## 5 Conclusions
The cause of this difference can be explained by analyzing the gradient and shape of the vertical wind speed profile, how high the turbulence intensity is, and how the temperature or heat flux distribution differs from other regions. Wind shear, turbulence intensity, and Richardson number, which are the most commonly used atmospheric stability classification methods, were used because wind resource analysis based on local atmospheric stability is essential to increase the accuracy of power output prediction. In the vicinity of the Anholt offshore wind farm, unstable atmospheric conditions (59.13%) were dominant rather than stable (18.54%) in the wind shear-based atmospheric stability classification. This means that the wind speed difference between the upper and lower layers is small, and it also means that a lot of mixing effects are taking place in the vertical direction. As a result of the classification of atmospheric stability according to the turbulence intensity, a stable atmosphere was dominant (61.46%), and it is judged that this area does not generate much turbulence. In the results of atmospheric stability classification by Richardson number, the unstable atmosphere was dominant (42.71%), while the strongly stable atmosphere was also found in a significant proportion (39.07%). As a result of comparing the error rate between the actual and the simulated power output (REWS, HHWS) according to the atmospheric stability classification criteria, the error rate was large when the wind shear-based atmospheric stability classification was very stable (22.59%, 28.35%). This means that when the wind speed difference between the upper and lower layers is large and strong wind shear occurs in the vertical direction, the error rate is large. The error rates were large (10.25%, 12.46%) even when the atmosphere was very unstable. However, in both cases, the prediction accuracy of REWS-based simulation was improved. In the results of classification of atmospheric stability according to the turbulence intensity, the REWS showed that the error rate was low when the atmosphere was strongly unstable (79.79%) or unstable (37.80%), but the error rate was basically very large. Finally, as a result of analyzing the error rate for each atmospheric stability based on the Richardson number, the REWS consistently improved only when the atmosphere was strongly unstable (5.47%). Through the results of this study, REWS, which is judged to be theoretically superior, showed better prediction accuracy than HHWS. However, this was not always the case, probably due to differences in atmospheric stability distribution based on local atmospheric boundary layer characteristics. In other words, when a wind farm project is to be carried out, it should be possible to determine which method, REWS or HHWS, can produce relatively more accurate power output based on the results of analysis of nearby atmospheric stability characteristics. Occasionally, the expected power output was small and the business license should be sold because it was thought that the project feasibility was insufficient, but the actual power output was higher. Conversely, the project was carried out because the expected power output was high, but the actual power output may be much lower. In order to reduce this severe risk, methodological research that can accurately calculate annual energy production should be continued. The author argues that in order to further expand and improve the technological maturity of wind energy, basic research on regional wind resources in the area where the project will be carried out should be completed in detail. The results of this study could be interpreted completely differently in other regions or countries. This is because latitude and longitude, air temperature, sea surface temperature, and amphibious distribution show very large differences from region to region. However, this study is expected to be widely used as basic data for developers and researchers who want a detailed analysis of offshore wind resources.