Skip to main content

Advertisement

SpringerLink
Log in

Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm

  • Original paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

Accurate electric load forecasting can provide critical support to makers of energy policy and managers of power systems. The support vector regression (SVR) model can be hybridized with novel meta-heuristic algorithms not only to identify fluctuations and the nonlinear tendencies of electric loads, but also to generate satisfactory forecasts. However, many such algorithms have numerous drawbacks, such as a low population diversity and trapping at local optima, which are problems of premature convergence. Accordingly, approaches to increase the accuracy of forecasting must be developed. In this investigation, quantum computing mechanism is used to quantamize dragonfly behaviors to enhance the searching effectiveness of the dragonfly algorithm, namely QDA. In addition, conducting the data preprocessing by the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) is useful to improve the forecasting accuracy. Thus, a new electric load forecasting model, the CEEMDAN-SVRQDA model, that combines the CEEMDAN and hybridizes the QDA with an SVR model, is proposed to provide more accurate forecasts. Two numerical examples from the Tokyo Electric Power Company (Japan) and the National Grid (UK) demonstrate that the proposed model outperforms other models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Ahmad, T., Chen, H.: Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems. Sustain. Cities Soc. 39, 460–473 (2019). https://doi.org/10.1016/j.scs.2018.12.013

    Article  Google Scholar 

  2. Xiao, L., Shao, W., Liang, T., Wang, C.: A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting. Appl. Energy 167, 135–153 (2016). https://doi.org/10.1016/j.apenergy.2016.01.050

    Article  Google Scholar 

  3. Fan, G.F., Peng, L.L., Zhao, X., Hong, W.C.: Applications of hybrid EMD with PSO and GA for an SVR-based load forecasting model. Energies 10, 1713 (2017). https://doi.org/10.3390/en10111713

    Article  Google Scholar 

  4. O’Connell, N., Pinson, P., Madsen, H., O’Malley, M.: Benefits and challenges of electrical demand response: a critical review. Renew. Sustain. Energy Rev. 39, 686–699 (2014). https://doi.org/10.1016/j.rser.2014.07.098

    Article  Google Scholar 

  5. Hong, W.C., Dong, Y., Zhang, W.Y., Chen, L.Y., Panigrahi, B.K.: Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm. Int. J. Electr. Power Energy Syst. 44, 604–614 (2013). https://doi.org/10.1016/j.ijepes.2012.08.010

    Article  Google Scholar 

  6. Fan, G., Peng, L.L., Hong, W.C., Sun, F.: Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing 173, 958–970 (2016). https://doi.org/10.1016/j.neucom.2015.08.051

    Article  Google Scholar 

  7. Sen, P., Roy, M., Pal, P.: Application of ARIMA for forecasting energy consumption and GHG emission: a case study of an Indian pig iron manufacturing organization. Energy 116, 1031–1038 (2016). https://doi.org/10.1016/j.energy.2016.10.068

    Article  Google Scholar 

  8. Yang, D., Sharma, V., Ye, Z., Lim, L.I., Zhao, L., Aryaputera, A.W.: Forecasting of global horizontal irradiance by exponential smoothing, using decompositions. Energy 81, 111–119 (2015). https://doi.org/10.1016/j.energy.2014.11.082

    Article  Google Scholar 

  9. Li, Y., Jiang, X., Zhu, H., He, X., Peeta, S.: Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory. Nonlinear Dyn. 85, 179–194 (2016). https://doi.org/10.1007/s11071-016-2677-5

    Article  MathSciNet  MATH  Google Scholar 

  10. Takeda, H., Tamura, Y., Sato, S.: Using the ensemble Kalman filter for electricity load forecasting and analysis. Energy 104, 184–198 (2016). https://doi.org/10.1016/j.energy.2016.03.070

    Article  Google Scholar 

  11. Lebotsa, M.E., Sigauke, C., Bere, A., Fildes, R., Boylan, J.E.: Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem. Appl. Energy 222, 104–118 (2018). https://doi.org/10.1016/j.apenergy.2018.03.155

    Article  Google Scholar 

  12. Kelo, S., Dudul, S.: A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature. Int. J. Electr. Power Energy Syst. 43, 1063–1071 (2012). https://doi.org/10.1016/j.ijepes.2012.06.009

    Article  Google Scholar 

  13. Singh, P., Dwivedi, P.: Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem. Appl. Energy 217, 537–549 (2018). https://doi.org/10.1016/j.apenergy.2018.02.131

    Article  Google Scholar 

  14. Hernández, L., Baladrón, C., Aguiar, J.M., Carro, B., Sánchez-Esguevillas, A., Lloret, J.: Artificial neural networks for short-term load forecasting in microgrids environment. Energy 75, 252–264 (2014). https://doi.org/10.1016/j.energy.2014.07.065

