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Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method

Author

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  • Zhao, Jing
  • Guo, Yanling
  • Xiao, Xia
  • Wang, Jianzhou
  • Chi, Dezhong
  • Guo, Zhenhai

Abstract

At present, operational power forecasts are primarily based on the predicted wind speed of a single-valued deterministic Numerical Weather Prediction (NWP) simulation. However, due to the unavoidable uncertainties from model initialization and/or model imperfections, recent numerical techniques cannot directly meet the actual needs of grid dispatch in many cases, which means that achieving accurate forecasts of wind speed and power is still a critical issue. On this topic, our paper contributes to the development of a new multi-step forecasting method termed CSFC-Apriori-WRF, providing a one-day ahead wind speed and power forecast consisting of 96 steps. This method is based on a Weather Research and Forecasting (WRF) simulation, a Cuckoo search (CS) optimized fuzzy clustering, and an Apriori association process. First, a wind speed forecast is generated by running a configured WRF model. Next, the wind speed forecasting series is divided into segments that meet certain conditions and are defined as “waves” in this paper. Next, combining the CS-optimized fuzzy clustering and Apriori algorithm, the proposed method extracts the association rules between the shape characteristics and the forecasting error of the divided waves. Applying the association rules in the final optimization process, the proposed method significantly reduces the uncertainties of the WRF simulation and performs best among other models to which it is compared.

Suggested Citation

  • Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
    • Handle: RePEc:eee:appene:v:197:y:2017:i:c:p:183-202
    DOI: 10.1016/j.apenergy.2017.04.017
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    References listed on IDEAS

    as
    1. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    2. Guo, Zhenhai & Zhao, Jing & Zhang, Wenyu & Wang, Jianzhou, 2011. "A corrected hybrid approach for wind speed prediction in Hexi Corridor of China," Energy, Elsevier, vol. 36(3), pages 1668-1679.
    3. Fyrippis, Ioannis & Axaopoulos, Petros J. & Panayiotou, Gregoris, 2010. "Wind energy potential assessment in Naxos Island, Greece," Applied Energy, Elsevier, vol. 87(2), pages 577-586, February.
    4. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    5. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    6. Carvalho, D. & Rocha, A. & Santos, C. Silva & Pereira, R., 2013. "Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques," Applied Energy, Elsevier, vol. 108(C), pages 493-504.
    7. Zjavka, Ladislav, 2015. "Wind speed forecast correction models using polynomial neural networks," Renewable Energy, Elsevier, vol. 83(C), pages 998-1006.
    8. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    9. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
    10. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    11. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    12. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    13. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    14. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    15. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    16. Ait Maatallah, Othman & Achuthan, Ajit & Janoyan, Kerop & Marzocca, Pier, 2015. "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Elsevier, vol. 145(C), pages 191-197.
    17. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    18. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    19. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    20. Alessandrini, S. & Sperati, S. & Pinson, P., 2013. "A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data," Applied Energy, Elsevier, vol. 107(C), pages 271-280.
    21. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    22. Pinson, P. & Nielsen, H.Aa. & Madsen, H. & Kariniotakis, G., 2009. "Skill forecasting from ensemble predictions of wind power," Applied Energy, Elsevier, vol. 86(7-8), pages 1326-1334, July.
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