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Wind power forecasting based on hourly wind speed data in South Korea using machine learning algorithms

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Abstract

Given that wind farms have high initial investment costs and are not easy to move after installation, the amount of energy that can be produced in the desired installation area needs to be predicted as accurately as possible before installation. Four machine learning algorithms are adopted to predict power production based on the daily wind speed average and standard deviation. The actual power output is calculated from the wind data generated by the numerical weather prediction, and its temporal resolution is 1 hour. The R-square (R2) values of the models range from 0.97 to 0.98 while adopting the average value of daily wind speed as the input data, and it increases by −1 % with the additional input data of the standard deviation of wind speed. The power production is predicted based on the wind data at a relatively lower height of 10 m than the hub height, where the R2 value ranges from 0.95 to 0.98. The results could provide the possibility of replacing the wind data measurement process at the hub height by that at a relatively lower height, reducing the cost of wind data measurement.

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References

  1. Ministry of Trace, Industry and Energy, Renewable Energy 3020 Implentation Plan, http://www.motie.go.kr/motiee/.

  2. C. S. Kim, New and Renewable Energy Statistics 2020, 2021 Edition, KOREA Energy Agency (2021).

  3. M. Inman, The true cost of fossil fuels, Scientific American, 308(4) (2013) 58–61.

    Article  Google Scholar 

  4. D. Weisser, A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies, Energy, 32(9) (2007) 1543–1559.

    Article  Google Scholar 

  5. S. Al-Yahyai, Y. Charabi and A. Gastli, Review of the use of numerical weather prediction (NWP) models for wind energy assessment, Renewable and Sustainable Energy Reviews, 14.9 (2010) 3192–3198.

    Article  Google Scholar 

  6. G. A. Grell, J. Dudhia and D. Stauffer, A Description of the Fifth-generation Penn State/NCAR Mesoscale Model (MM5), No. NCAR/TN-398+STR, University Corporation for Atmospheric Research (1994) doi:https://doi.org/10.5065/D60Z716B.

  7. B. Jimenez, F. Durante, B. Lange, T. Kreutzer and J. Tambke, Offshore wind resource assessment with WAsP and MM5: Comparative study for the German Bight, Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 10(2) (2007) 121–134.

    Article  Google Scholar 

  8. J. Done, C. A. Davis and M. Weisman, The next generation of NWP: explicit forecasts of convection using the weather research and forecasting (WRF) model, Atmospheric Science Letters, 5(6) (2004) 110–117.

    Article  Google Scholar 

  9. C.-H. Bang, J.-W. Lee and S.-Y. Hong, Predictability experiments of fog and visibility in local airports over Korea using the WRF model, Journal of Korean Society for Atmospheric Environment, 24(E2) (2008) 92–101.

    Google Scholar 

  10. U. Y. Byun, S. Y. Hong, H. Shin, J. W. Lee, J. I. Song, S. J. Hahm, J. K. Kim, H. W. Kim and J. S. Kim, WRF-based short-range forecast system of the Korea Air Force: Verification of prediction skill in 2009 summer, Atmosphere, 21(2) (2011) 197–208.

    Google Scholar 

  11. J. Brioude, D. Arnold, A. Stohl, M. Cassiani, D. Morton, P. Seibert, W. Angevine, S. Evan, A. Dingwel, J. D. Fast, R. C. Easter, I. Pisso, J. Burkhart and G. Wotawa, The lagrangian particle dispersion model FLEXPART-WRF version 3.1, Geoscientific Model Development, 6(6) (2013) 1889–1904.

    Article  Google Scholar 

  12. A. J. Litta, S. M. Ididcula, U. C. Mohanty and S. Kiran Prasad, Comparison of thunderstorm simulations from WRF-NMM and WRF-ARW models over east indian region, The Scientific World Journal (2012).

  13. D. C. Wong, J. Pleim, R. Mathur, F. Binkowski, T. Otte, R. Gilliam, G. Pouliot, A. Xiu, J. O. Young and D. Kang, WRF-CMAQ two-way coupled system with aerosol feedback: software development and preliminary results, Geoscientific Model Development, 5(2) (2012) 299–312.

    Article  Google Scholar 

  14. C. Wan, Z. Xu, P. Pinson, Z. Y. Dong and K. P. Wong, Probabilistic forecasting of wind power generation using extreme learning machine, IEEE Transactions on Power Systems, 29(3) (2013) 1033–1044.

    Article  Google Scholar 

  15. A. Zameer, A. Khan and S. G. Javed, Machine Learning based short term wind power prediction using a hybrid learning model, Computers and Electrical Engineering, 45 (2015) 122–133.

    Article  Google Scholar 

  16. J. Wang and Y. Li, Short-term wind speed prediction using signal preprocessing technique and evolutionary support vector regression, Neural Processing Letters, 48(2) (2018) 1043–1061.

    Article  Google Scholar 

  17. A. Lahouar and J. Ben Hadj Slama, Hour-ahead wind power forecast based on random forests, Renewable Energy, 109 (2017) 529–541.

