An improved back propagation neural network based on complexity decomposition technology and modified flower pollination optimization for short-term load forecasting

  • Lina Pan
  • Xiaosu Feng
  • Fawen Sang
  • Longjie Li
  • Mingwei Leng
  • Xiaoyun Chen
Original Article
  • 154 Downloads

Abstract

Accurate short-term load forecasting (STLF) is crucial for reliable operation of a power system. Back propagation neural network (BPNN) is widely used in the forecasting field because of its powerful approximation capability. However, due to a variety of unstable factors, electrical time series often exhibit highly noisy and nonlinear characteristics. Usually, a large deviation will be produced when employing single BPNN to capture the complex data pattern. To solve this problem, this paper proposes a new hybrid forecasting approach that combines ensemble empirical mode decomposition (EEMD), chaotic self-adaptive flower pollination algorithm (CSFPA) and BPNN. EEMD is employed to decompose the original load series with the purpose of reducing the forecasting complexity. Developed CSFPA uses logistic equation to produce the chaotic initial population. In addition, aiming at providing a better optimization capability, CSFPA calculates the self-adaptive switch probability at each iteration. The best initial weights and biases of BPNN are provided by the optimization result of CSFPA. The performance of the proposed method is validated by two real-world load data sets from different electricity markets. The numerical results demonstrate that the proposed method outperforms three advanced methods; it is an effective and promising method for STLF.

Keywords

Short-term load forecasting Hybrid forecasting method Complexity decomposition Back propagation neural network Modified flower pollination algorithm 

