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Hybrid Adaptive Systems of Computational Intelligence and Their On-line Learning for Green IT in Energy Management Tasks

  • Yevgeniy BodyanskiyEmail author
  • Olena Vynokurova
  • Iryna Pliss
  • Dmytro Peleshko
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 74)

Abstract

In this book chapter, we have considered a topical problem of intelligent energy management, which arises in the context of an intensively developed science direction—Green IT. The hybrid neuro-neo-fuzzy system and its high-speed learning algorithm are proposed. This system can be used for on-line prediction of essentially non-stationary nonlinear chaotic and stochastic time series, which describe electrical load producing and consuming processes. The considered hybrid adaptive system of computational intelligence has some advantages over the conventional artificial neural networks and neuro-fuzzy systems. The proposed hybrid neuro-neo-fuzzy prediction system provides a high quality load prediction that is very important for power systems.

Keywords

Computational intelligence On-line learning Green IT Hybrid adaptive systems Neuro-neo-fuzzy system  

References

  1. 1.
    Abraham, A., Khan, M.R.: Neuro-fuzzy paradigms for intelligent energy management. In: Abraham, A., Jain, L., Zwaag, B.J. (eds.) Innovations in Intelligent Systems, Part 2, pp. 285–314. Springer, Berlin (2004)Google Scholar
  2. 2.
    Khotanzad, A., Hwang, R.C., Abaye, A., Maratukulam, D.: An adaptive modular artificial neural network hourly load forecaster and its implementation at electric utilities. IEEE Trans. Power Syst. 10(3), 1716–1722 (1995)CrossRefGoogle Scholar
  3. 3.
    Kiartzis, S.J., Zoumas, C.E., Theocharis, J.B., Bakirtzis, A.G., Petridis, V.: Short-term load forecasting in an autonomous power system using artificial Neural Networks. IEEE Trans. Power Syst. 12(4), 1591–1596 (1997)CrossRefGoogle Scholar
  4. 4.
    Khotanzad, A., Afkhami-Rohani, R., Maratukulam, D.: ANNSTLF-artificial neural network short-term load forecaster-generation tree. IEEE Trans. Power Syst. 13(4), 1413–1422 (1998)CrossRefGoogle Scholar
  5. 5.
    Abraham, A.: An evolving fuzzy neural network model based reactive power control. In: Proceedings of the Second International Conference on Computers in Industry, pp. 247–253, Bahrain (2000)Google Scholar
  6. 6.
    Abraham, A., Nath, B.: A neuro-fuzzy approach for forecasting electricity demand in victoria. Appl. Soft Comput. 1(2), 127–138 (2001)CrossRefGoogle Scholar
  7. 7.
    Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Syst. 16(1), 44–55 (2001)CrossRefGoogle Scholar
  8. 8.
    Khan, M.R., Zak, L., Ondrusek, C.: Forecasting weekly load using a hybrid fuzzy-neural network approach. Int. J. Eng. Mech. 5, 327–336 (2001)Google Scholar
  9. 9.
    Bodyanskiy, Y., Popov, S., Rybalchenko, T.: Multilayer neuro-fuzzy network for short term electric load forecasting. In: Hirsch, E.A., Razborov, A.A., Semenov, A., Slissenko, A. (eds.) Third International Computer Science Symposium in Russia, CSR 2008 Moscow, Russia, 7–12 June 2008. Lecture Notes in Computer Science, vol. 5010, pp. 339–348, Springer, Berlin (2008)Google Scholar
  10. 10.
    Bodyanskiy, Y., Popov, S., Rybalchenko, T.: Feedforward neural network with a specialized architecture for estimation of the temperature influence on the electric load. In: Proceedings Forth International IEEE Conference on Intelligent Systems, Golden Sands Resort, vol. 1, pp. 7.14–7.18, Varna, Bulgaria (2008)Google Scholar
  11. 11.
    Bodyanskiy, Y., Otto, P., Babenko, A., Popov, S.: Neural network approach to signals parameters estimation in electric power systems. In: Proceedings 55 International Wiss. Koll. “Crossing Borders within ABC Automation, Biomedical Engineering and Computer Science”, pp. 173–179, Ilmenau, TUI (2010)Google Scholar
  12. 12.
    Roessler, F., Teich, T., Franke, S.: Neural networks for smart homes and energy efficiency. In: Katalinic, B. (ed.) DAAAM International Scientific Book, pp. 305–314. DAAAM International Publishing, Vienna (2012)Google Scholar
  13. 13.
    Reaz, M.B.I.: Artificial intelligence techniques for advanced smart home implementation. Acta Technica Corvininesis Bull. Eng. 6(2), 51–57 (2013)MathSciNetGoogle Scholar
  14. 14.
    Lee, S-H., Lee, S-J., Moon, K-I.: An ANFIS control system for smart home. Int. J. Adv. Comput. Technol. 5(11), 464–470 (2013)Google Scholar
  15. 15.
    Lee, S.-H., Lee, S.-J., Moon, K.-I.: Smart home security system using multiple ANFIS. Int. J. Smart Home 7(3), 121–132 (2013)MathSciNetGoogle Scholar
  16. 16.
    Kim, J.H., Lee, M.J. (eds.): Green IT: Technologies and Applications. Springer, Berlin (2011)Google Scholar
  17. 17.
    Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer, Berlin (2008)CrossRefzbMATHGoogle Scholar
  18. 18.
    Mumford, C.L.: Computational Intelligence Collaboration, Fusion and Emergence. Springer, Berlin (2009)zbMATHGoogle Scholar
