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Wind resource assessment based on numerical simulations and an optimized ensemble system

机译：基于数值模拟和优化集成系统的风资源评估

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High-quality wind data are essential for the whole wind energy assessment process. At present, wind data obtained from numerical weather prediction models are regarded as the most promising alternative to overcome the multiple constraints of observation data. However, the associated numerical uncertainties may lead to incorrect results. New capabilities and strategies are highly required to improve the utilization of wind data obtained from numerical models and further to reduce their uncertainty. Our paper contributes to the development of an improved wind energy assessment method, which utilizes a set of wind speeds obtained from different single-valued Weather Research and Forecasting simulations to estimate the yearly wind speed distribution and power generation. First, the wind data were generated by running a set of configured numerical models. Second, the wind speed series were divided into segments (i.e., "waves"), which were clustered into several groups by the fuzzy C-means clustering. Third, we applied a Cuckoo search optimized induced ordered weighted average method, induced by the gray relationship to each wave group. The proposed method reduced the numerical uncertainties and had superior performance compared to other tested models. In this study, we demonstrated an improvement in the utilization of data obtained from numerical simulations and constructed a practical tool for real wind applications.
机译：高质量的风数据对于整个风能评估过程至关重要。目前，从数值天气预报模型获得的风数据被认为是克服观测数据多重约束的最有前途的选择。但是，相关的数值不确定性可能导致错误的结果。迫切需要新的功能和策略来提高从数值模型获得的风数据的利用率，并进一步减少其不确定性。我们的论文为改进风能评估方法的发展做出了贡献，该方法利用从不同的单值天气研究和预报模拟获得的一组风速来估算年度风速分布和发电量。首先，通过运行一组配置的数值模型来生成风数据。其次，将风速级数划分为多个段（即“波”），这些段通过模糊C均值聚类被分为几组。第三，我们应用了Cuckoo搜索优化的诱导有序加权平均方法，该方法由与每个波组的灰色关系诱导。与其他测试模型相比，该方法减少了数值不确定性，并具有优越的性能。在这项研究中，我们展示了从数值模拟获得的数据利用率的提高，并构建了用于实际风能应用的实用工具。

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