Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection | Neural Computing and Applications Skip to main content
Springer Nature Link
Log in

Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection

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

Abstract

Due to deregulation of electricity industry, accurate load forecasting and predicting the future electricity demand play an important role in the regional and national power system strategy management. Electricity load forecasting is a challenging task because electric load has complex and nonlinear relationships with several factors. In this paper, two hybrid models are developed for short-term load forecasting (STLF). These models use “ant colony optimization (ACO)” and “combination of genetic algorithm (GA) and ACO (GA-ACO)” for feature selection and multi-layer perceptron (MLP) for hourly load prediction. Weather and climatic conditions, month, season, day of the week, and time of the day are considered as load-influencing factors in this study. Using load time-series of a regional power system, the performance of ACO + MLP and GA-ACO + MLP hybrid models is compared with principal component analysis (PCA) + MLP hybrid model and also with the case of no-feature selection (NFS) when using MLP and radial basis function (RBF) neural models. Experimental results and the performance comparison with similar recent researches in this field show that the proposed GA-ACO + MLP hybrid model performs better in load prediction of 24-h ahead in terms of mean absolute percentage error (MAPE).

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Gross G, Galiana F (1987) Short term load forecasting. Proc IEEE 75:1558–1573

    Article  Google Scholar 

  2. Sadownik R, Barbosa E (1999) Short-term forecasting of industrial electricity consumption in Brazil. Int J Forecast 18:215–224

    Article  Google Scholar 

  3. Afshin M, Sadeghian A (2007) PCA-based least squares support vector machines in week-ahead load forecasting. In: Proceedings of IEEE industrial and commercial power systems technical conference, pp 1–6

  4. Kothari DP, Nagrath IJ (2003) Modern power system analysis, 3rd edn. Tata McGraw Hill, New Delhi

    Google Scholar 

  5. Feinberg EA, Genethliou D (2005) Load forecasting. In: Chow JH, Wu FF, Momoh JJ (eds) Applied mathematics for restructured electric power systems: optimization, control and computational intelligence, power electronics and power systems. Springer, US, pp 269–285

    Google Scholar 

  6. Gonzalez-Romera E, Jaramillo-Moran MA, Carmona-Fernandez D (2006) Monthly electric energy demand forecasting based on trend extraction. IEEE Trans Power Syst 21:1946–1953

    Article  Google Scholar 

  7. Kyriakides E, Polycarpou M (2007) Short term electric load forecasting: a tutorial. In: Chen K, Wang L (eds) Trends in neural computation, studies in computational intelligence. Springer, Berlin, pp 391–418

    Chapter  Google Scholar 

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

    Article  MATH  Google Scholar 

  9. Amjady N (2001) Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Trans Power Syst 16:498–505

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. 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 68:47–59

    Article  Google Scholar 

  12. Gao J, Sun H-B, Tang G-Q (2001) Application of optimal combined forecasting based on fuzzy synthetic evaluation on power load forecast. J Syst Eng 16:106–110

    Google Scholar 

  13. Kodogiannis VS, Anagnostakis EM (2002) Soft computing based techniques for short-term load forecasting. Fuzzy Sets Syst 128:413–426

    Article  MathSciNet  MATH  Google Scholar 

  14. Al-Kandari AM, Soliman SA, El-Hawary ME (2004) Fuzzy short-term electric load forecasting. Int J Electr Power Energy Syst 26:111–122

    Article  Google Scholar 

  15. Kim KH, Park JK, Hwang KJ, Kim SH (1995) Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert-systems. IEEE Trans Power Syst 10:1534–1539

    Article  Google Scholar 

  16. Satish B, Swarup KS, Srinivas S, Rao AH (2004) Effect of temperature on short-term load forecasting using an integrated ANN. Electr Power Syst Res 72:95–101

    Article  Google Scholar 

  17. He W (2008) Forecasting electricity load with optimized local learning models. Int J Electr Power Energy Syst 30:603–608

    Article  Google Scholar 

  18. Alves da Silva A, Ferreira VH, Velasquez MG (2008) Input space to neural network based load forecasters. Int J Forecast 24:616–629

    Article  Google Scholar 

  19. Xiao Z, Ye S-J, Zhang B, Sun C-X (2009) BP neural network with rough set for short term load forecasting. Expert Syst Appl 36:273–279

    Article  Google Scholar 

  20. Niu D, Wang Y, Dash Wu D (2010) Power load forecasting using support vector machine and ant colony optimization. Expert Syst Appl 37:2531–2539

    Article  Google Scholar 

  21. Christianse WR (1971) Short term load forecasting using general exponentials smoothing. IEEE Trans Power Apparatus Syst 90:900–911

    Article  Google Scholar 

  22. Park JH, Park YM, Lee KY (1991) Composite modeling for adaptive short-term load forecasting. IEEE Trans Power Syst 6:450–457

    Article  Google Scholar 

  23. Gelb A (1974) Applied optimal estimation. MIT Press, Massachusetts

    Google Scholar 

  24. Brown RG (1983) Introduction to random signal analysis and Kalman filtering. Wiley, New York

    Google Scholar 

  25. Moghram I, Rahman S (1989) Analysis and evaluation of five short-term load forecasting techniques. IEEE Trans Power Syst 4:1484–1491

    Article  Google Scholar 

  26. Douglas AP, Breipohl AM, Lee FN, Adapa R (1998) The impact of temperature forecast uncertainty on Bayesian load forecasting. IEEE Trans Power Syst 13:1507–1513