Abbreviations

STLF

Short-term load forecasting

ANN

Artificial neural network

BPNN

Back propagation neural network

SVR

Support vector regression

SOM

Self-organized map

RBF

Radial basis function

ARIMA

Autoregressive integrated moving average

SARIMA

Seasonal autoregressive integrated moving average

EMD

Empirical mode decomposition

EEMD

Ensemble empirical mode decomposition

IMF

Intrinsic mode function

WT

Wavelet transform

PSO

Particle swarm optimization

SSO

Shark smell optimization

FPA

Flower pollination algorithm

HBMO

Honey bee mating optimization

MA

Memetic algorithm

MAE

Mean absolute error

RMSE

Root-mean-square error

MAPE

Mean absolute percentage error

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 61602225) and Introduce Talents Science Projects for Northwest University for Nationalities (No. xbmuyjrcs201616).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    2012 India blackouts (2012). Wikipedia. https://en.wikipedia.org/wiki/2012_India_blackouts. Accessed 1 Aug 2017
  2. 2.
    Four Nigerian states in total darkness as national grid collapses (2016). P.M. News. https://www.pmnewsnigeria.com/2016/06/20/four-nigerian-states-in-total-darkness-as-national-grid-collapses/. Accessed 1 Aug 2017
  3. 3.
    Deihimi A, Orang O, Showkati H (2013) Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy 57:382–401. doi: 10.1016/j.energy.2013.06.007 CrossRefGoogle Scholar
  4. 4.
    Kavousi-Fard A, Samet H, Marzbani F (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41(13):6047–6056. doi: 10.1016/j.eswa.2014.03.053 CrossRefGoogle Scholar
  5. 5.
    Taylor JW (2012) Short-term load forecasting with exponentially weighted methods. IEEE Trans Power Syst 27(1):458–464. doi: 10.1109/TPWRS.2011.2161780 MathSciNetCrossRefGoogle Scholar
  6. 6.
    Bianco V, Manca O, Nardini S (2009) Electricity consumption forecasting in Italy using linear regression models. Energy 34(9):1413–1421. doi: 10.1016/j.energy.2009.06.034 CrossRefGoogle Scholar
  7. 7.
    Vu DH, Muttaqi KM, Agalgaonkar AP (2015) A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables. Appl Energy 140:385–394. doi: 10.1016/j.apenergy.2014.12.011 CrossRefGoogle Scholar
  8. 8.
    Al-Hamadi HM, Soliman SA (2004) Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model. Electr Power Syst Res 68(1):47–59. doi: 10.1016/S0378-7796(03)00150-0 CrossRefGoogle Scholar
  9. 9.
    Guan C, Luh PB, Michel LD, Chi Z (2013) Hybrid Kalman filters for very short-term load forecasting and prediction interval estimation. IEEE Trans Power Syst 28(4):3806–3817. doi: 10.1109/TPWRS.2013.2264488 CrossRefGoogle Scholar
  10. 10.
    Shyh-Jier H, Kuang-Rong S (2003) Short-term load forecasting via ARMA model identification including non-Gaussian process considerations. IEEE Trans Power Syst 18(2):673–679. doi: 10.1109/TPWRS.2003.811010 CrossRefGoogle Scholar
  11. 11.
    Boroojeni KG, Amini MH, Bahrami S, Iyengar SS, Sarwat AI, Karabasoglu O (2017) A novel multi-time-scale modeling for electric power demand forecasting: from short-term to medium-term horizon. Electr Power Syst Res 142:58–73. doi: 10.1016/j.epsr.2016.08.031 CrossRefGoogle Scholar
  12. 12.
    Kelo S, Dudul S (2012) A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature. Int J Electr Power Energy Syst 43(1):1063–1071. doi: 10.1016/j.ijepes.2012.06.009 CrossRefGoogle Scholar
  13. 13.
    Awan SM, Aslam M, Khan ZA, Saeed H (2014) An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting. Neural Comput Appl 25(7):1967–1978. doi: 10.1007/s00521-014-1685-y CrossRefGoogle Scholar
  14. 14.
    Hu R, Wen S, Zeng Z, Huang T (2017) A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221:24–31. doi: 10.1016/j.neucom.2016.09.027 CrossRefGoogle Scholar
  15. 15.
    Jain A, Jain MB, Srinivas E (2010) A novel hybrid method for short term load forecasting using fuzzy logic and particle swarm optimization. In: 2010 international conference on power system technology, 24–28 October 2010, pp 1–7. doi: 10.1109/POWERCON.2010.5666080
  16. 16.
    Çevik HH, Çunkaş M (2015) Short-term load forecasting using fuzzy logic and ANFIS. Neural Comput Appl 26(6):1355–1367. doi: 10.1007/s00521-014-1809-4 CrossRefGoogle Scholar
  17. 17.
    Niu D, Wang Y, Wu DD (2010) Power load forecasting using support vector machine and ant colony optimization. Expert Syst Appl 37(3):2531–2539. doi: 10.1016/j.eswa.2009.08.019 CrossRefGoogle Scholar
  18. 18.
    Rahman S, Bhatnagar R (1988) An expert system based algorithm for short term load forecast. IEEE Trans Power Syst 3(2):392–399. doi: 10.1109/59.192889 CrossRefGoogle Scholar
  19. 19.
    Kwang-Ho K, Jong-Keun P, Kab-Ju H, Sung-Hak K (1995) Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Trans Power Syst 10(3):1534–1539. doi: 10.1109/59.466492 CrossRefGoogle Scholar
  20. 20.
    Fan S, Chen L (2006) Short-term load forecasting based on an adaptive hybrid method. IEEE Trans Power Syst 21(1):392–401. doi: 10.1109/TPWRS.2005.860944 MathSciNetCrossRefGoogle Scholar
  21. 21.
    Wang J, Zhu S, Zhang W, Lu H (2010) Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy 35(4):1671–1678. doi: 10.1016/j.energy.2009.12.015 CrossRefGoogle Scholar
  22. 22.
    Bashir ZA, El-Hawary ME (2009) Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Trans Power Syst 24(1):20–27. doi: 10.1109/TPWRS.2008.2008606 CrossRefGoogle Scholar
  23. 23.
    Dedinec A, Filiposka S, Dedinec A, Kocarev L (2016) Deep belief network based electricity load forecasting: an analysis of Macedonian case. Energy 115. Part 3:1688–1700. doi: 10.1016/j.energy.2016.07.090 Google Scholar
  24. 24.
    Panapakidis IP (2016) Application of hybrid computational intelligence models in short-term bus load forecasting. Expert Syst Appl 54:105–120. doi: 10.1016/j.eswa.2016.01.034 CrossRefGoogle Scholar
  25. 25.
    Abedinia O, Amjady N (2016) Short-term load forecast of electrical power system by radial basis function neural network and new stochastic search algorithm. Int Trans Electr Energy Syst 26(7):1511–1525. doi: 10.1002/etep.2160 CrossRefGoogle Scholar
  26. 26.
    Hu Z, Bao Y, Xiong T (2014) Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression. Appl Soft Comput 25:15–25. doi: 10.1016/j.asoc.2014.09.007 CrossRefGoogle Scholar
  27. 27.
    Saleh S, Mohammadi S, Rostami M-A, Askari M-R (2014) A hybrid artificial-based model for accurate short term electric load prediction. J Intell Fuzzy Syst 27(6):3103–3110. doi: 10.3233/IFS-141267 Google Scholar
  28. 28.
    Yang X-S (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation: 11th international conference, UCNC 2012, Orléan, France, September 3–7, 2012. Proceedings. Springer, Berlin, pp 240–249. doi: 10.1007/978-3-642-32894-7_27 CrossRefGoogle Scholar
  29. 29.
    Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 01(01):1–41. doi: 10.1142/S1793536909000047 CrossRefGoogle Scholar
  30. 30.
    Hecht-Nielsen R (1989) Theory of the backpropagation neural network. In: International 1989 joint conference on neural networks, 0–0 1989 1989, vol 591, pp 593–605. doi: 10.1109/IJCNN.1989.118638
  31. 31.
    Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the international conference on Neural Networks, 1987. IEEE Press, New York, pp 11–13Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Lina Pan
    • 1
  • Xiaosu Feng
    • 1
  • Fawen Sang
    • 1
  • Longjie Li
    • 1
  • Mingwei Leng
    • 2
  • Xiaoyun Chen
    • 1
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.School of Educational Science and TechnologyNorthwest University for NationalitiesLanzhouChina

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