    Article  Google Scholar 

  15. Lusis, P., Khalilpour, K.R., Andrew, L., Liebman, A.: Short-term residential load forecasting: impact of calendar effects and forecast granularity. Appl. Energy 205, 654–669 (2017). https://doi.org/10.1016/j.apenergy.2017.07.114

    Article  Google Scholar 

  16. Duan, Q., Liu, J., Zhao, D.: Short term electric load forecasting using an automated system of model choice. Int. J. Electr. Power Energy Syst. 91, 92–100 (2017). https://doi.org/10.1016/j.ijepes.2017.03.006

    Article  Google Scholar 

  17. Zhang, W., Zhang, S., Zhang, S.: Two-factor high-order fuzzy-trend FTS model based on BSO–FCM and improved KA for TAIEX stock forecasting. Nonlinear Dyn. 94, 1429–1446 (2018). https://doi.org/10.1007/s11071-018-4433-5

    Article  Google Scholar 

  18. Lou, C.W., Dong, M.C.: A novel random fuzzy neural networks for tackling uncertainties of electric load forecasting. Int. J. Electr. Power Energy Syst. 73, 34–44 (2015). https://doi.org/10.1016/j.ijepes.2015.03.003

    Article  Google Scholar 

  19. Hua, J.C., Noorian, F., Moss, D., Leong, P.H.W., Gunaratne, G.H.: High-dimensional time series prediction using kernel-based Koopman mode regression. Nonlinear Dyn. 90, 1785–1806 (2017). https://doi.org/10.1007/s11071-017-3764-y

    Article  MATH  Google Scholar 

  20. Fan, G.F., Peng, L.L., Hong, W.C.: Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model. Appl. Energy 224, 13–33 (2018). https://doi.org/10.1016/j.apenergy.2018.04.075

    Article  Google Scholar 

  21. Zhai, M.Y.: A new method for short-term load forecasting based on fractal interpretation and wavelet analysis. Int. J. Electr. Power Energy Syst. 69, 241–245 (2015). https://doi.org/10.1016/j.ijepes.2014.12.087

    Article  Google Scholar 

  22. Niu, M., Sun, S., Wu, J., Yu, L., Wang, J.: An innovative integrated model using the singular spectrum analysis and nonlinear multi-layer perceptron network optimized by hybrid intelligent algorithm for short-term load forecasting. Appl. Math. Model. 40, 4079–4093 (2016). https://doi.org/10.1016/j.apm.2015.11.030

    Article  MathSciNet  Google Scholar 

  23. Boubaker, S.: Identification of nonlinear Hammerstein system using mixed integer-real coded particle swarm optimization: application to the electric daily peak-load forecasting. Nonlinear Dyn. 90, 797–814 (2017). https://doi.org/10.1007/s11071-017-3693-9

    Article  Google Scholar 

  24. Aras, S., Kocakoç, İ.D.: A new model selection strategy in time series forecasting with artificial neural networks: IHTS. Neurocomputing 174, 974–987 (2016). https://doi.org/10.1016/j.neucom.2015.10.036

    Article  Google Scholar 

  25. Panapakidis, I.P., Dagoumas, A.S.: Day-ahead electricity price forecasting via the application of artificial neural network based models. Appl. Energy 172, 132–151 (2016). https://doi.org/10.1016/j.apenergy.2016.03.089

    Article  Google Scholar 

  26. Lahmiri, S.: Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Appl. Math. Comput. 320, 444–451 (2018). https://doi.org/10.1016/j.amc.2017.09.049

    Article  MathSciNet  MATH  Google Scholar 

  27. Hong, W.C.: Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 74, 2096–2107 (2011). https://doi.org/10.1016/j.neucom.2010.12.032

    Article  Google Scholar 

  28. Hong, W.C.: Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting. Neural Comput. Appl. 21, 583–593 (2012). https://doi.org/10.1007/s00521-010-0456-7

    Article  Google Scholar 

  29. Hong, W.C., Dong, Y., Zheng, F., Wei, S.Y.: Hybrid evolutionary algorithms in a SVR traffic flow forecasting model. Appl. Math. Comput. 217, 6733–6747 (2011). https://doi.org/10.1016/j.amc.2011.01.073

    Article  MathSciNet  MATH  Google Scholar 

  30. Chen, R., Liang, C.Y., Hong, W.C., Gu, D.X.: Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl. Soft Comput. 26, 435–443 (2015). https://doi.org/10.1016/j.asoc.2014.10.022

    Article  Google Scholar 

  31. Hong, W.C., Dong, Y., Chen, L.Y., Wei, S.Y.: SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Appl. Soft Comput. 11, 1881–1890 (2011). https://doi.org/10.1016/j.asoc.2010.06.003

