Understanding the regional theoretical wind potential is crucial for wind power planning and construction. Previous studies have faced challenges including inconsistent wind speed data quality, unquantified uncertainties in distribution parameters, and flawed methods for estimating theoretical wind potential. Therefore, this study introduced a Hierarchical Bayesian-Monte Carlo framework that processed multi-year and multi-source wind speed data in a probabilistic and hierarchical manner. It could quantify the uncertainties associated with wind speed distributions and their parameters and reduce prediction errors by integrating the historical data. Moreover, the effects of wind speed and air density variations over the blade sweep height and the maximum possible power coefficient were considered on the traditional method of estimating theoretical wind potential. The results showed that the wind speed distributions in the Qinghai-Tibetan Plateau followed Weibull functions, with the prior distributions of their parameters k and λ being gamma functions. Using the Metropolis-Hastings algorithm to simulate the posterior distributions indicated that the overall standard deviations after merging the two chains of k and λ were less than 0.0193 and 0.0244 m/s, respectively. The uncertainties of k and λ were less than 0.08 and 0.097 m/s, respectively. The discrepancies between the predicted and actual wind speeds were less than 0.089 m/s. These findings confirmed the validity and reliability of the Hierarchical Bayesian-Monte Carlo model. Furthermore, in the Qinghai-Tibetan Plateau, 19.31 % of the area had the maximum theoretical wind potential, 21.43 % a high level, and 19.78 % a moderate level. Consequently, the flexible methodological framework established by this study can effectively support the identification of optimal locations for wind power development across regions.

中文翻译：

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揭示青藏高原的理论风潜力：Weibull 二元分布的新型贝叶斯蒙特卡洛框架


了解区域理论风电潜力对于风电规划和建设至关重要。以前的研究面临挑战，包括风速数据质量不一致、分布参数的不确定性不量化以及估计理论风势的方法存在缺陷。因此，本研究引入了一个分层贝叶斯-蒙特卡洛框架，该框架以概率和分层方式处理多年和多源风速数据。它可以量化与风速分布及其参数相关的不确定性，并通过整合历史数据来减少预测误差。此外，在估计理论风势的传统方法中，考虑了风速和空气密度变化对叶片后掠高度和最大可能功率系数的影响。结果表明：青藏高原地区风速分布服从魏布尔函数，其参数 k 和 λ 的先验分布为 γ 函数。使用 Metropolis-Hastings 算法模拟后验分布表明，合并 k 和 λ 两条链后的总体标准差分别小于 0.0193 和 0.0244 m/s。k 和 λ 的不确定性分别小于 0.08 和 0.097 m/s。预测风速和实际风速之间的差异小于 0.089 m/s。这些发现证实了分层贝叶斯-蒙特卡洛模型的有效性和可靠性。此外，青藏高原理论风势最大 19.31 %，高 21.43 %，中等 19.78 %。 因此，本研究建立的灵活的方法框架可以有效地支持确定跨区域风电开发的最佳位置。