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Hybrid model for renewable energy and loads prediction based on data mining and variational mode decomposition

Hybrid model for renewable energy and loads prediction based on data mining and variational mode decomposition

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Accurate renewable resource and load forecasting plays a key role in the progress of power grid planning schemes. In this study, a hybrid short-term forecasting method based K-means clustering and variational mode decomposition (VMD) technique is proposed to deal with the problem of forecasting accuracy. K-means clustering is a means of data mining approach and used for classifying data into several clusters. A cluster selection method is adopted to extract similar features from historical days. To better analyse the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies. Self-adaptive evolutionary extreme learning machine as a novel and fast regression tool is trained and used for predicting each component. Eventually, the forecasting result generated by reconstructing all the predicted components values. The performance of the proposed hybrid forecasting model is evaluated by using real data from National Renewable Energy Laboratory. The simulation results show that it can obtain better forecasting accuracy than some previously reported methods.

References

    1. 1)
      • 1. Huang, J., Jiang, C., Xu, R.: ‘A review on distributed energy resources and MicroGrid’, Renew. Sustain. Energy Rev., 2008, 12, (9), pp. 24722483.
    2. 2)
      • 2. Roulston, M.S., Kaplan, D.T., Hardenberg, J., et al: ‘Using medium-range weather forecasts to improve the value of wind energy production’, Renew. Energy, 2003, 28, (4), pp. 585602.
    3. 3)
      • 3. Xu, Q., He, D., Zhang, N., et al: ‘A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining’, IEEE Trans. Sustain. Energy, 2015, 6, (4), pp. 12831291.
    4. 4)
      • 4. Chang, G.W., Lu, H.J., Chang, Y.R., et al: ‘An improved neural network-based approach for short-term wind speed and power forecast’, Renew. Energy, 2017, 105, (1), pp. 301311.
    5. 5)
      • 5. Huang, S.R.: ‘Short-Term load forecasting using threshold autoregressive models’, IET Renew. Power Gener., 1997, 144, (5), pp. 477481.
    6. 6)
      • 6. Chen, P., Pedersen, T., Bak-Jensen, B., et al: ‘ARIMA-based time series model of stochastic wind power generation’, IEEE Trans. Power Syst., 2010, 25, (2), pp. 667676.
    7. 7)
      • 7. Lydia, M., Kumar, S.S., Selvakumar, A.I., et al: ‘Linear and non-linear autoregressive models for short-term wind speed forecasting’, Energy Convers. Manage., 2016, 112, (1), pp. 115124.
    8. 8)
      • 8. Huang, R., Huang, T., Gadh, R., et al: ‘Solar generation prediction using the ARMA model in a laboratory-level micro-grid’. Proc. 3rd IEEE Int. Conf. Smart Grid Commun., Tainan, Taiwan, 5–8 November 2012, pp. 528533.
    9. 9)
      • 9. Ssekulima, E.B., Anwar, M.B., Al Hinai, A., et al: ‘Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review’, IET Renew. Power Gener., 2016, 10, (7), pp. 885989.
    10. 10)
      • 10. Giraud, F., Salameh, Z.M.: ‘Analysis of the effects of a passing cloud on a grid-interactive photovoltaic system with battery storage using neural networks’, IEEE Trans. Energy Convers., 1999, 14, (4), pp. 15721577.
    11. 11)
      • 11. Mabel, M., Fernandez, E.: ‘Analysis of wind power generation and prediction using ANN: a case study’, Renew. Energy, 2008, 33, (5), pp. 986992.
    12. 12)
      • 12. Zhang, R., Dong, Z. Y., Xu, Y., et al: ‘Short-term load forecasting of Australian national electricity market by an ensemble model of extreme learning machine’, IET Gener. Transm. Distrib., 2013, 7, (4), pp. 391397.
    13. 13)
      • 13. Ren, C., An, N., Wang, J., et al: ‘Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting’, Knowl. Based Syst., 2014, 56, (1), pp. 226239.
    14. 14)
      • 14. Li, H.Z., Guo, S., Li, C.J., et al: ‘A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm’, Knowl. Based Syst., 2013, 37, (1), pp. 378387.
    15. 15)
      • 15. Salcedo-Sanz, S., Pastor-Sanchez, A., Del, J., et al: ‘A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction’, Renew. Energy, 2015, 75, pp. 93101.
    16. 16)
      • 16. Lorenzo, J., Méndez, J., Castrillón, M., et al: ‘Short-term wind power forecast based on cluster analysis and artificial neural networks’. Proc. of the 11th Int. Work-Conf. on Artificial Neural Networks, 8–10 June 2011, pp. 191198,.
    17. 17)
      • 17. Zhang, X., Wang, R., Liao, T., et al: ‘Short-term fore-casting of wind power generation based on the similar day and Elman neural network’. Proc. IEEE SSCI, Cape Town, South Africa, 7–10 December 2015, pp. 647650.
    18. 18)
      • 18. Conejo, A.J., Plazas, M.A., Espínola, R., et al: ‘Day-ahead electricity price forecasting using the wavelet transform and ARIMA models’, IEEE Trans. Power Syst., 2005, 20, (2), pp. 10351042.
    19. 19)
      • 19. Lave, M., Kleissl, J., Stein, J.: ‘A wavelet-based variability model (WVM) for solar PV power plants’, IEEE Trans. Sustain. Energy, 2013, 4, (2), pp. 501509.
    20. 20)
      • 20. Ren, Y., Suganthan, P.N., Srikanth, N.: ‘A novel empirical mode decomposition with support vector regression for wind speed forecasting’, IEEE Trans. Neural Netw. Learn. Syst., 2016, 27, (8), pp. 17931798.
    21. 21)
      • 21. Dragomiretskiy, K., Zosso, D.: ‘Variational mode decomposition’, IEEE Trans. Signal Process., 2014, 62, (3), pp. 531544.
    22. 22)
      • 22. Huang, G.B., Zhu, Q.Y., Siew, C.K.: ‘Extreme learning machine: theory and applications’, Neurocomputing, 2006, 70, (13), pp. 489501.
    23. 23)
      • 23. Cao, J., Lin, Z., Huang, G.B.: ‘Self-adaptive evolutionary extreme machine learning’, Neural Process. Lett., 2012, 36, (3), pp. 285305.
    24. 24)
      • 24. Qin, A.K., Huang, V.L., Suganthan, P.N.: ‘Differential evolution algorithm with strategy adaptation for global numerical optimization’, IEEE Trans. Evol. Comput., 2009, 13, (2), pp. 398417.
    25. 25)
      • 25. ‘National Renewable Energy Laboratory’, http://www.nrel.gov, accessed 10 November 2016.
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