On the Use of Syntactic Pattern Recognition Methods, Neural Networks, and Fuzzy Systems for Short-Term Electrical Load Forecasting

  • Janusz Jurek
  • Tomasz Peszek
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 30)

Abstract

Several artificial intelligence methods of short-term electrical load forecasting are discussed in the paper. The model of a hybrid system based on syntactic pattern recognition, neural networks, and fuzzy techniques is introduced. The application of the model and the experimental results of short-term electrical load forecasting are presented.

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References

  1. 1.
    Flasiński M, Jurek J (1999) Dynamically Programmed Automata for Quasi Context Sensitive Languages as a Tool for Inference Support in Pattern Recognition-Based Real-Time Control Expert Systems. Pattern Recognition, 32(4), 671–690CrossRefGoogle Scholar
  2. 2.
    Fu KS (1982) Syntactic Pattern Recognition and Applications, Prentice HallGoogle Scholar
  3. 3.
    Hippert SH, Pedriera CE, Souza RC (2001) Neural networks for short-term load forecasting: a review and evaluation, IEEE Trans. Power Systems, 16(1), 44–55CrossRefGoogle Scholar
  4. 4.
    Jurek J (2005) Recent developments of the syntactic pattern recognition model based on quasi-context sensitive languages, accepted for publication in Pattern Recognition LettersGoogle Scholar
  5. 5.
    Jurek J (2004) Towards Grammatical Inferencing of GDPLL(k) Grammars for Applications in Syntactic Pattern Recognition-Based Expert Systems, Lecture Notes in Computer Science, 3070, 604–609Google Scholar
  6. 6.
    Mastorocostas PA, Theocharis JB, Kiartzis SJ, Bakisrtzis AG (2000) A hybrid fuzzy modeling method for short-term load forecasting, Mathematics and Computers in Simulation, 51, 221–232CrossRefGoogle Scholar
  7. 7.
    Papadakis SE (1998) A novel approach to short-term load forecasting using fuzzy neural network, IEEE Trans. Power Systems, 13(2), 480–492CrossRefGoogle Scholar
  8. 8.
    Tadeusiewicz R (1993), Sieci neuronowe, Akademicka Oficyna Wydawnicza, Warszawa.Google Scholar
  9. 9.
    Tadeusiewicz R, Flasiński M (1991) Rozpoznawanie Obrazów, Państwowe Wydawnictwo Naukowe PWN, Warszawa.Google Scholar
  10. 10.
    Zieliński J (1997) Survey of short-term electrical load forecasting methods, Mat. Konf. APE’97 Aktualne Problemy w Elektroenergetyce, Gdañsk, Jurata 11–13 czerwca 199, tom IV, 121–129Google Scholar
  11. 11.
    Zieliński J (2000), Inteligentne systemy w zarzadzaniu. Teoria i praktyka, Wydawnictwo naukowe PWN, Warszawa.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Janusz Jurek
    • 1
  • Tomasz Peszek
    • 1
  1. 1.Institute of Computer ScienceJagiellonian UniversityCracowPoland

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