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A Multivariate and Multimodal Wind Distribution model

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  • Zhang, Jie
  • Chowdhury, Souma
  • Messac, Achille
  • Castillo, Luciano

Abstract

This paper presents a new methodology to accurately characterize and predict the annual variation of wind conditions. The estimate of the distribution of wind conditions is necessary to quantify the available energy (power density) at a site, and to design optimal wind farm configurations. A smooth multivariate wind distribution model is developed to capture the coupled variation of wind speed, wind direction, and air density. The wind distribution model developed in this paper avoids the limiting assumption of unimodality of the distribution. This method, which we call the Multivariate and Multimodal Wind Distribution (MMWD) model, is an evolution from existing wind distribution modeling techniques. Multivariate kernel density estimation, a standard non-parametric approach to estimate the probability density function of random variables, is adopted for this purpose. The MMWD technique is successfully applied to model (i) the distribution of wind speed (univariate); (ii) the joint distribution of wind speed and wind direction (bivariate); and (iii) the joint distribution of wind speed, wind direction, and air density (multivariate). The latter is a novel contribution of this paper, while the former offers opportunities for validation. Both onshore and offshore wind distributions are estimated using the MMWD model. Recorded wind data, obtained from the North Dakota Agricultural Weather Network (NDAWN) and the National Data Buoy Center (NDBC), is used in this paper. The coupled distribution was found to be multimodal. A strong correlation among the wind condition parameters was also observed.

Suggested Citation

  • Zhang, Jie & Chowdhury, Souma & Messac, Achille & Castillo, Luciano, 2013. "A Multivariate and Multimodal Wind Distribution model," Renewable Energy, Elsevier, vol. 51(C), pages 436-447.
    • Handle: RePEc:eee:renene:v:51:y:2013:i:c:p:436-447
    DOI: 10.1016/j.renene.2012.09.026
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    4. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
    5. Nan Yang & Yu Huang & Dengxu Hou & Songkai Liu & Di Ye & Bangtian Dong & Youping Fan, 2019. "Adaptive Nonparametric Kernel Density Estimation Approach for Joint Probability Density Function Modeling of Multiple Wind Farms," Energies, MDPI, vol. 12(7), pages 1-15, April.
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    10. Zhang, Jie & Jain, Rishabh & Hodge, Bri-Mathias, 2016. "A data-driven method to characterize turbulence-caused uncertainty in wind power generation," Energy, Elsevier, vol. 112(C), pages 1139-1152.
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    15. Hernández-Escobedo, Q. & Saldaña-Flores, R. & Rodríguez-García, E.R. & Manzano-Agugliaro, F., 2014. "Wind energy resource in Northern Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 890-914.
    16. Mohammad Arashi & Priyanka Nagar & Andriette Bekker, 2020. "Joint Probabilistic Modeling of Wind Speed and Wind Direction for Wind Energy Analysis: A Case Study in Humansdorp and Noupoort," Sustainability, MDPI, vol. 12(11), pages 1-15, May.
    17. Taylor, James W. & Jeon, Jooyoung, 2015. "Forecasting wind power quantiles using conditional kernel estimation," Renewable Energy, Elsevier, vol. 80(C), pages 370-379.
    18. Ju Feng & Wen Zhong Shen, 2015. "Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction," Energies, MDPI, vol. 8(4), pages 1-18, April.
    19. Jung, Christopher & Schindler, Dirk, 2019. "The role of air density in wind energy assessment – A case study from Germany," Energy, Elsevier, vol. 171(C), pages 385-392.
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