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Wind turbine power curve modeling for reliable power prediction using monotonic regression

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  • Mehrjoo, Mehrdad
  • Jafari Jozani, Mohammad
  • Pawlak, Miroslaw

Abstract

Wind turbine power curve modeling plays an important role in wind energy management and power forecasting and it is often done based on parametric or non-parametric methods. As wind-power data are often noisy, even after polishing data using proper methods, fitted wind turbine power curves could be very different from the theoretical ones that are provided by manufacturers. For example, it might be the case that the theoretical wind turbine power curve is a non-decreasing function of speed but the fitted statistical model does not necessarily meet this desirable property. In this paper, we present two nonparametric techniques based on tilting method and monotonic spline regression methodology to construct wind turbine power curves that preserve monotonicity. To measure the performance of our proposed methods, we evaluate and compare our estimates with some commonly used power curve fitting methods based on historical data from a wind farm in Manitoba, Canada. Results show that monotone spline regression performs the best while the tilting approach performs similar to the methods we studied in this paper with the benefit of finding a curve that is more similar to the theoretical power curve.

Suggested Citation

  • Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2020. "Wind turbine power curve modeling for reliable power prediction using monotonic regression," Renewable Energy, Elsevier, vol. 147(P1), pages 214-222.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:214-222
    DOI: 10.1016/j.renene.2019.08.060
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    Cited by:

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    4. Zou, Runmin & Yang, Jiaxin & Wang, Yun & Liu, Fang & Essaaidi, Mohamed & Srinivasan, Dipti, 2021. "Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer," Applied Energy, Elsevier, vol. 304(C).
    5. Diego Francisco Larios & Enrique Personal & Antonio Parejo & Sebastián García & Antonio García & Carlos Leon, 2020. "Operational Simulation Environment for SCADA Integration of Renewable Resources," Energies, MDPI, vol. 13(6), pages 1-37, March.
    6. Liang, Guoyuan & Su, Yahao & Wu, Xinyu & Ma, Jiajun & Long, Huan & Song, Zhe, 2023. "Abnormal data cleaning for wind turbines by image segmentation based on active shape model and class uncertainty," Renewable Energy, Elsevier, vol. 216(C).
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    8. Malz, E.C. & Verendel, V. & Gros, S., 2020. "Computing the power profiles for an Airborne Wind Energy system based on large-scale wind data," Renewable Energy, Elsevier, vol. 162(C), pages 766-778.

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