Solar and Wind Power Forecasting with Optimal ARIMA Parameters

  • Authors

    • M Veda Swaroop
    • P Linga Reddy
    2018-02-09
    https://doi.org/10.14419/ijet.v7i1.8.16402
  • ARIMA, Forecasting, Renewable energy integration, Solar power, Wind power.

  • Abstract

    The solar and wind renewable energy sources are gaining popularity to encourage green energy into the power system. The cost of generation of solar and wind energy sources are decreasing and competing with conventional coal-based generation. Therefore, it is very important to integrate these renewable sources into the power system. Integrating Solar and wind energy sources require to solve the uncertainty problem. Both the solar and wind energy generation is uncertain and not controllable. In this paper, sliding window optimal ARIMA forecasting algorithm is proposed to solve the uncertainty associated with solar and wind sources. The proposed forecasting method is used on the data collected from National Renewable Energy Laboratory website.

     

     

  • References

    1. [1] M. Beaudin, H. Zareipour, A. Schellenberg, W. Rosehart, Energy Storage for Mitigating the Variability of Renewable Electricity Sources, Energy Storage Smart Grids Plan. Oper. Renew. Var. Energy Resour. 14 (2014) 1–33. doi:10.1016/B978-0-12-410491-4.00001-4.

      [2] D. Van Der Meer, G.R.C. Mouli, G.M.E. Mouli, L.R. Elizondo, P. Bauer, Energy Management System with PV Power Forecast to Optimally Charge EVs at the Workplace, IEEE Trans. Ind. Informatics. 14 (2018) 311–320. doi:10.1109/TII.2016.2634624.

      [3] D. Kwiatkowski, P.C.B. Phillips, P. Schmidt, Y. Shin, Testing the null hypothesis of stationarity against the alternative of a unit root, J. Econom. 54 (1992) 159–178. doi:10.1016/0304-4076(92)90104-Y.

      [4] KPSS Test, available online: http://in.mathworks.com/help/econ/kpsstest.html, last visit:22.04.2018

      [5] H. Akaike, A New Look at the Statistical Model Identification, IEEE Trans. Automat. Contr. 19 (1974) 716–723. doi:10.1109/TAC.1974.1100705.

      [6] A.I.C Test, available online: http://in.mathworks.com/help/ident/ref/aic.html, last visit:22.04.2018

      [7] NREL Solar dataset, available online: https://www.nrel.gov/grid/solar-power-data.html, last visit:22.04.2018

      [8] NREL Wind dataset, available online: https://www.nrel.gov/grid/wind-integration-data.html, last visit:22.04.2018

      [9] a. Sfetsos, C. Siriopoulos, Time Series Forecasting of Averaged Data With Efficient Use of Information, IEEE Trans. Syst. Man, Cybern. - Part A Syst. Humans. 35 (2005) 738–745. doi:10.1109/TSMCA.2005.851133.

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  • How to Cite

    Veda Swaroop, M., & Linga Reddy, P. (2018). Solar and Wind Power Forecasting with Optimal ARIMA Parameters. International Journal of Engineering & Technology, 7(1.8), 201-203. https://doi.org/10.14419/ijet.v7i1.8.16402

    Received date: 2018-07-27

    Accepted date: 2018-07-27

    Published date: 2018-02-09