Optimization and Engineering

, Volume 11, Issue 2, pp 303–317 | Cite as

One step-ahead ANFIS time series model for forecasting electricity loads

  • Ching-Hsue Cheng
  • Liang-Ying Wei


In electric industry, electricity loads forecasting has become more and more important, because demand quantity is a major determinant in electricity supply strategy. Furthermore, accurate regional loads forecasting is one of principal factors for electric industry to improve the management performance. Recently, time series analysis and statistical methods have been developed for electricity loads forecasting. However, there are two drawbacks in the past forecasting models: (1) conventional statistical methods, such as regression models are unable to deal with the nonlinear relationships well, because of electricity loads are known to be nonlinear; and (2) the rules generated from conventional statistical methods (i.e., ARIMA), and artificial intelligence technologies (i.e., support vector machines (SVM) and artificial neural networks (ANN)) are not easily comprehensive for policy-maker. Based on these reasons above, this paper proposes a new model, which incorporates one step-ahead concept into adaptive-network-based fuzzy inference system (ANFIS) to build a fusion ANFIS model and enhances forecasting for electricity loads by adaptive forecasting equation. The fuzzy if-then rules produced from fusion ANFIS model, which can be understood for human recognition, and the adaptive network in fusion ANFIS model can deal with the nonlinear relationships. This study optimizes the proposed model by adaptive network and adaptive forecasting equation to improve electricity loads forecasting accuracy. To evaluate forecasting performances, six different models are used as comparison models. The experimental results indicate that the proposed model is superior to the listing models in terms of mean absolute percentage errors (MAPE).


Electricity loads ANFIS Adaptive learning Time series One step-ahead method 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Box G, Jenkins G (1976) Time series analysis: Forecasting and control. Holden-Day, San Francisco MATHGoogle Scholar
  2. Bunn DW, Farmer ED (1985) Comparative models for electrical load forecasting. Wiley, New York Google Scholar
  3. Chang FJ, Chiang YM, Chang LC (2007) Multi-step-ahead neural networks for flood forecasting. Hydrol Sci J Sci Hydrol 52(1) Google Scholar
  4. Chen TL, Cheng CH, Teoh HJ (2007) Fuzzy time-series based on Fibonacci sequence for stock price forecasting. Physica A 380:377–390 CrossRefGoogle Scholar
  5. Chen TL, Cheng CH, Teoh HJ (2008) High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets. Physica A 387:876–888 CrossRefGoogle Scholar
  6. Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278 MathSciNetGoogle Scholar
  7. Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427–431 CrossRefMathSciNetMATHGoogle Scholar
  8. Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13(3):134–144 CrossRefMathSciNetGoogle Scholar
  9. Enders W (2004) Applied econometric time series. Wiley, New York Google Scholar
  10. Evans GW, Honkapohja S (2001) Learning and expectations in macroeconomics. Princeton University Press, Princeton. ISBN 0-691-04921 Google Scholar
  11. Fan S, Mao C, Chen L (2005) Peak load forecasting using the selforganizing map. Advances in neural network-ISNN 2005. Springer, Berlin. Part III, 640-9 Google Scholar
  12. Haida T, Muto S (1994) Regression based peak load forecasting using a transformation technique. IEEE Trans Power Syst 9(4):1788–1794 CrossRefGoogle Scholar
  13. Hippert HS, Pedreira CE, Castro R (2001) Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans Power Syst 16(1):44–55 CrossRefGoogle Scholar
  14. Hsu CC, Chen CY (2003) Regional load forecasting in Taiwan—applications of artificial neural networks. Energy Convers Manag 44:1941–1949 CrossRefGoogle Scholar
  15. Huang SJ, Shih KR (2003) Short-term load forecasting via ARMA model identification including non-Gaussian process considerations. IEEE Trans Power Syst 18(2):673–679 CrossRefGoogle Scholar
  16. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23(3):665–685 CrossRefMathSciNetGoogle Scholar
  17. Nowak MP, Schultz R, Westphalen A (2005) A stochastic integer programming model for incorporating day-ahead trading of into hydro-thermal unit commitment. Optim Eng 6:163–176 CrossRefMathSciNetMATHGoogle Scholar
  18. Pai PF (2006) Hybrid ellipsoidal fuzzy systems in forecasting regional electricity loads. Energy Convers Manag 47(15–16):2283–2289 CrossRefGoogle Scholar
  19. Pai PF, Hong WC (2005) Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electr Power Syst Res 74:417–425 CrossRefGoogle Scholar
  20. Pino R, Parreno J, Gomez A, Priore P (2008) Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks. Eng Appl Artif Intell 21:53–62 CrossRefGoogle Scholar
  21. Rnberg RN, Misch WR (2002) A two-stage planning model for power scheduling in a hydro-thermal system under uncertainty. Optim Eng 3:355–378 CrossRefMathSciNetGoogle Scholar
  22. Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions. In: Proc IFAC symp fuzzy inform, knowledge representation and decision analysis, pp 55–60 Google Scholar
  23. Taylor JW, Buizza R (2003) Using weather ensemble predictions in electricity demand forecasting. Int J Forecast 19:57–70 CrossRefGoogle Scholar
  24. Ying LC, Pan MC (2008) Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Convers Manag 49:205–211 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Information ManagementNational Yunlin University of Science and TechnologyTouliuTaiwan
  2. 2.Department of Information ManagementYuanpei UniversityHsin ChuROC

Personalised recommendations