    Article  Google Scholar 

  32. Yu, P.S., Yang, T.C., Chen, S.Y., Kuo, C.M., Tseng, H.W.: Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting. J. Hydrol. 552, 92–104 (2017). https://doi.org/10.1016/j.jhydrol.2017.06.020

    Article  Google Scholar 

  33. Xiang, Y., Gou, L., He, L., Xia, S., Wang, W.: A SVR-ANN combined model based on ensemble EMD for rainfall prediction. Appl. Soft Comput. 73, 874–883 (2018). https://doi.org/10.1016/j.asoc.2018.09.018

    Article  Google Scholar 

  34. Fan, G., Wang, H., Qing, S., Hong, W.C., Li, H.J.: Support vector regression model based on empirical mode decomposition and auto regression for electric load forecasting. Energies 6, 1887–1901 (2013). https://doi.org/10.3390/en6041887

    Article  Google Scholar 

  35. Geng, J., Huang, M.L., Li, M.W., Hong, W.C.: Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model. Neurocomputing 151, 1362–1373 (2015). https://doi.org/10.1016/j.neucom.2014.10.055

    Article  Google Scholar 

  36. Hong, W.C., Dong, Y., Lai, C.Y., Chen, L.Y., Wei, S.Y.: SVR with hybrid chaotic immune algorithm for seasonal load demand forecasting. Energies 4, 960–977 (2011). https://doi.org/10.3390/en4060960

    Article  Google Scholar 

  37. Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimization algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017). https://doi.org/10.1016/j.advengsoft.2017.01.004

    Article  Google Scholar 

  38. Ks, S.R., Murugan, S.: Memory based hybrid dragonfly algorithm for numerical optimization problems. Expert Syst. Appl. 83, 63–78 (2017). https://doi.org/10.1016/j.eswa.2017.04.033

    Article  Google Scholar 

  39. Prawin, J., Rao, A.R.M., Lakshmi, K.: Nonlinear parametric identification strategy combining reverse path and hybrid dynamic quantum particle swarm optimization. Nonlinear Dyn. 84, 797–815 (2017). https://doi.org/10.1007/s11071-015-2528-9

    Article  MathSciNet  Google Scholar 

  40. Huang, M.L.: Hybridization of chaotic quantum particle swarm optimization with SVR in electric demand forecasting. Energies 9, 426 (2016). https://doi.org/10.3390/en9060426

    Article  Google Scholar 

  41. Peng, L.L., Fan, G.F., Huang, M.L., Hong, W.C.: Hybridizing DEMD and quantum PSO with SVR in electric load forecasting. Energies 9, 221 (2016). https://doi.org/10.3390/en9030221

    Article  Google Scholar 

  42. Li, M.W., Geng, J., Wang, S., Hong, W.C.: Hybrid chaotic quantum bat algorithm with SVR in electric load forecasting. Energies 10, 2180 (2017). https://doi.org/10.3390/en10122180

    Article  Google Scholar 

  43. Li, M.W., Geng, J., Hong, W.C., Zhang, Y.: Hybridizing chaotic and quantum mechanisms and fruit fly optimization algorithm with least squares support vector regression model in electric load forecasting. Energies 11, 2226 (2018). https://doi.org/10.3390/en11092226

    Article  Google Scholar 

  44. Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27, 1053–1073 (2016). https://doi.org/10.1007/s00521-015-1920-1

    Article  Google Scholar 

  45. Chen, B.J., Chang, M.W.: Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Trans. Power Syst. 19(4), 1821–1830 (2004). https://doi.org/10.1109/TPWRS.2004.835679

    Article  MathSciNet  Google Scholar 

  46. Pai, P.F., Hong, W.C.: Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers. Manag. 46(17), 2669–2688 (2005). https://doi.org/10.1016/j.enconman.2005.02.004

    Article  Google Scholar 

  47. Pai, P.F., Hong, W.C.: Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electr. Power Syst. Res. 74(3), 417–425 (2005). https://doi.org/10.1016/j.epsr.2005.01.006

    Article  Google Scholar 

  48. Dey, S., Bhattacharyya, S., Maulik, U.: Quantum inspired genetic algorithm and particle swarm optimization using chaotic map model based interference for gray level image thresholding. Swarm Evol. Comput. 15, 38–57 (2014). https://doi.org/10.1016/j.swevo.2013.11.002

    Article  Google Scholar 

  49. Cortés, M.A.D., Ortega-Sánchez, N., Hinojosa, S., Oliva, D., Cuevas, E., Rojas, R., Demin, A.: A multi-level thresholding method for breast thermograms analysis using dragonfly algorithm. Infrared Phys. Technol. 93, 346–361 (2018). https://doi.org/10.1016/j.infrared.2018.08.007

    Article  Google Scholar 

  50. Mafarja, M., Aljarah, I., Heidari, A.A., Faris, H., Fournier-Viger, P., Li, X., Mirjalili, S.: Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl. Based Syst. 161, 185–204 (2018). https://doi.org/10.1016/j.knosys.2018.08.003