  19. 19.
    Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P.: Computational Intelligence. A Methodological Introduction. Springer, Berlin (2013)zbMATHGoogle Scholar
  20. 20.
    Du, K.-L., Swamy, M.N.S.: Neural Networks and Statistical Learning. Springer, London (2014)CrossRefzbMATHGoogle Scholar
  21. 21.
    Aggarwal, C.C.: Data Mining: The Textbook. Springer, Switzerland (2015)CrossRefzbMATHGoogle Scholar
  22. 22.
    Bifet, A.: Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams. IOS Press, Amsterdam (2010)zbMATHGoogle Scholar
  23. 23.
    Aggarwal, C. (ed.): Data Stream: Models and Algorithms. Springer Science+Bussiness Media, LLC, N.Y. (2007)Google Scholar
  24. 24.
    Jang, R.J.-S.: ANFIS: Adaptive Network Based Fuzzy Inference Systems. IEEE Trans. Syst. Man Cybern. 23(3), 116–132 (1993)Google Scholar
  25. 25.
    Hastie, T., Tibshirani, R.: Generalized Additive Models. Chapman and Hall, London (1990)zbMATHGoogle Scholar
  26. 26.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2013)Google Scholar
  27. 27.
    Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and its Applications to Modeling and Control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)Google Scholar
  28. 28.
    Sugeno, M., Kang, G.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28(1), 15–33 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Yamakawa, T., Uchino, E., Miki, T., Kusanagi, H.: A neo-fuzzy neuron and its applications to system identification and prediction of the system behavior. In: Proceedings 2-nd International Conference on Fuzzy Logic and Neural Networks (IIZUKA-92), Iizuka, Japan, 17–22 July 1992, pp. 477–483 (1992)Google Scholar
  30. 30.
    Uchino, E., Yamakawa, T.: Soft computing based signal prediction, restoration and filtering. In: Ruan, D. (ed.) Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algorithms, pp. 331–349. Kluwer Academic Publishers, Boston (1997)CrossRefGoogle Scholar
  31. 31.
    Miki, T., Yamakawa, T.: Analog implementation of neo-fuzzy neuron and its on-board learning. In: Mastorakis, N.E. (ed.) Computational Intelligence and Application, pp. 144–149. WSES Press, Piraeus (1999)Google Scholar
  32. 32.
    Bodyanskiy, Y., Setlak, G., Pliss, I., Vynokurova, O.: Hybrid neuro-neo-fuzzy system and its adaptive learning algorithm. In: Proceedings of Xth IEEE International Scientific and Technical Conference “Computer Science and Information Technologies”, Lviv, Ukraine, Sept 2015, pp. 111–114 (2015)Google Scholar
  33. 33.
    Bodyanskiy, Y., Gorshkov, Y., Kolodyazhniy, V., Rvacheva, T.: Hybrid neuro-fuzzy network with variable shape basis function. In: Proceedings East West Fuzzy Colloquium, Zittau/Goerlitz, Germany, 13–15 Sept 2006, pp. 322–331 (2006)Google Scholar
  34. 34.
    Bodyanskiy, Y., Gorshkov, Y., Otto, P., Pliss, I.: Medical image analysis using neuro-fuzzy network. In: Proceedings of 54 International Scientific Colloquium. (IWK-2009) Information Technology and Electrical Engineering, Ilmenau, Germany, 9–12 Sept 2009, pp. 243–248 (2009)Google Scholar
  35. 35.
    Bodyanskiy, Y., Kokshenev, I., Kolodyazhniy, V.: An adaptive learning algorithm for a neo-fuzzy neuron. In: Proceedings of 3rd International Conference of European Union Society for Fuzzy Logic and Technology (EUSFLAT), Zittau, Germany, 10–12 Sept 2003, pp. 375–379 (2003)Google Scholar
  36. 36.
    Landim, R.P., Rodrigues, B., Silva, S.R., Caminhas, W.M.: A neo-fuzzy-neuron with real time training applied to flux observer for an induction motor. In: Proceedings of IEEE Vth Brazilian Symposium on Neural Networks, Belo Horizonte, 9–11 Dec 1998, pp. 67–72 (1998)Google Scholar
  37. 37.
    Ye, Bodyanskiy, Tyshchenko, O., Wojcik, W.: Multivariate non-stationary time series predictor based on an adaptive neuro-fuzzy approach. Elektronika 54(8), 10–13 (2013)Google Scholar
  38. 38.
    Otto, P., Ye, Bodyanskiy, Kolodyazhniy, V.: A new learning algorithm for a forecasting neuro-fuzzy network. Integr. Comput. Aided Eng. 10(4), 399–409 (2003)Google Scholar
  39. 39.
    Ye, Bodyanskiy, Vynokurova, O.: Hybrid type-2 wavelet-neuro-fuzzy network for businesses process prediction. Bus. Inform. 21, 9–21 (2011)Google Scholar
  40. 40.
    Deoras, A.: Electricity load forecasting using neural networks. In: Electricity Load and Price Forecasting Webinar Case Study (2011). http://www.mathworks.com/matlabcentral/fileexchange/28684-electricity-load-and-price-forecasting-webinar-case-study/content/Electricity%20Load%20&%20Price%20Forecasting/Load/html/LoadScriptNN.html. Data set from the New England ISO http://www.iso-ne.com/

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Yevgeniy Bodyanskiy
    • 1
    Email author
  • Olena Vynokurova
    • 1
  • Iryna Pliss
    • 1
  • Dmytro Peleshko
    • 2
  1. 1.Kharkiv National University of Radio ElectronicsKharkivUkraine
  2. 2.Lviv Politechnic National UniversityLvivUkraine

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