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The effect of missing data on wind resource estimation

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  • Coville, Aidan
  • Siddiqui, Afzal
  • Vogstad, Klaus-Ole

Abstract

Investment in renewable energy sources requires reliable data. However, meteorological datasets are often plagued by missing data, which can bias energy resource estimates if the missingness is systematic. We address this issue by considering the influence of missing data due to icing of equipment during the winter on the wind resource estimation for a potential wind turbine site in Norway. Using a mean-reverting jump-diffusion (MRJD) process to model electricity prices, we also account for the impact on the expected revenue from a wind turbine constructed at the site. While missing data due to icing significantly bias the wind resource estimate downwards, their impact on revenue estimates is dampened because of volatile electricity spot prices. By contrast, with low-volatility electricity prices, the effect of missing data on revenue estimates is highly significant. The seasonality-based method we develop removes most of the bias in wind resource and revenue estimation caused by missing data.

Suggested Citation

  • Coville, Aidan & Siddiqui, Afzal & Vogstad, Klaus-Ole, 2011. "The effect of missing data on wind resource estimation," Energy, Elsevier, vol. 36(7), pages 4505-4517.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:7:p:4505-4517
    DOI: 10.1016/j.energy.2011.03.067
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    References listed on IDEAS

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    Cited by:

    1. Makbul A. M. Ramli & Houssem R. E. H. Bouchekara, 2025. "Wind Resource Assessment for Potential Wind Turbine Operations in the City of Yanbu, Saudi Arabia," Energies, MDPI, vol. 18(8), pages 1-29, April.
    2. LM López-Manrique & EV Macias-Melo & KM Aguilar-Castro & I Hernández-Pérez & HP Díaz-Hernández, 2021. "Review on methodological and normative advances in assessment and estimation of wind energy," Energy & Environment, , vol. 32(1), pages 25-61, February.
    3. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
    4. Korprasertsak, Natapol & Leephakpreeda, Thananchai, 2018. "Nyquist-based adaptive sampling rate for wind measurement under varying wind conditions," Renewable Energy, Elsevier, vol. 119(C), pages 290-298.
    5. Liu, Ming-Lei & Ji, Qiang & Fan, Ying, 2013. "How does oil market uncertainty interact with other markets? An empirical analysis of implied volatility index," Energy, Elsevier, vol. 55(C), pages 860-868.

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