    Article  Google Scholar 

  51. Jafari, M., Chaleshtari, M.H.B.: Using dragonfly algorithm for optimization of orthotropic infinite plates with a quasi-triangular cut-out. Eur. J. Mech. A. Solids 66, 1–14 (2017). https://doi.org/10.1016/j.euromechsol.2017.06.003

    Article  MathSciNet  MATH  Google Scholar 

  52. Ghanem, W.A.H.M., Jantan, A.: A cognitively inspired hybridization of artificial bee colony and dragonfly algorithms for training multi-layer perceptrons. Cogn. Comput. 10(6), 1096–1134 (2018). https://doi.org/10.1007/s12559-018-9588-3

    Article  Google Scholar 

  53. Hida, T.: Brownian Motion. Springer, New York (1980). https://doi.org/10.1007/978-1-4612-6030-1

    Book  MATH  Google Scholar 

  54. El-Nabulsi, R.A.: The fractional Boltzmann transport equation. Comput. Math. Appl. 62(3), 1568–1575 (2011). https://doi.org/10.1016/j.camwa.2011.03.040

    Article  MathSciNet  MATH  Google Scholar 

  55. Hakli, H., Uguz, H.: A novel particle swarm optimization algorithm with Levy flight. Appl. Soft Comput. 23, 333–345 (2014). https://doi.org/10.1016/j.asoc.2014.06.034

    Article  Google Scholar 

  56. Yang, X.: Firefly algorithm, Levy flights and global optimization. In: Bramer, M., Ellis, R., Petridis, M. (eds.) Research and Development in Intelligent Systems, vol. XXVI, pp. 209–218. Springer, London (2010). https://doi.org/10.1007/978-1-84882-983-1_15

    Chapter  Google Scholar 

  57. Heidari, A., Pahlavani, P.: An efficient modified grey wolf optimizer with Lévy flight for optimization tasks. Appl. Soft Comput. 60, 115–134 (2017). https://doi.org/10.1016/j.asoc.2017.06.044

    Article  Google Scholar 

  58. Barthelemy, P., Bertolotti, J., Wiersma, D.S.: A Lévy flight for light. Nature 453, 495–498 (2008). https://doi.org/10.1038/nature06948

    Article  Google Scholar 

  59. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. A Math. Phys. Eng. Sci. 454(1971), 903–995 (1998). https://doi.org/10.1098/rspa.1998.0193

    Article  MathSciNet  MATH  Google Scholar 

  60. Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1, 1–41 (2009). https://doi.org/10.1142/S1793536909000047

    Article  Google Scholar 

  61. Wang, J., Luo, Y., Tang, L., Ge, P.: A new weighted CEEMDAN-based prediction model: an experimental investigation of decomposition and non-decomposition approaches. Knowl. Based Syst. 160, 188–199 (2018). https://doi.org/10.1016/j.enconman.2017.01.022

    Article  Google Scholar 

  62. Yeh, J.R., Shieh, J.S., Huang, N.E.: Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Adv. Adapt. Data Anal. 2, 135–156 (2010). https://doi.org/10.1142/S1793536910000422

    Article  MathSciNet  Google Scholar 

  63. Torres, ME., Colominas, MA., Schlotthauer, G., Flandrin, P.: A complete ensemble empirical mode decomposition with adaptive noise. In: Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4144–4147 (2011) https://doi.org/10.1109/ICASSP.2011.5947265

  64. Carvalho, F.A.T.D., Neto, E.A.L., Ferreira, M.R.P.: A robust regression method based on exponential-type kernel functions. Neurocomputing 234, 58–74 (2017). https://doi.org/10.1016/j.neucom.2016.12.035

    Article  Google Scholar 

  65. Ranjini, K.S.S., Murugan, S.: Memory based hybrid dragonfly algorithm for numerical optimization problems. Expert Syst. Appl. 83, 63–78 (2017). https://doi.org/10.1016/j.eswa.2017.04.033

    Article  Google Scholar 

  66. National Grid UK. https://www.nationalgrid.com/uk

  67. Tokyo Electric Power Company. https://www4.tepco.co.jp/index-e.html

  68. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1, 3–18 (2011). https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

Download references

Acknowledgements

This research was conducted with the support from Jiangsu Normal University (No. 9213618401), China.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Jiangsu Normal University, No. 101, Shanghai Rd., Tongshan District, Xuzhou, 221116, Jiangsu, China

    Zichen Zhang & Wei-Chiang Hong

Authors
  1. Zichen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei-Chiang Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei-Chiang Hong.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Hong, WC. Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dyn 98, 1107–1136 (2019). https://doi.org/10.1007/s11071-019-05252-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-019-05252-7

Keywords

Search

Navigation