Skip to main content
SpringerLink
Log in

An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Short-term electric load forecasting (STLF) is an essential tool for power generation planning, transmission dispatching, and day-to-day utility operations. A number of techniques are used and reported in the literature to build an accurate forecasting model. Out of them Artificial Neural Networks (ANN) are proven most promising technique for STLF model building. Many learning schemes are being used to boost the ANN performance with improved results. This motivated us to explore better optimization approaches to devise a more suitable prediction technique. In this study, we propose a new hybrid model for STLF by combining greater optimization ability of artificial bee colony (ABC) algorithm with ANN. The ABC is used as an alternative learning scheme to get optimized set of neuron connection weights for ANN. This formulation showed improved convergence rate without trapping into local minimum. Forecasting results obtained by this new approach have been presented and compared with other mature and competitive approaches, which confirms its applicability in forecasting domain.

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

Similar content being viewed by others

References

  1. Hooshmand Rahmat-Allah, Amooshahi Habib, Parastegari Moein (2013) A hybrid intelligent algorithm based short-term load forecasting approach. Int J Electr Power Energy Syst 45(1):313–324

    Article  Google Scholar 

  2. Alfares Hesham K, Nazeeruddin Mohammad (2002) Electric load forecasting: literature survey and classification of methods. Int J Syst Sci 33(1):23–34

    Article  MATH  Google Scholar 

  3. Hanmandlu Madasu, Chauhan Bhavesh Kumar (2011) Load forecasting using hybrid models. IEEE Trans Power Syst 26(1):20–29

    Article  Google Scholar 

  4. Hahn H, Meyer-Nieberg S, Pickl S (2009) Electric load forecasting methods: tools for decision making. Eur J Oper Res 199(3):902–907

    Article  MATH  Google Scholar 

  5. Suganthi L, Samuel AA (2012) Energy models for demand forecasting—a review. Renew Sustain Energy Rev 16(2):1223–1240

    Article  Google Scholar 

  6. Sheikhan M, Mohammadi N (2012) Neural-based electricity load forecasting using hybrid of ga and aco for feature selection. Neural Comput Appl 21:1961–1970

    Article  Google Scholar 

  7. Fletcher R (1987) Pract Methods Optim, 2nd edn. Wiley-Interscience, New York, NY, USA

    Google Scholar 

  8. Karaboga Dervis, Basturk Bahriye (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MATH  MathSciNet  Google Scholar 

  9. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of the IEEE international Conference on neural networks, volume 4, p 1942–1948

  10. Holland John H (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press

  11. Huang S-J, Shih K-R (2003) Short-term load forecasting via ARMA model identification including non-gaussian process considerations. IEEE Trans Power Syst 18(2):673–679

    Article  Google Scholar 

  12. Wi Y-M, Joo S-K, Song K-B (2012) Holiday load forecasting using fuzzy polynomial regression with weather feature selection and adjustment. IEEE Trans Power Syst 27(2):596–603

    Article  Google Scholar 

  13. Taylor JW (2012) Short-term load forecasting with exponentially weighted methods. IEEE Trans Power Syst 27(1):458–464

    Article  Google Scholar 

  14. Charytoniuk W, Chen MS, Van Olinda P (1998) Nonparametric regression based short-term load forecasting. IEEE Trans Power Syst 13(3):725–730

    Article  Google Scholar 

  15. Yao X (1999) Evolving artificial neural networks. IEEE Proc 87(9):1423–1447

    Article  Google Scholar 

  16. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  17. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    Article  MathSciNet  Google Scholar 

  18. Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sustain Energy Rev 33:102–109

    Article  Google Scholar 

  19. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    Google Scholar 

  20. Baños R, Manzano-Agugliaro F, Montoya FG, Gil C, Alcayde A, Gómez J (2011) Optimization methods applied to renewable and sustainable energy: a review. Renew Sustain Energy Rev 15(4):1753–1766

    Article  Google Scholar 

  21. Hong Wei-Chiang (2010) Application of chaotic ant swarm optimization in electric load forecasting. Energy Policy 38(10):5830–5839

