energy siting data are subject to data gaps that can for instance originate from a failure of the measurement devices or data loggers. This is in particular true for offshore wind energy sites where the harsh climate can restrict the accessibility of the measurement platform, which can also lead to much longer gaps than onshore. In this study, we investigate the impact of data gaps, in terms of a bias in the estimation of siting parameters and its mitigation by correlation and filling with mesoscale model data. Investigations are performed for three offshore sites in Europe, considering 2 years of parallel measurement data at the sites, and based on typical wind energy siting statistics. We find a mitigation of the data gaps' impact, i.e. a reduction of the observed biases, by a factor of 10 on mean wind speed, direction and Weibull scale parameter and a factor of 3 on Weibull shape parameter. With increasing gap length, the gaps' impact increases linearly for the overall measurement period while this behaviour is more complex when investigated in terms of seasons. This considerable reduction of the impact of the gaps found for the statistics of the measurement time series almost vanishes when considering long-term corrected data, for which we refer to 30 years of reanalysis data.
Highlights
A wind resource assessment is performed at the beginning of every wind energy project
As the aim of this study is to investigate the impact of gaps on offshore wind-energy-relevant wind statistics, a reference time series with a low amount of missing data was needed
Our study proposes a methodology that allows us to quantify the impact of data gaps in time series on wind statistics
Summary
A wind resource assessment is performed at the beginning of every wind energy project. During the lifetime of a wind farm, reassessments are typically done that can be based on wind turbine or further wind measurement data Based on this estimate, an expected energy yield is derived which serves as a basis for any economic considerations of the project. Uncertainties and a possible bias in the wind resource estimate propagate up to the financing of a wind project with the percentage uncertainty value increasing from uncertainty in wind speed to uncertainty in wind farm production to uncertainty in the expected return on investment. To reduce these uncertainties, starting from the wind measurements is of high interest and relevance
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