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A frequency domain approach to characterize and analyze wind speed patterns

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  • Jung, Jaesung
  • Tam, Kwa-Sur

Abstract

This paper introduces the frequency domain approach to characterize and analyze wind speed patterns. It first presents the technique of and the prerequisite conditions for the frequency domain approach. Three years of wind speed data at 10 different locations have been used. This paper demonstrates that wind speed patterns during different times and at different locations can be well characterized by using the frequency domain approach with its compact and structured format. We also perform analysis using the characterized dataset. It affirms that the frequency domain approach is a useful indicator for understanding the characteristics of wind speed patterns and can express the information with superior accuracy.

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  • Jung, Jaesung & Tam, Kwa-Sur, 2013. "A frequency domain approach to characterize and analyze wind speed patterns," Applied Energy, Elsevier, vol. 103(C), pages 435-443.
    • Handle: RePEc:eee:appene:v:103:y:2013:i:c:p:435-443
    DOI: 10.1016/j.apenergy.2012.10.006
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    Cited by:

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    2. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    3. Liang, Zhengtang & Liang, Jun & Zhang, Li & Wang, Chengfu & Yun, Zhihao & Zhang, Xu, 2015. "Analysis of multi-scale chaotic characteristics of wind power based on Hilbert–Huang transform and Hurst analysis," Applied Energy, Elsevier, vol. 159(C), pages 51-61.
    4. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    5. Xu, Li & Ou, Yanxia & Cai, Jingjing & Wang, Jin & Fu, Yang & Bian, Xiaoyan, 2023. "Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition," Renewable Energy, Elsevier, vol. 216(C).
    6. Shi, Jie & Wang, Luhao & Lee, Wei-Jen & Cheng, Xingong & Zong, Xiju, 2019. "Hybrid Energy Storage System (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction," Applied Energy, Elsevier, vol. 256(C).
    7. Christy Pérez-Albornoz & Ángel Hernández-Gómez & Victor Ramirez & Damien Guilbert, 2023. "Forecast Optimization of Wind Speed in the North Coast of the Yucatan Peninsula, Using the Single and Double Exponential Method," Clean Technol., MDPI, vol. 5(2), pages 1-22, June.
    8. Yuan, Qiheng & Zhou, Keliang & Yao, Jing, 2020. "A new measure of wind power variability with implications for the optimal sizing of standalone wind power systems," Renewable Energy, Elsevier, vol. 150(C), pages 538-549.
    9. Escalante Soberanis, M.A. & Mérida, W., 2015. "Regarding the influence of the Van der Hoven spectrum on wind energy applications in the meteorological mesoscale and microscale," Renewable Energy, Elsevier, vol. 81(C), pages 286-292.
    10. Masseran, Nurulkamal, 2016. "Modeling the fluctuations of wind speed data by considering their mean and volatility effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 777-784.
    11. Ke Gong & Yi Peng & Yong Wang & Maozeng Xu, 2018. "Time series analysis for C2C conversion rate," Electronic Commerce Research, Springer, vol. 18(4), pages 763-789, December.

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