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A novel hybrid technique for prediction of electric power generation in wind farms based on WIPSO, neural network and wavelet transform

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  • Nourani Esfetang, Naser
  • Kazemzadeh, Rasool

Abstract

This paper proposes a novel hybrid technique for prediction of power generation in wind farm. Using this technique, the effective number of input parameters is determined by data correlation analysis. Then, the type of the effective number of parameters are selected from current parameters via neural network. The WT (wavelet transform) is used for filtering input data related to wind power; while, neural network ‘RBF (Radial basis function)’ is utilized as a preliminary predictor. Main predictor motor is combined three neural networks of MLP (Multilayer perceptron) with learning algorithms: BR (Bayesian regularization), RP(Resilient back propagation), and LM(Levenberg Marquardt). The heuristic technique ‘WIPSO (Weight improved particle swarm optimization)’ is used to improve the accuracy of predictions and escape from local minimum to optimize weights of neural networks. Input data is realistic data of wind farms in Southern Alberta, Canada for recent years. The data is a complete set of wind power and five meteorological characteristics including wind speed, wind direction, temperature, air pressure and humidity. Simulation results verify appropriate selection of the number and type of input parameters and heuristic algorithm application. Additionally, the absolute superiority of this technique is validated compared with the other methods. Prediction error is also reduced perceptibly.

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  • Nourani Esfetang, Naser & Kazemzadeh, Rasool, 2018. "A novel hybrid technique for prediction of electric power generation in wind farms based on WIPSO, neural network and wavelet transform," Energy, Elsevier, vol. 149(C), pages 662-674.
    • Handle: RePEc:eee:energy:v:149:y:2018:i:c:p:662-674
    DOI: 10.1016/j.energy.2018.02.076
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    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Hernandez-Escobedo, Quetzalcoatl & Manzano-Agugliaro, Francisco & Gazquez-Parra, Jose Antonio & Zapata-Sierra, Antonio, 2011. "Is the wind a periodical phenomenon? The case of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 721-728, January.
    3. Men, Zhongxian & Yee, Eugene & Lien, Fue-Sang & Wen, Deyong & Chen, Yongsheng, 2016. "Short-term wind speed and power forecasting using an ensemble of mixture density neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 203-211.
    4. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    5. Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo & Monteiro, Cláudio & Sousa, João & Bessa, Ricardo, 2009. "Comparison of two new short-term wind-power forecasting systems," Renewable Energy, Elsevier, vol. 34(7), pages 1848-1854.
    6. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    7. Monfared, Mohammad & Rastegar, Hasan & Kojabadi, Hossein Madadi, 2009. "A new strategy for wind speed forecasting using artificial intelligent methods," Renewable Energy, Elsevier, vol. 34(3), pages 845-848.
    8. Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
    9. Han, Li & Romero, Carlos E. & Yao, Zheng, 2015. "Wind power forecasting based on principle component phase space reconstruction," Renewable Energy, Elsevier, vol. 81(C), pages 737-744.
    10. Manzano-Agugliaro, F. & Alcayde, A. & Montoya, F.G. & Zapata-Sierra, A. & Gil, C., 2013. "Scientific production of renewable energies worldwide: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 134-143.
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