Wind Speed Forecasting in Different Seasons Using ELM Batch Learning Algorithm in Indian Context

  • Authors

    • Rashmi. P. Shetty
    • A Sathyabhama
    • Srinivasa. Pai. P
    • Ranjith Shetty K
    https://doi.org/10.14419/ijet.v7i3.34.19456
  • Wind speed forecasting, Seasonal model, Wavelet denoising, PACF, ELM

  • Efficient wind speed forecasting is important for wind energy sector for better wind power integration. This paper focuses on developing seasonal wind speed forecasting models in Indian context. Wavelet transform (WT) technique has been used for denoising the data obtained from supervisory control and data acquisition (SCADA) of a 1.5 MW wind turbine located in central dry zone of Karnataka, to reduce the unnecessary fluctuations in the wind speed time series. Partial auto correlation function (PACF) has been used for selection of input parameters, which greatly influences the forecasting accuracy. Forecasting models have been developed using a fast and efficient extreme learning machine (ELM) algorithm. The results have been compared with conventional back propagation (BP) algorithm. The results show that the seasonal models developed using ELM have better forecasting performance compared to BP.

     

  • References

    1. [1] X. Y. Yang and G. S. Liang, “Development of wind power generation and its market prospect,†Power System Technology, 27(7), pp.78-79, 2003.

      [2] P. Ramasamy, S.S. Chandel and A.K. Yadav, “Wind speed prediction in the mountainous region of India using an artificial neural network model,†Renewable Energy, vol.80, pp.338-347, 2015.

      [3] G. Shrimali, S.Trivedi, S. Srinivasan, S. Goel and D. Nelson, “Cost-effective policies for reaching India's 2022 renewable targets,†Renewable Energy, vol. 93, pp.255-268, 2016.

      [4] G. Sideratos and N.D. Hatziargyriou, “An advanced statistical method for wind power forecasting,†IEEE Transactions on power systems, vol. 22(1), pp.258-265, 2007.

      [5] X. Wang and L.I. Hui, “Multiscale prediction of wind speed and output power for the wind farm,†Journal of Control Theory and Applications, vol. 10(2), pp.251-258, 2012.

      [6] P. Gomes and R. Castro, “Wind speed and wind power forecasting using statistical models: autoregressive moving average (ARMA) and artificial neural networks (ANN),†International Journal of Sustainable Energy Development, vol.1 2012.

      [7] E. Pelikan, K. Eben, J. Resler, P. JuruÅ¡, P. KrÄ, M. Brabec, T. Brabec and P. May, “Wind power forecasting by an empirical model using NWP outputs,†In Environment and Electrical Engineering (EEEIC), 2010, 9th International Conference on, 2010, pp. 45-48

      [8] E. Erdem and J. Shi, “ARMA based approaches for forecasting the tuple of wind speed and directionâ€. Applied Energy, vol. 88(4), pp.1405-1414, 2011.

      [9] U. Schlink, and G. Tetzlaff, “Wind speed forecasting from 1 to 30 minutesâ€, Theoretical and applied climatology, vol. 60 (1-4), pp.191-198, 1998.

      [10] M. Lydia, S.S. Kumar, A.I. Selvakumar and G.E.P. Kumar, “Linear and non-linear autoregressive models for short-term wind speed forecasting,†Energy Conversion and Management, vol. 112, pp.115-124, 2016.

      [11] G.H. Guo, J. Wu, H.Y. Lu and J.Z. Wang, “ A case study on a hybrid wind speed forecasting method using BP neural network,†Knowledge-based systems, vol. 24(7), pp.1048-1056, 2011.

      [12] G. Li and J. Shi, “On comparing three artificial neural networks for wind speed forecasting,†Applied Energy, vol. 87(7), pp.2313-2320, 2010.

      [13] T.G. Barbounis, J.B. Theocharis, M.C. Alexiadis and P.S. Dokopoulos, “Long-term wind speed and power forecasting using local recurrent neural network models,†IEEE Transactions on Energy Conversion, vol. 21(1), pp.273-284, 2006.

      [14] J. Hu, J. Wang and G. Zeng, “A hybrid forecasting approach applied to wind speed time series,†Renewable Energy, vol. 60, pp.185-194, 2013.

      [15] H. Liu, C. Chen, H.Q. Tian and Y. F. Li, “A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks,†Renewable Energy, vol. 48, pp.545-556, 2012.

      [16] H. Li, J. Wang, H. Lu and Z. Guo, “Research and application of a combined model based on variable weight for short term wind speed forecasting,â€. Renewable Energy, vol. 116, pp.669-684, 2018.

      [17] G. B. Huang, Q.Y. Zhu and C.K. Siew, “Extreme learning machine: theory and applications,†Neurocomputing, vol. 70(1-3), pp.489-501, 2016.

      [18] H. Liu, H.Q. Tian and Y.F. Li, “Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms,†Energy Conversion and Management, vol.100, pp.16-22, 2015.

      [19] X. Wang, Y. Zheng, L. Li, L. Zhou, G. Yao and T. Huang, “Short-term wind power prediction based on wavelet decomposition and extreme learning machine,†In International Symposium on Neural Networks, 2012. pp. 645-653.

      [20] C. Zhang, J. Zhou, C. Li, W. Fu and T. Peng, “A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting,†Energy Conversion and Management, vol. 143, pp.360-376, 2017.

      [21] Z. Guo, W. Zhao, H. Lu and J. Wang, “Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model,†Renewable Energy, vol. 37(1), pp.241-249, 2012.

      [22] C. Feng, M. Cui, B.M. Hodge and J. Zhang, “A data-driven multi-model methodology with deep feature selection for short-term wind forecasting,†Applied Energy, vol. 190, pp.1245-1257, 2017.

      [23] H. Liu, Z. Duan, F.Z. Han and Y.F.Li, “Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm,†Energy Conversion and Management, vol. 156, pp.525-541, 2018.

      [24] T. Nakai and K. Shimoyama, “Ultrasonic anemometer angle of attack errors under turbulent conditions,†Agricultural and forest meteorology, vol. 162, pp.14-26, 2012.

      [25] E. I. Kaganov and A.M. Yaglom, “Errors in wind-speed measurements by rotation anemometers,†Boundary-Layer Meteorology, vol. 10(1), pp.15-34, 1976.

      [26] Q. Lin, J. Wang, and W. Qiao, “November. Denoising of wind speed data by wavelet thresholding,†In Chinese Automation Congress (CAC), 2013, pp. 518-521.

      [27] D. Liu, D. Niu, H. Wang and L. Fan, L, “Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm,†Renewable Energy, vol. 62, pp.592-597, 2014.

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  • How to Cite

    P. Shetty, R., Sathyabhama, A., Pai. P, S., & Shetty K, R. (2018). Wind Speed Forecasting in Different Seasons Using ELM Batch Learning Algorithm in Indian Context. International Journal of Engineering & Technology, 7(3.34), 705-709. https://doi.org/10.14419/ijet.v7i3.34.19456