    Article  Google Scholar 

  22. Hong Wei-Chiang, Dong Yucheng (2013) Cyclic electric load forecasting by seasonal svr with chaotic genetic algorithm. Int J Electr Power Energy Syst 44(1):604–614

    Article  MATH  Google Scholar 

  23. Wang Jianjun, Li Li, Niu Dongxiao, Tan Zhongfu (2012) An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl Energy 94:65–70

    Article  Google Scholar 

  24. Bahrami S, Hooshmand R-A, Parastegari M (2014) Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm. Energy 72:434–442

    Article  Google Scholar 

  25. Selakov A, Cvijetinović D, Milović L, Mellon S, Bekut D (2014) Hybrid pso-svm method for short-term load forecasting during periods with significant temperature variations in city of burbank. Appl Soft Comput 16:80–88

    Article  Google Scholar 

  26. Li H, Liu K, Li X (2010) A comparative study of artificial bee colony, bees algorithms and differential evolution on numerical benchmark problems. In: Cai Z, Tong H, Kang Z, Liu Y (eds) Computational intelligence and intelligent systems. Communications in computer and information science, vol 107. Springer, Berlin, pp 198–207

  27. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    Article  MATH  MathSciNet  Google Scholar 

  28. El-Abd Mohammed (2012) Performance assessment of foraging algorithms vs. evolutionary algorithms. Inf Sci 182(1):243–263

    Article  MathSciNet  Google Scholar 

  29. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (abc) algorithm and applications. Artif Intell Rev  42(1):21–57

    Article  Google Scholar 

  30. Awan SM, Khan ZA, Aslam M, Mahmood W, Ahsan A (2012) Application of narx based ffnn, svr and ann fitting models for long term industrial load forecasting and their comparison. In: IEEE international symposium on industrial electronics (ISIE), 2012, p 803–807

  31. Haykin S (1999) Neural Networks: a comprehensive foundation. Prentice Hall International Editions Series, Prentice Hall

  32. Karaboga Dervis, Ozturk Celal (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11(1):652–657

    Article  Google Scholar 

  33. Shi Yuhui, Eberhart Russell C (1998) Parameter selection in particle swarm optimization. In: Evolutionary Programming VII, volume 1447 Lecture Notes in Computer Science. Springer, Heidelberg, pp 591–600

  34. Goldberg David E (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc, Boston

    MATH  Google Scholar 

  35. Heaton J (2010) Programming neural networks with Encog 2 in Java. Heaton Research Inc, Chesterfield

    Google Scholar 

  36. Lee Kwang Y, El-Sharkawi Mohamed A (2008) Modern heuristic optimization techniques: theory and applications to power systems, vol 39. Wiley, New York

    Book  Google Scholar 

  37. Diebold FX, Mariano RS (2002) Comparing predictive accuracy. J Bus Econ Stat 20(1):134–144

    Article  MathSciNet  Google Scholar 

  38. Hung Wei-Mou, Hong Wei-Chiang (2009) Application of svr with improved ant colony optimization algorithms in exchange rate forecasting. Control Cybern 38(3):863–891

    Google Scholar 

  39. Hong Wei-Chiang (2011) Electric load forecasting by seasonal recurrent svr (support vector regression) with chaotic artificial bee colony algorithm. Energy 36(9):5568–5578

    Article  Google Scholar 

Download references

Acknowledgments

Project (No. ICTRDF/TR&D/2009/22) supported by the National ICT R&D Fund, Pakistan.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan

    Shahid M. Awan & Muhammad Aslam

  2. Department of Electrical Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan

    Zubair A. Khan

  3. Al-Khawarizmi Institute of Computer Science, University of Engineering and Technology, Lahore, 54890, Pakistan

    Hassan Saeed

Authors
  1. Shahid M. Awan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Aslam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zubair A. Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hassan Saeed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shahid M. Awan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Awan, S.M., Aslam, M., Khan, Z.A. et al. An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting. Neural Comput & Applic 25, 1967–1978 (2014). https://doi.org/10.1007/s00521-014-1685-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-014-1685-y

Keywords

Search

Navigation