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A wind turbine (WT) site optimization procedure was developed and applied on two different onshore and one offshore sites, supplied with tall met masts and belonging to exemplary wind climates. In addition to detecting the most suitable WT for each site, Kohonen's self-organizing maps (SOMs) were employed to improve investigation of those parameters mostly influencing site optimization. Three years (2013-2015) of 1-h vertical observations from local met masts and a database of 377 onshore and 23 offshore commercial WTs were used. As a result, maximizing capacity factor (CF) was confirmed as a good objective function, though not the best, which was minimizing levelized cost of energy (LCoE). In general, these two conditions do not necessarily match: variously setting WT parameters may either result in an LCoE reduction or CF increase, but both conditions do not occur concurrently. A key finding was that minimum LCoE cannot be achieved by indefinitely increasing the WT hub height, but rather through detection of an optimum value obtained as a unique solution of the optimization procedure. Furthermore, the capability of SOM to recognise the cluster structure of all parameters influencing WT site optimization shed further light on their mutual relationship, thus proving to be an ideal tool to address the non-convex nature of this issue.

A wind turbine (WT) site optimization procedure was developed and applied on two different onshore and one offshore sites, supplied with tall met masts and belonging to exemplary wind climates. In addition to detecting the most suitable WT for each site, Kohonen's self-organizing maps (SOMs) were employed to improve investigation of those parameters mostly influencing site optimization. Three years (2013-2015) of 1-h vertical observations from local met masts and a database of 377 onshore and 23 offshore commercial WTs were used. As a result, maximizing capacity factor (CF) was confirmed as a good objective function, though not the best, which was minimizing levelized cost of energy (LCoE). In general, these two conditions do not necessarily match: variously setting WT parameters may either result in an LCoE reduction or CF increase, but both conditions do not occur concurrently. A key finding was that minimum LCoE cannot be achieved by indefinitely increasing the WT hub height, but rather through detection of an optimum value obtained as a unique solution of the optimization procedure. Furthermore, the capability of SOM to recognise the cluster structure of all parameters influencing WT site optimization shed further light on their mutual relationship, thus proving to be an ideal tool to address the non-convex nature of this issue.

The reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.

The reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.