The interannual variations of installed capacity for offshore wind turbines in China : estimations derived solely from remote sensing

Abstract

Accurately and thoroughly determining the installed capacity of offshore wind turbines (OWTs) and offshore wind farms (OWFs) is crucial for evaluating offshore wind energy and guiding future development. However, existing statistical data only provide aggregated information on capacity, and detailed attribute data are not publicly available for free. Here, we present a novel pure remote sensing method to estimate the OWT installed capacity, successfully applied to estimate the installed capacity of OWTs in China from 2015 to 2022. This approach first used deep learning to identify turbine shadows from Sentinel-2 images, then estimated the hub height corresponding to the shadows by combining the solar elevation angle, and finally related the height to the capacity through a polynomial model. The results demonstrate that the pure remote sensing method exhibits excellent performance in estimating the installed capacity of OWTs. Comparing the generated single OWT capacity with the officially published results, the root mean square error (RMSE) is 0.27 MW (5.06%). From the end of 2015 to 2022, the total installed capacity of OWTs in China’s mainland increased from 1.06 GW to 30.18 GW, with the highest annual growth rate reaching 149.92%. These remote sensing-based estimates closely match the data documented in the existing reports (R2 = 0.99, RMSE = 0.62 GW). The average capacity per turbine increased from 4 MW to 4.84 MW, and the maximum capacity of OWFs increased from 632.89 MW in 2015 to 1305.04 MW in 2022 (geographically). By the end of 2022, OWFs with an installed capacity exceeding 100 MW accounted for 90.83% of the total number of OWFs in China’s mainland, indicating a trend toward larger-scale development of OWFs. This study provides a reference for large-scale assessments of OWT installed capacity. Additionally, it can be used for the construction of high-capacity OWFs to design future installations.