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Fuzzy First-Order Transition-Rules-Trained Hybrid Forecasting System for Short-Term Wind Speed Forecasts

Author

Listed:
  • Yulong Bai

    (College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou 730070, China)

  • Lihong Tang

    (College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou 730070, China)

  • Manhong Fan

    (College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou 730070, China)

  • Xiaoyan Ma

    (College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou 730070, China)

  • Yang Yang

    (College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou 730070, China)

Abstract

Due to the ever-increasing environmental pollution becoming progressively more serious, wind power has been widely used around the world in recent years. However, because of their randomness and intermittence, the accurate prediction of wind speeds is difficult. To address this problem, this article proposes a hybrid system for short-wind-speed prediction. The system combines the autoregressive differential moving average (ARIMA) model with a three-layer feedforward neural network. An ARIMA model was employed to predict linear patterns in series, while a feedforward neural network was used to predict the nonlinear patterns in series. To improve accuracy of the predictions, the neural network models were trained by using two methods: first-order transition rules and fuzzy first-order transition rules. The Levenberg–Marquardt (LM) algorithm was applied to update the weight and deviation of each layer of neural network. The dominance matrix method was employed to calculate the weight of the hybrid system, which was used to establish the linear hybrid system. To evaluate the performance, three statistical indices were used: the mean square error (MSE), the root mean square error (RMSE) and the mean absolute percentage error (MAPE). A set of Lorenz-63 simulated values and two datasets collected from different wind fields in Qilian County, Qinghai Province, China, were utilized as to perform a comparative study. The results show the following: (a) compared with the neural network trained by first-order transition rules, the prediction accuracy of the neural network trained by the fuzzy first-order transition rules was higher; (b) the proposed hybrid system attains superior performance compared with a single model; and (c) the proposed hybrid system balances the forecast accuracy and convergence speed simultaneously during forecasting. Therefore, it was feasible to apply the hybrid model to the prediction of real time-series.

Suggested Citation

  • Yulong Bai & Lihong Tang & Manhong Fan & Xiaoyan Ma & Yang Yang, 2020. "Fuzzy First-Order Transition-Rules-Trained Hybrid Forecasting System for Short-Term Wind Speed Forecasts," Energies, MDPI, vol. 13(13), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3332-:d:378232
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    References listed on IDEAS

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    1. M. H. Lee & H. J. Sadaei & Suhartono, 2013. "Improving TAIEX forecasting using fuzzy time series with Box--Cox power transformation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(11), pages 2407-2422, November.
    2. 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.
    3. Jian Yang & Xin Zhao & Haikun Wei & Kanjian Zhang, 2019. "Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction," Energies, MDPI, vol. 12(3), pages 1-12, January.
    4. Jiang, Ping & Yang, Hufang & Heng, Jiani, 2019. "A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting," Applied Energy, Elsevier, vol. 235(C), pages 786-801.
    5. 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.
    6. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    7. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
    8. Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
    9. Wang, Jianzhou & Wang, Shiqi & Yang, Wendong, 2019. "A novel non-linear combination system for short-term wind speed forecast," Renewable Energy, Elsevier, vol. 143(C), pages 1172-1192.
    10. Zhi Liu & Tie Zhang, 2019. "A second-order fuzzy time series model for stock price analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(14), pages 2514-2526, October.
    11. Tai Vovan, 2019. "An improved fuzzy time series forecasting model using variations of data," Fuzzy Optimization and Decision Making, Springer, vol. 18(2), pages 151-173, June.
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    Cited by:

    1. Luis Lopez & Ingrid Oliveros & Luis Torres & Lacides Ripoll & Jose Soto & Giovanny Salazar & Santiago Cantillo, 2020. "Prediction of Wind Speed Using Hybrid Techniques," Energies, MDPI, vol. 13(23), pages 1-13, November.
    2. Tang, Li-Hong & Bai, Yu-Long & Yang, Jie & Lu, Ya-Ni, 2020. "A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).

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