An Improved SA-Based BP-ANN Technique for Annual Runoff Forecasting Under Uncertain Environment

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 242)

Abstract

In this paper, the author presents an integrated approach combining the simulated annealing method and the feed forward neural network to forecast the annual runoff in power system under uncertain environment. The type of neural network used in this method is a multi-layer pre-trained by the SA. Finally, we use the SA-based ANN to see if we actually could reduce the error of annual runoff forecasting. The proposed Simulated Algorithm-based Error Back Propagation Artificial Neural Net (SA-based BP-ANN) annual forecasting scheme was tested using data obtained from a case study including 24 h time periods. The result demonstrated the accuracy of the proposed annual runoff forecasting.

Keywords

Forecasting Fuzzy BP-ANN SA 

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References

  1. 1.
    Liao G, Tsao T (2004) Application of fuzzy neural networks and artificial intelligence for load forecasting. Electric Power Systems Research 70:237–244Google Scholar
  2. 2.
    Yang Z (2004) Schedule management of the Xiaolongmen hydraulic power plant on the jialing river. Mastersthesis, Sichuan University (In Chinese)Google Scholar
  3. 3.
    Zadeh LA (1965) Fuzzy sets. Information and Control 8(3):338–353Google Scholar
  4. 4.
    Xu J, Zhou Y (2011) Fuzzy-like multiple objective decision making. Springer-Verlag, Heidelberg, BerlinGoogle Scholar
  5. 5.
    Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JC (eds.), Parallel Distributed Processing, Foundations, MIT Press, Cambridge, MA 1:318–362Google Scholar
  6. 6.
    Ouyang Z, Shahidehpour SM (1992) A hybrid artificial neural network-dynamic programming approach to unit commitment. IEEE Transactions on Power Systems 7:236–246Google Scholar
  7. 7.
    Hsu YY, Yang CC (1991) Design of artificial networks for short-term load forecasting, Parts I and II. IEE Proceedings Communications, 138:407–418Google Scholar
  8. 8.
    Hsu YY, Chen CR (1991) Tuning of power system stabilizers using an artificial neural network. IEEE Transactions on Energy Conversion, EC 6:612–619Google Scholar
  9. 9.
    Santoso NL, Tan OT (1990) Neural-net based real-time control of capacitors installed on distribution systems. IEEE Transactions on Power Delivery 5:266–272Google Scholar
  10. 10.
    Hsu YY, Jeng, LH (1992) Analysis of torsional oscillations using an artificial neural network. IEEE Transactions on Energy Conversion 7:684–690Google Scholar
  11. 11.
    Sobajic DJ, Pao YH (1989) Artificial neural-net based dynamic security assessment for electric power systems. IEEE Transactions on Power Systems 4:220–228Google Scholar
  12. 12.
    Neibur D, Germond AJ (1992) Power system static security assessment using the Kohonen neural network classifier. IEEE Transactions on Power Systems 7:865–872Google Scholar
  13. 13.
    Xu J, Tu Y, Zeng Z (2001) Bi-level optimization of regional water resources allocation problem under fuzzy random environment. Journal of Water Resources Planning and Management 7:865–872Google Scholar
  14. 14.
    Heilpern S (1992) The expected value of a fuzzy number. Fuzzy Set System 47(1):81–86Google Scholar
  15. 15.
    Kerus R, Meyer KD (1987) Statistics with vague data. Reidel D Publishing Company, DordrechtGoogle Scholar
  16. 16.
    Debasis S, JayantMM(2003) ANNSA: a hybrid artificial neural network/simulated annealing algorithm for optimal control problems. Chemical Engineering Science 58:3131–3142Google Scholar
  17. 17.
    Nayaka R, Sharma JD (2000) A hybrid neural network and simulated annealing approach to the unit commitment problem. Computers and Electrical Engineering 26:461–477Google Scholar
  18. 18.
    Khan Z, Prasad LB, Singh T (1997) Machining condition optimization by genetic algorithms and simulated annealing. Computers Operations Research 24(7):647–657Google Scholar
  19. 19.
    Wang ZG, Rahman M, Wong YS et al (2005) Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing. International Journal of Machine Tools & Manufacture 45:1726–1734Google Scholar
  20. 20.
    Yi D, Ge X (2005) An improved PSO-based ANN with simulated annealing technique. Neurocomputing 63:527–533Google Scholar
  21. 21.
    Lu WZ, Fan HY, Lo SM (2003) Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong. Neurocomputing 51:387–400Google Scholar
  22. 22.
    Liang R, Hsu Y (1994) Scheduling of hydroelectric generation units using artificial neural networks. Proceedings-Generation, Transmission and Distribution 141(5):452–458Google Scholar
  23. 23.
    Gurrment S (1995) Fast approach to artificial network training and its application to economic load dispatch. Electric machines and Power systems 23(1):13–24Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Uncertainty Decision-Making LaboratorySichuan UniversityChengduPeople’s Republic of China

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