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Artificial Immune System for Short-Term Electric Load Forecasting

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Book cover Artificial Intelligence and Soft Computing – ICAISC 2008 (ICAISC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

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Abstract

This paper proposes a novel model, based on the artificial immune system, to solve the problem of short-term load forecasting. An artificial immune system is trained to recognize antigens which encode sequences of load time series. The created immune memory is a representation of these sequences. In the forecast procedure a new incomplete antigen, containing only the first part of the sequence, is presented to the model. The second forecasted part of the sequence is reconstructed from activated antibodies. The model was verified using several real data examples of the short-term load forecast.

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References

  1. Bunn, D., Farmer, E.: Economic and Operational Context of Electric Load Prediction. In: Bunn, D., Farmer, E. (eds.) Comparative Models for Electrical Load Forecasting, pp. 3–11. Wiley, Chichester (1985)

    Google Scholar 

  2. Engle, R.F., Mustafa, C., Rice, J.: Modeling Peak Electricity Demand. Journal of Forecasting 11, 241–251 (1992)

    Article  Google Scholar 

  3. Gross, G., Galiana, F.D.: Short-Term Load Forecasting. Proc. IEEE 75(12), 1558–1573 (1987)

    Article  Google Scholar 

  4. Metaxiotis, K., Kagiannas, A., Askounis, D., Psarras, J.: Artificial Intelligence in Short Term Load Forecasting: A State-of-the-Art Survey for the Researcher. Energy Conversion and Management 44, 1525–1534 (2003)

    Article  Google Scholar 

  5. Khotanzad, A., Afkhami-Rohani, R., Lu, T.L., Davis, M.H., Abaye, A., Maratukulam, D.: ANNSTLF - A Neural Network Based Electric Load Forecasting System. IEEE Trans. on Neural Networks 8(4), 835–846 (1997)

    Article  Google Scholar 

  6. Dudek, G.: Short Term Load Forecasting using Radial Basis Neural Networks. In: 5th National Conference on Forecasting in Power Engineering PE 2000 (in Polish), Wydawnictwo Politechniki Częstochowskiej, Częstochowa, pp. 59–68 (2000)

    Google Scholar 

  7. Osowski, S., Siwek, K., Kądzielawa, A.: Neural Network Approach to Load Forecasting. In: 2nd Conference on Neural Networks and Their Applications, Wydawnictwo Politechniki Częstochowskiej, Częstochowa, pp. 355–360 (1996)

    Google Scholar 

  8. Dudek, G.: Application of the Hecht-Nielsen Neural Network to Short Term Load Forecasting. In: International Conference on Present-Day Problems of Power Engineering APE 1997 (in Polish), Gdańsk-Jurata, vol. 4, pp. 65–72 (1997)

    Google Scholar 

  9. Vermaak, J., Botha, E.C.: Recurrent Neural Networks for Short-Term Load Forecasting. IEEE Trans. on Power Systems 13(1), 126–132 (1998)

    Article  Google Scholar 

  10. Papadakis, S.E., Theocharis, J.B., Bakirtzis, A.G.: A Load Curve Based Fuzzy Modeling Technique for Short-Term Load Forecasting. Fuzzy Sets and Systems 135, 279–303 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  11. Dudek, G.: Short-Term Electrical Load Forecasting using Fuzzy Clustering Method. Przegląd Elektrotechniczny (in Polish) 9, 26–28 (2006)

    Google Scholar 

  12. Rahman, S., Hazim, O.: A Generalized Knowledge-Based Short Term Load Forecasting Technique. IEEE Trans. on Power Systems 8(2), 508–514 (1993)

    Article  Google Scholar 

  13. Markovic, M., Fraissler, W.: Short-Term Load Forecast by Plausibility Checking of Announced Demand: An Expert System Approach. Eur. Trans. Electr. Power Eng/ETEP 3(5), 353–358 (1993)

    Article  Google Scholar 

  14. Kodogiannis, V.S., Anagnostakis, E.M.: Soft Computing Based Techniques for Short-Term Load Forecasting. Fuzzy Sets and Systems 128, 413–426 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  15. Liang, R.-H., Cheng, C.-C.: Short-Term Load Forecasting by a Neuro-Fuzzy Based Approach. Electrical Power and Energy Systems 24, 103–111 (2003)

    Article  Google Scholar 

  16. Dash, P.K., Liew, A.C., Rahman, S., Dash, S.: Fuzzy and Neuro-Fuzzy Computing Models for Electric Load Forecasting. Engng Applic. Artif. Intell. 8(4), 423–433 (1995)

    Article  Google Scholar 

  17. Tamimi, M., Egbert, R.: Short Term Electric Load Forecasting via Fuzzy Neural Collaboration. Electric Power System Research 56, 243–248 (2000)

    Article  Google Scholar 

  18. Dudek, G.: Regression Tree as a Forecasting Tool. In: National Conference on Forecasting in Power Engineering PE 2004 (in Polish), Wydawnictwo Politechniki Częstochowskiej, Częstochowa, pp. 99–105 (2004)

    Google Scholar 

  19. Dudek, G.: Application of Hierarchical Clustering Methods to the Daily Electrical Load Profile Forecasting. Przegląd Elektrotechniczny (in Polish) 9, 9–11 (2006)

    Google Scholar 

  20. Pai, P.-F., Hong, W.-C.: Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting. Energy Conversion and Management 46, 2669–2688 (2005)

    Article  Google Scholar 

  21. Dobrzańska, I.: Hausdorff Dimension as a Tool of Step Prediction. Badania Operacyjne i Decyzje (in Polish) 2, 31–62 (1991)

    Google Scholar 

  22. Łyp, J.: Methodology of the Electric Load Analysis and Forecasting for the Local Power Systems. Ph.D. Dissertation (in Polish), Dept. Elect. Eng., Czestochowa University of Technology (2003)

    Google Scholar 

  23. Popławski, T., Dąsal, K.: Canonical Distribution in Short-Term Load Forecasting. In: Szkutnik, J., Kolcun, M. (eds.) Technical and Economic Aspect of Modern Technology Transfer in Context of Integration with European Union, Mercury-Smekal Pub House, Kosice, Slovak Republic, pp. 147–153 (2004)

    Google Scholar 

  24. Dasgupta, D., Ji, Z., Gonzalez, F.: Artificial Immune System (AIS) Research in the Last Five Years. In: Congress on Evolutionary Computation, Canberra, Australia, pp. 123–130 (2003)

    Google Scholar 

  25. Timmis, J., Knight, T., De Castro, L.N., Hart, E.: An Overview of Artificial Immune Systems. In: Paton, R., Bolouri, H., Holcombe, M., Parish, J.H., Tateson, R. (eds.) Computation in Cells and Tissues: Perspectives and Tools for Thought. Natural Computation Series, pp. 51–86. Springer, Heidelberg (2004)

    Google Scholar 

  26. Wierzchoń, S.: Artificial Immune Systems. Theory and Applications. Akademicka Oficyna Wydawnicza EXIT, Warsaw (in Polish) (2001)

    Google Scholar 

  27. Dudek, G.: Short-Term Load Forecasting using Fuzzy Clustering and Genetic Algorithms. Final report of the Polish State Committee for Scientific Research founded grant no. 3T10B02329. Dept. Elect. Eng., Czestochowa University of Technology (unpublished, in Polish) (2006)

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Czestochowa University of Technology, Al. Armii Krajowej 17, 42-200, Czestochowa, Poland

    Grzegorz Dudek

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  1. Grzegorz Dudek
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Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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Dudek, G. (2008). Artificial Immune System for Short-Term Electric Load Forecasting. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_95

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_95

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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