    Article  Google Scholar 

  27. Carpinteiro OAS, Reis AJR, da Silva APA (2004) A hierarchical neural model in short-term load forecasting. Appl Soft Comput 4:405–412

    Article  Google Scholar 

  28. Cai Y, Wang J-Z, Tang Y, Yang Y-C (2011) An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) & HS-ARTMAP (Hyper-spherical ARTMAP network) neural network. Energy 36:1340–1350

    Article  Google Scholar 

  29. Liao G-C, Tsao T-P (2004) Application of fuzzy neural networks and artificial intelligence for load forecasting. Electr Power Syst Res 70:237–244

    Article  Google Scholar 

  30. Yao SJ, Song YH, Zhang LZ, Cheng XY (2000) Wavelet transform and neural networks for short-term electrical load forecasting. Energy Convers Manag 41:1975–1988

    Article  Google Scholar 

  31. Zhang B-L, Dong Z-Y (2001) An adaptive neural-wavelet model for short-term load forecasting. Electr Power Syst Res 59:121–129

    Article  Google Scholar 

  32. Pai P-F, Hong W-C (2005) Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electr Power Syst Res 74:417–425

    Article  Google Scholar 

  33. Pai P-F, Hong W-C (2005) Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers Manag 46:2669–2688

    Article  Google Scholar 

  34. Hong W-C (2009) Hybrid evolutionary algorithms in a SVR-based electric load forecasting model. Electr Power Energy Syst 31:409–417

    Article  Google Scholar 

  35. Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1:131–156

    Article  Google Scholar 

  36. Wang Z, Cao Y (2006) Short-term load forecasting based on mutual information and artificial neural networks. Lecture Notes Comput Sci 3972:1246–1251

    Article  Google Scholar 

  37. Siwek K, Osowski S (2010) Neural network ensemble for 24-hour load pattern prediction in power systems. Stud Comput Intell 302:151–169

    Article  Google Scholar 

  38. Hippert HS, Taylor JW (2010) An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting. Neural Netw 23:386–395

    Article  Google Scholar 

  39. Niu D, Gu Z, Zhang Y (2009) An AFSA-TSGM based wavelet neural network for power load forecasting. Lecture Notes Comput Sci 5553:1034–1043

    Article  Google Scholar 

  40. Niu D, Kou B, Zhang Y, Gu Z (2009) A short-term load forecasting model based on LS-SVM optimized by dynamic inertia weight particle swarm optimization algorithm. Lecture Notes Comput Sci 5552:242–250

    Article  Google Scholar 

  41. Ghanbari A, Hadavandi E, Abbasian-Naghneh S (2010) An intelligent ACO-SA approach for short term electricity load prediction. Lecture Notes Comput Sci 6216:623–633

    Article  Google Scholar 

  42. Kelo SM, Dudul SV (2011) Short-term Maharashtra state electrical power load prediction with special emphasis on seasonal changes using a novel focused time lagged recurrent neural network based on time delay neural network model. Expert Syst Appl 38:1554–1564

    Article  Google Scholar 

  43. Maniezzo V, Colomi A (1999) The ant system applied to the quadratic assignment problem. Knowl Data Eng 11:769–778

    Article  Google Scholar 

  44. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, New York

    MATH  Google Scholar 

  45. Srinivas M, Patnik LM (1994) Genetic algorithms: a survey. IEEE Computer Society Press, Los Alamitos

    Google Scholar 

  46. Amjady N, Keynia F (2009) Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm. Energy 34:46–57

    Article  Google Scholar 

  47. Kohavi R, John JH (1997) Wrappers for feature subset selection. Artif Intell 97:273–324

    Article  MATH  Google Scholar 

  48. Fodor IK (2002) A survey of dimension reduction techniques, Technical Report UCRL-ID-148494, Lawrence Livermore National Laboratory, US Department of Energy

  49. Rashidy Kanan H, Faez K (2008) An improved feature selection method based on ant colony optimization (ACO) evaluated on face recognition system. Appl Math Comput 205:716–725

    Article  MATH  Google Scholar 

  50. Al-Ani A (2005) Feature subset selection using ant colony optimization. Int J Comput Intell 2:53–58

    Google Scholar 

  51. Nemati S, Basiri ME, Ghasem-Aghaee N, Hosseinzadeh Aghdam M (2009) A novel ACO–GA hybrid algorithm for feature selection in protein function prediction. Expert Syst Appl 36:12086–12094

    Article  Google Scholar 

  52. Mishra S, Patra SK (2008) Short term load forecasting using a neural network trained by a hybrid artificial immune system. In: Proceedings of IEEE third international conference on industrial and information systems, pp 1–5

  53. Wang J, Liu Z, Lu P (2008) Electricity load forecasting based on adaptive quantum-behaved particle swarm optimization and support vector machines on global level. In: Proceedings of international symposium on computational intelligence and design, pp 233–236

Download references

Author information

Authors and Affiliations

  1. EE Department, Faculty of Engineering, Islamic Azad University, South Tehran Branch, P.O. Box: 11365-4435, Tehran, Iran

    Mansour Sheikhan & Najmeh Mohammadi

  2. Research Center of West Tehran Province Power Distribution Company, Tehran, Iran

    Najmeh Mohammadi

Authors
  1. Mansour Sheikhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Najmeh Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mansour Sheikhan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sheikhan, M., Mohammadi, N. Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection. Neural Comput & Applic 21, 1961–1970 (2012). https://doi.org/10.1007/s00521-011-0599-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0599-1

Keywords

Search

Navigation