A hybrid WOA-KDE and mixed copula framework for directional wind assessment in complex terrain

IF 7 2区 工程技术 Q1 ENERGY & FUELS
Weijia Wang , Fubin Chen , Yi Li , Lanxi Weng
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引用次数: 0

Abstract

Accurate wind energy assessment in complex terrain remains a significant challenge due to the difficulty of reliably modeling the bivariate probabilistic relationship between wind speed and direction. To address such concern, this study proposes a novel hybrid framework that integrates advanced marginal and joint distribution modeling techniques. Wind speed is modeled using Kernel Density Estimation (KDE), optimized through the Whale Optimization Algorithm (WOA), resulting in superior performance over conventional parametric approaches. For wind direction, the Von Mises distribution is employed, demonstrating a 2–13 % improvement (measured by R2) in goodness-of- fit compared to traditional harmonic models. These optimized marginal distributions are then coupled via a mixed copula constructed as a linear combination of Gumbel, Clayton, and Frank copulas. The resulting joint probability density function (JPDF) exhibits significantly enhanced performance, achieving a comprehensive evaluation metric of 4.92 and outperforming five widely used single copulas, as well as the Angular-Linear (AL) and multiplication models. The mixed copula approach yields a 5–20% improvement (measured by composite metric) in JPDF accuracy compared to these classic models, producing more realistic and reliable estimates of wind characteristics. The validated model is applied to conduct a comprehensive directional wind energy assessment, offering a high-resolution analytical tool for wind energy applications.
混合WOA-KDE和混合copula框架在复杂地形风向评估中的应用
由于难以对风速和风向之间的二元概率关系进行可靠的建模,复杂地形下准确的风能评估仍然是一个重大挑战。为了解决这一问题,本研究提出了一种新的混合框架,该框架集成了先进的边际和联合分布建模技术。风速使用核密度估计(KDE)建模,通过鲸鱼优化算法(WOA)进行优化,从而获得优于传统参数方法的性能。对于风向,采用Von Mises分布,与传统谐波模型相比,拟合优度提高了2 - 13%(由R2测量)。然后,这些优化的边际分布通过一个混合联结函数耦合,该联结函数是由Gumbel、Clayton和Frank联结函数构成的线性组合。所得到的联合概率密度函数(JPDF)表现出显著增强的性能,达到了4.92的综合评价指标,优于五种广泛使用的单copula模型,以及角线性(AL)和乘法模型。与这些经典模型相比,混合copula方法在JPDF精度上提高了5-20%(通过复合度量测量),产生了更真实、更可靠的风特性估计。将验证的模型应用于风能综合定向评价,为风能应用提供了高分辨率的分析工具。
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来源期刊
Sustainable Energy Technologies and Assessments
Sustainable Energy Technologies and Assessments Energy-Renewable Energy, Sustainability and the Environment
CiteScore
12.70
自引率
12.50%
发文量
1091
期刊介绍: Encouraging a transition to a sustainable energy future is imperative for our world. Technologies that enable this shift in various sectors like transportation, heating, and power systems are of utmost importance. Sustainable Energy Technologies and Assessments welcomes papers focusing on a range of aspects and levels of technological advancements in energy generation and utilization. The aim is to reduce the negative environmental impact associated with energy production and consumption, spanning from laboratory experiments to real-world applications in the commercial sector.
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