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Short-term prediction of wind power with a clustering approach

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  • Kusiak, Andrew
  • Li, Wenyan

Abstract

A clustering approach is presented for short-term prediction of power produced by a wind turbine at low wind speeds. Increased prediction accuracy of wind power to be produced at future time periods is often bounded by the prediction model complexity and computational time involved. In this paper, a trade-off between the two conflicting objectives is addressed. First, a set of the most relevant parameters (predictors) is selected using the underlying physics and pattern immersed in data. Five scenarios of the input space are created with the k-means clustering algorithm. The most promising clustering scenario is applied to produce a model for each clustered subspace. Computational results are compared and the benefits of cluster–specific (customized) models are discussed. The results show that the prediction accuracy is improved the input space is clustered and customized prediction models are developed.

Suggested Citation

  • Kusiak, Andrew & Li, Wenyan, 2010. "Short-term prediction of wind power with a clustering approach," Renewable Energy, Elsevier, vol. 35(10), pages 2362-2369.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:10:p:2362-2369
    DOI: 10.1016/j.renene.2010.03.027
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    References listed on IDEAS

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    1. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
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    1. Suárez-Cetrulo, Andrés L. & Burnham-King, Lauren & Haughton, David & Carbajo, Ricardo Simón, 2022. "Wind power forecasting using ensemble learning for day-ahead energy trading," Renewable Energy, Elsevier, vol. 191(C), pages 685-698.
    2. Yu-Fang Wang, 2020. "Adaptive job shop scheduling strategy based on weighted Q-learning algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 417-432, February.
    3. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    4. Song, Zhe & Zhang, Zijun & Jiang, Yu & Zhu, Jin, 2018. "Wind turbine health state monitoring based on a Bayesian data-driven approach," Renewable Energy, Elsevier, vol. 125(C), pages 172-181.
    5. Xiong, Linyun & Li, Penghan & Wang, Ziqiang & Wang, Jie, 2020. "Multi-agent based multi objective renewable energy management for diversified community power consumers," Applied Energy, Elsevier, vol. 259(C).
    6. Hsu-Hao Yang & Mei-Ling Huang & Shih-Wei Yang, 2015. "Integrating Auto-Associative Neural Networks with Hotelling T 2 Control Charts for Wind Turbine Fault Detection," Energies, MDPI, vol. 8(10), pages 1-16, October.
    7. Amar Azhar & Huzaifa Hashim, 2024. "Wind Mapping of Malaysia Using Ward’s Clustering Method," Energies, MDPI, vol. 17(7), pages 1-24, March.
    8. Qiaomu Zhu & Jinfu Chen & Lin Zhu & Xianzhong Duan & Yilu Liu, 2018. "Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach," Energies, MDPI, vol. 11(4), pages 1-18, March.
    9. Samet, Haidar & Marzbani, Fatemeh, 2014. "Quantizing the deterministic nonlinearity in wind speed time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1143-1154.
    10. Salcedo-Sanz, S. & Pastor-Sánchez, A. & Del Ser, J. & Prieto, L. & Geem, Z.W., 2015. "A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction," Renewable Energy, Elsevier, vol. 75(C), pages 93-101.
    11. Goh, H.H. & Lee, S.W. & Chua, Q.S. & Goh, K.C. & Teo, K.T.K., 2016. "Wind energy assessment considering wind speed correlation in Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1389-1400.
    12. Bigdeli, Nooshin & Afshar, Karim & Gazafroudi, Amin Shokri & Ramandi, Mostafa Yousefi, 2013. "A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 20-29.
    13. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    14. Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Predictive model of yaw error in a wind turbine," Energy, Elsevier, vol. 123(C), pages 119-130.
    15. Catalão, J.P.S. & Pousinho, H.M.I. & Mendes, V.M.F., 2011. "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable Energy, Elsevier, vol. 36(4), pages 1245-1251.
    16. Amar Azhar & Huzaifa Hashim, 2023. "A Review of Wind Clustering Methods Based on the Wind Speed and Trend in Malaysia," Energies, MDPI, vol. 16(8), pages 1-24, April.
    17. Vera-Tudela, Luis & Kühn, Martin, 2017. "Analysing wind turbine fatigue load prediction: The impact of wind farm flow conditions," Renewable Energy, Elsevier, vol. 107(C), pages 352-360.
    18. Xiaojun Shen & Chongchen Zhou & Guojie Li & Xuejiao Fu & Tek Tjing Lie, 2018. "Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines," Energies, MDPI, vol. 11(7), pages 1-23, July.
    19. Optis, Mike & Perr-Sauer, Jordan, 2019. "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 27-41.
    20. Chidean, Mihaela I. & Caamaño, Antonio J. & Ramiro-Bargueño, Julio & Casanova-Mateo, Carlos & Salcedo-Sanz, Sancho, 2018. "Spatio-temporal analysis of wind resource in the Iberian Peninsula with data-coupled clustering," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2684-2694.

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