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A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China

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  • Dong, Qingli
  • Sun, Yuhuan
  • Li, Peizhi

Abstract

As a crucial issue in the wind power industry, it is a tough and challenging task to predict the wind power accurately because of its nonlinearity, non-stationary and chaos. In this paper, we propose a novel hybrid model, which combines an integrated processing strategy and an optimized local linear fuzzy neural network, to forecast the wind power. First, discrete wavelet transform and singular spectrum analysis are used to filter out the noises and extract the trends from original wind power series, respectively. Then, the novel no-negative-constraint-combination theory together with the CS algorithm are adopted to integrate these two subseries obtained from the first step to retain strengths of each method. Based on the phase space reconstruction model, we could determine the most proper structure of the input sets and the output sets. Next, the local linear fuzzy neural network, with the initial rule consequent parameters optimized by the seeker optimization algorithm, is utilized to make wind power forecasts for a selected number of forward time steps. The numerical results from two experiments demonstrate that the proposed hybrid model is an effective approach to predict wind power, and the accuracy of prediction is highly improved compared with conventional forecasting models.

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  • Dong, Qingli & Sun, Yuhuan & Li, Peizhi, 2017. "A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China," Renewable Energy, Elsevier, vol. 102(PA), pages 241-257.
    • Handle: RePEc:eee:renene:v:102:y:2017:i:pa:p:241-257
    DOI: 10.1016/j.renene.2016.10.030
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    1. Feng, Yi & Lin, Heyun & Ho, S.L. & Yan, Jianhu & Dong, Jianning & Fang, Shuhua & Huang, Yunkai, 2015. "Overview of wind power generation in China: Status and development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 847-858.
    2. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
    3. Schuelke-Leech, Beth-Anne & Barry, Betsy & Muratori, Matteo & Yurkovich, B.J., 2015. "Big Data issues and opportunities for electric utilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 937-947.
    4. Croonenbroeck, Carsten & Stadtmann, Georg, 2015. "Minimizing asymmetric loss in medium-term wind power forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 197-208.
    5. Hu, Jianming & Wang, Jianzhou & Ma, Kailiang, 2015. "A hybrid technique for short-term wind speed prediction," Energy, Elsevier, vol. 81(C), pages 563-574.
    6. 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.
    7. 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.
    8. Xiao, Ling & Wang, Jianzhou & Dong, Yao & Wu, Jie, 2015. "Combined forecasting models for wind energy forecasting: A case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 271-288.
    9. Amirkhani, S. & Nasirivatan, Sh. & Kasaeian, A.B. & Hajinezhad, A., 2015. "ANN and ANFIS models to predict the performance of solar chimney power plants," Renewable Energy, Elsevier, vol. 83(C), pages 597-607.
    10. Liu, Hui & Chen, Chao & Tian, Hong-qi & Li, Yan-fei, 2012. "A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks," Renewable Energy, Elsevier, vol. 48(C), pages 545-556.
    11. 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.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Jiang, Yu & Song, Zhe & Kusiak, Andrew, 2013. "Very short-term wind speed forecasting with Bayesian structural break model," Renewable Energy, Elsevier, vol. 50(C), pages 637-647.
    14. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    15. 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.
    16. Carrizosa, Emilio & Olivares-Nadal, Alba V. & Ramírez-Cobo, Pepa, 2013. "Time series interpolation via global optimization of moments fitting," European Journal of Operational Research, Elsevier, vol. 230(1), pages 97-112.
    17. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    18. 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.
    19. 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.
    20. Riahy, G.H. & Abedi, M., 2008. "Short term wind speed forecasting for wind turbine applications using linear prediction method," Renewable Energy, Elsevier, vol. 33(1), pages 35-41.
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    17. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    18. Khatereh Ghasvarian Jahromi & Davood Gharavian & Hamid Reza Mahdiani, 2023. "Wind power prediction based on wind speed forecast using hidden Markov model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 101-123, January.
    19. Wenlong Fu & Kai Wang & Jianzhong Zhou & Yanhe Xu & Jiawen Tan & Tie Chen, 2019. "A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy," Sustainability, MDPI, vol. 11(6), pages 1-24, March.
    20. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
    21. Cuadra, L. & Ocampo-Estrella, I. & Alexandre, E. & Salcedo-Sanz, S., 2019. "A study on the impact of easements in the deployment of wind farms near airport facilities," Renewable Energy, Elsevier, vol. 135(C), pages 566-588.
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