    Article  Google Scholar 

  18. H. Liu, C. Chen, X. Lv, X. Wu and M. Liu, Deterministic wind energy forecasting, a review of intelligent predictors and auxiliary methods, Energy Conversion and Management, 195 (2019) 328–345.

    Article  Google Scholar 

  19. Jørgensen, K. Lau and H. R. Shaker, Wind power forecasting using machine learning: state of the art, trends and challenges, 2020 IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE), IEEE (2020) 44–50.

  20. M. Neshat, M. M. Nezhad, E. Abbasnejad, S. Mirjalili, L. B. Tjernberg, D. A. Garcia, B. Alexander and M. Wagner, A deep learning-based evolutionary model for short-term wind speed forecasting: a case study of the Lillgrund offshore wind farm, Energy Conversion and Management, 236 (2021) 114002.

    Article  Google Scholar 

  21. J. Heinermann and O. Kramer, Machine learning ensembles for wind power prediction, Renewable Energy, 89 (2016) 671–679.

    Article  Google Scholar 

  22. A. U. Haque, P. Mandal, J. Meng and M. Negnevitsky, Wind speed forecast model for wind farm based on a hybrid machine learning algorithm, International Journal of Sustainable Energy, 34(1) (2015) 38–51.

    Article  Google Scholar 

  23. M. Optis and J. Perr-Sauer, The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production, Renewable and Sustainable Energy Reviews, 112 (2019) 27–41.

    Article  Google Scholar 

  24. H. Demolli, A. S. Dokuz, A. Ecemis and M. Gokcek, Wind power forecasting based on daily wind speed data using machine learning algorithms, Energy Conversion and Management, 198 (2019) 111823.

    Article  Google Scholar 

  25. P. Piotrowski, D. Baczyński, M. Kopyt, K. Szafranek, P. Helt and T. Gulczyński, Analysis of forecasted meteorological data (NWP) for efficient spatial forecasting of wind power generation, Electric Power Systems Research, 175 (2019) 105891.

    Article  Google Scholar 

  26. D. Kim and J. Hur, Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method, Energy, 157 (2018) 211–226.

    Article  Google Scholar 

  27. W. Dong, Q. Yang and X. Fang, Multi-step ahead wind power generation prediction based on hybrid machine learning techniques, Energies, 11(8) (2018) 1975.

    Article  Google Scholar 

  28. P. Piotrowski, D. Baczyński, M. Kopyt, K. Szafranek, P. Helt and T. Gulczyński, Analysis of forecasted meteorological data (NWP) for efficient spatial forecasting of wind power generation, Electric Power Systems Research, 175 (2019) 105891.

    Article  Google Scholar 

  29. G. Alkhayat and R. Mehmood, A review and taxonomy of wind and solar energy forecasting methods based on deep learning, Energy and AI (2021) 100060.

  30. A. L. Samuel, Some studies in machine learning using the game of checkers, II—Recent progress, IBM Journal of Research and Development, 11(6) (1967) 601–617.

    Article  Google Scholar 

  31. Alpaydin, Ethem, Introduction to Machine Learning, MIT Press (2020).

  32. S.-C. Wang, Artificial neural network, Interdisciplinary Computing in Java Programming, Springer, Boston, MA (2003) 81–100.

    Chapter  Google Scholar 

  33. Z. Yao and W. L. Ruzzo, A regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data, BMC Bioinformatics, 7 (1) (2006).

  34. L. Breiman, Random forests, Machine Learning, 45(1) (2001) 5–32.

    Article  MATH  Google Scholar 

  35. H. Drucker, C. J. Burges, L. Kaufman, A. Smola and V. Vapnik, Support vector regression machines, Advances in Neural Information Processing Systems, 9 (1997) 155–161.

    Google Scholar 

  36. W. C. Hong, Y. Dong, L. Y. Chen and S. Y. Wei, SVR with hybrid chaotic genetic algorithms for tourism demand forecasting, Applied Soft Computing, 11(2) (2011) 1881–1890.

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Seoul National University of Science and Technology, Seoul, 01811, Korea

    Jeonghyeon Kim, Asif Afzal & Sung Goon Park

  2. University Centre for Research & Development, Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India

    Asif Afzal

  3. Renewable Resource Map Laboratory, Korea Institute of Energy Research, Daejeon, Korea

    Hyun-Goo Kim

  4. Department of Aerospace Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Vietnam

    Cong Truong Dinh

Authors
  1. Jeonghyeon Kim
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  2. Asif Afzal
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  3. Hyun-Goo Kim
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  4. Cong Truong Dinh
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  5. Sung Goon Park
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Corresponding author

Correspondence to Sung Goon Park.

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Sung Goon Park received his Ph.D. in Mechanical Engineering from the Korea Advanced Institute of Science and Technology (KAIST). He is currently an Assistant Professor at the Seoul National University of Science and Technology, Korea. His research interests include computational simulations of fluid-structure interactions and energy systems.

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Kim, J., Afzal, A., Kim, HG. et al. Wind power forecasting based on hourly wind speed data in South Korea using machine learning algorithms. J Mech Sci Technol 36, 6107–6113 (2022). https://doi.org/10.1007/s12206-022-1125-3

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  • DOI: https://doi.org/10.1007/s12206-022-1125-3

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