季节-日风矢量的自动概率分析和参数化建模

Q4 Energy
Nicholas Cook
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引用次数: 0

摘要

偏移椭圆法向混合模式的改进和扩展版本已被开发出来,用于自动参数化季节日风矢量。该方法使用R脚本实现自动化,消除了原始方法中人工监督带来的确认偏差的任何潜在风险。该方法的改进包括高斯混合聚类的最新算法,使用贝叶斯正则化来设置组件的数量并限制过度拟合的倾向。一个新的扩展使用模糊逻辑来评估的概率分布,自协方差和随机扰动的频谱周围的平均季节-日变化的混合物的每个组成部分。这些额外的参数使OEN模型的预测得到验证,并通过每小时的METAR平均风速报告在南澳大利亚阿德莱德演示了其自动化应用,显示出比之前发表的分析有显着改进。OEN混合模型直接适用于季节性和日变化非常重要的风工程应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Probabilistic Analysis and Parametric Modelling of the Seasonal-Diurnal Wind Vector
A refined and extended version of the Offset Elliptical Normal mixture model has been developed to parameterise the seasonal diurnal wind vector automatically. Automated using R scripts, the method eliminates any potential risk of confirmation bias posed by the manual supervision in the original method. Refinements to the method include the latest algorithms for clustering of Gaussian mixtures, with Bayesian regularisation to set the number of components and to limit the predisposition to overfit. A new extension uses fuzzy logic to evaluate the probability distributions, autocovariances and spectra of the random perturbations around the mean seasonal-diurnal variations for each component of the mixture. These additional parameters allow the predictions of the OEN model to be validated and its automated application demonstrated using the hourly METAR reports of mean wind speeds at Adelaide, South Australia, showing significant improvements over the previously published analysis. The OEN mixture model is directly applicable to a wide range of wind engineering applications where seasonal and diurnal variation is of importance.
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来源期刊
Journal of Nuclear Energy Science and Power Generation Technology
Journal of Nuclear Energy Science and Power Generation Technology Energy-Energy Engineering and Power Technology
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