Understanding the impact of data gaps on long-term offshore wind resource estimates

Abstract. In the context of a wind farm project, the wind resource is assessed to predict the power output and the optimal positioning of the wind turbines. That requires taking wind measurements on the site of interest and extrapolating these to the long-term using so-called "measure, correlate, and predict" (MCP) methods. The failure of sensors, power supply, or software are common phenomena. These disruptions cause gaps in the measured data, which can be especially long in offshore measurement campaigns due to harsh weather conditions causing system failures and preventing servicing and redeployment. The present study investigates the effect of measurement data gaps on long-term offshore wind estimates by analyzing the bias they introduce in the parameters commonly used for wind resource assessment. Furthermore, it aims to show how filling the gaps can mitigate their effect. To achieve this, we perform the investigations for three offshore sites in Europe with 2 years of concurrent measurements. We use reanalysis data and various MCP methods to fill gaps in the measured data and extrapolate this data to the long term. The results of the investigations show that the effects of gaps on long-term extrapolations are lower than expected. For instance, gaps of 180 days cause an average deviation of the long-term mean wind speed of less than 0.04 ms-1 for all tested sites. Filling the gaps can slightly reduce their impact if the MCP method used for gap filling performs better for predicting known data than the MCP method used for long-term extrapolating.