Skip to main content
SpringerLink
Log in

Feature selection for daily peak load forecasting using a neuro-fuzzy system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Accurate electrical daily peak load forecasting (DPLF) is essential for power system management in order to prevent overloading and grid failure. Fuzzy neural networks have been successfully applied to load forecasting due to their nonlinear mapping and generalized behavior. In this paper, a neuro-fuzzy based DPLF (N-DPLF) model with a feature selection method is proposed for DPLF. The load data is clustered into seven subsets according to the season and day type. For each subset, the four features with the highest salience ranks are selected. After training N-DPLF model, the formed BSWs (bounded sum of weighted fuzzy membership functions) in accordance with the selected features denote characteristics of these features. The N-DPLF model provides explicit BSWs in hyperboxes, instead of the uncertain black box nature of neural network models, so that the selected features can be interpreted by the visually constructed BSWs. The N-DPLF model with a feature selection method shows a mean absolute percentage error (MAPE) of 1.86 % using Korea Power Exchange data over 1-year period.

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

Similar content being viewed by others

References

  1. Abdel-Aal RE (2006) Modeling and forecasting electric daily peak loads using abductive networks. Electr Power Energy Syst 28:133–141

    Article  Google Scholar 

  2. Alves da Silva AP, Ferreira VH, Velasquez RMG (2008) Input space to neural network based load forecasters. Int J Forecast 24(4):616–629

    Article  Google Scholar 

  3. Amin-Naseri MR, Soroush AR (2008) Combined use of unsupervised and supervised learning for daily peak load forecasting. Energy Convers Manag 49:1302–1308

    Article  Google Scholar 

  4. Amjady N (2001) Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Trans Power Syst 16:798–805

    Article  Google Scholar 

  5. Chai SH, Lim JS (2007) Economic turning point forecasting using fuzzy neural network and non-overlap area distribution measurement method. Korean Econ Rev 23(1):111–130

    Google Scholar 

  6. Chai SH, Lim JS (2007) Nonlinear time series prediction modeling by weighted average defuzzification based on NEWFM (in Korean). J Intell Inf Syst 17(4):160–165

    Google Scholar 

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

    Article  Google Scholar 

  8. Chen B, Chang M, Lin C (2004) Load forecasting using support vector machines: a study on eunite competition 2001. IEEE Trans Power Syst 19(4):1821–1830

    Article  Google Scholar 

  9. Chen Y, Luh PB, Zhao Y, Michel LD, Coolbeth MA, Friedland PB, Rourke SJ (2010) Short-term load forecasting: similar day-based wavelet neural networks. IEEE Trans Power Syst 25(1):322–330

    Article  Google Scholar 

  10. Chung KY (2013) Recent trends on convergence and ubiquitous computing. Pers Ubiquit Comput. doi:10.1007/s00779-013-0743-2

    Google Scholar 

  11. Chung KY, Na YJ, Lee JH (2013) Interactive design recommendation using sensor based smart wear and weather WebBot. Wirel Pers Commun 73(2):243–256

    Article  Google Scholar 

  12. Drezga I, Rahman S (1998) Input variable selection for ANN-based short-term load forecasting. IEEE Trans Power Syst 13(4):1238–1244

    Article  Google Scholar 

  13. Fan S, Chen L (2006) Short-term load forecasting based on an adaptive hybrid method. IEEE Trans Power Syst 21(1):392–401

    Article  Google Scholar 

  14. Hanmandlu M, Chauhan BK (2011) Load forecasting using hybrid models. IEEE Trans Power Syst 26(1):20–29

    Article  Google Scholar 

  15. 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 

  16. Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans Power Syst 16:44–55

    Article  Google Scholar 

  17. 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 

  18. Huang H-C, Hwang R-C, Hsieh J-G (2002) A new artificial intelligent peak power load forecaster based on non-fixed neural networks. Electr Power Energy Syst 24(3):245–250

    Article  Google Scholar 

  19. Jo NH, Song K-B, Roh Y, Kang D (2006) A study on the short-term load forecasting using support vector machine (in Korean). Trans KIEE 55A(7):306–312

    Google Scholar 

  20. Jung EY, Kim JH, Chung KY, Park DK (2013) Home health gateway based healthcare services through U-health platform. Wirel Pers Commun 73(2):207–218

    Article  Google Scholar 

  21. Kim C, Yu I, Song YH (2002) Kohonen neural network and wavelet transform based approach to short-term load forecasting. Electr Power Energy Syst 63:169–176

    Article  Google Scholar 

  22. Kim JY, Chung KY, Jung JJ (2014) Single tag sharing scheme for multiple-object RFID applications. Multimedia Tools Appl 68(2):465–477

    Article  Google Scholar 

  23. Ko JW, Chung KY, Han JS (2013) Model transformation verification using similarity and graph comparison algorithm. Multimedia Tools Appl. doi:10.1007/s11042-013-1581-y

    Google Scholar 

  24. Lauret P, Fock E, Randrianarivony RN, Manicom-Ramsamy J-F (2008) Bayesian neural network approach to short time load forecasting. Energy Convers Manag 49:1156–1166

    Article  Google Scholar 

  25. Lee S-H, Lim JS (2011) Forecasting KOSPI based on a neural network with weighted fuzzy membership functions. Expert Syst Appl 38:4259–4263

    Article  Google Scholar 

  26. Liao G-C, Tsao T-P (2006) Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short term load forecasting. IEEE Trans Evol Comput 10(3):330–340

    Article  Google Scholar 

  27. Lim JS (2009) Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system. IEEE Trans Neural Netw 20(3):522–527

    Article  Google Scholar 

  28. Lim JS, Wang D, Kim Y-S, Gupta S (2006) A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome. Neurocomputing 69(7–9):969–974

    Article  Google Scholar 

  29. Mao H, Zeng X-J, Leng G, Zhai Y-J, Keane JA (2009) Short-term and midterm load forecasting using a bilevel optimization model. IEEE Trans Power Syst 24(2):1080–1090

    Article  Google Scholar 

  30. Pandian SC, Duraiswamy K, Rajan CCA, Kanagaraj N (2006) Fuzzy approach for short term load forecasting. Electr Power Syst Res 76:541–548

    Article  Google Scholar 

  31. Reis AJR, da Silva APA (2005) Feature extraction via multiresolution analysis for short-term load forecasting. IEEE Trans Power Syst 20(1):189–198

    Article  Google Scholar 

  32. Rho MJ, Jang KS, Chung KY, Choi IY (2013) Comparison of knowledge, attitudes, and trust for the use of personal health information in clinical research. Multimedia Tools Appl. doi:10.1007/s11042-013-1772-6

    Google Scholar 

  33. Saini LM, Soni MK (2002) Artificial neural network-based peak load forecasting using conjugate gradient methods. IEEE Trans Power Syst 17(3):907–912

    Article  Google Scholar 

  34. Senjyu T, Mandal P, Uezato K, Funabashi T (2005) Next day load curve forecasting using hybrid correction method. IEEE Trans Power Syst 20(1):102–109

    Article  Google Scholar 

  35. Sheikhan N, Mohammadi N (2011) Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection. Neural Comput & Applic. doi:10.1007/s00521-011-0599-1

    Google Scholar 

  36. Siwek K, Osowski S, Szupiluk R (2009) Ensemble neural network approach for accurate load forecasting in a power system. Int J Appl Math Comput Sci 19(2):303–315

    Article  MATH  Google Scholar 

  37. Song GY, Cheon Y, Lee K, Lim H, Chung KY, Rim HC (2013) Multiple categorizations of products: cognitive modeling of customers through social media data mining. Pers Ubiquit Comput. doi:10.1007/s00779-013-0740-5

    Google Scholar 

  38. Subbaraj P, Rajasekaran V (2008) Evolutionary techniques based combined artificial neural networks for peak load forecasting. World Acad Sci Eng Technol 45:680–686

    Google Scholar 

  39. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132

    Article  MATH  Google Scholar 

  40. Taylor JW, Buizza R (2002) Neural network load forecasting with weather ensemble predictions. IEEE Trans Power Syst 17(3):626–632

    Article  Google Scholar 

  41. Taylor J, McSharry P (2007) Short-term load forecasting methods: an evaluation based on european data. IEEE Trans Power Syst 22(4):2213–2219

    Article  Google Scholar 

  42. 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 

  43. Yun Z, Quan Z, Caixin S, Shaolan L, Yuming L, Yang S (2008) RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans Power Syst 23(3):853–858

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Power Generation & Electricity DeliveryCore Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 2013T100200068).

Author information

Authors and Affiliations

  1. IT College, Gachon University, Seongnam, Republic of Korea

    Sung-Yong Son & Joon S. Lim

  2. Department of Computer Science & Engineering, Anyang University, Anyang, Republic of Korea

    Sang-Hong Lee

  3. School of Computer Information Engineering, Sangji University, Wonju, Republic of Korea

    Kyungyong Chung

Authors
  1. Sung-Yong Son
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sang-Hong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kyungyong Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joon S. Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joon S. Lim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Son, SY., Lee, SH., Chung, K. et al. Feature selection for daily peak load forecasting using a neuro-fuzzy system. Multimed Tools Appl 74, 2321–2336 (2015). https://doi.org/10.1007/s11042-014-1943-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-1943-0

Keywords

Search

Navigation