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Erschienen in:
Concepts of Green IT Engineering: Taxonomy, Principles and Implementation Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

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

verfasst von : Yevgeniy Bodyanskiy, Olena Vynokurova, Iryna Pliss, Dmytro Peleshko

Erschienen in: Green IT Engineering: Concepts, Models, Complex Systems Architectures

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel MSS Models of Smart Grids with Multi-level Degradation and Recovery
Nächstes Kapitel PLC-Based Systems for Data Acquisition and Supervisory Control of Environment-Friendly Energy-Saving Technologies
Literatur
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)
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)CrossRef
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)CrossRef
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)CrossRef
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)
6.
Abraham, A., Nath, B.: A neuro-fuzzy approach for forecasting electricity demand in victoria. Appl. Soft Comput. 1(2), 127–138 (2001)CrossRef
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)CrossRef
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)
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)
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)
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)
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)
13.
Reaz, M.B.I.: Artificial intelligence techniques for advanced smart home implementation. Acta Technica Corvininesis Bull. Eng. 6(2), 51–57 (2013)MathSciNet
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)
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)MathSciNet
16.
Kim, J.H., Lee, M.J. (eds.): Green IT: Technologies and Applications. Springer, Berlin (2011)
17.
Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer, Berlin (2008)CrossRefMATH
18.
Mumford, C.L.: Computational Intelligence Collaboration, Fusion and Emergence. Springer, Berlin (2009)MATH
19.
Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P.: Computational Intelligence. A Methodological Introduction. Springer, Berlin (2013)MATH
20.
Du, K.-L., Swamy, M.N.S.: Neural Networks and Statistical Learning. Springer, London (2014)CrossRefMATH
21.
22.
Bifet, A.: Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams. IOS Press, Amsterdam (2010)MATH
23.
Aggarwal, C. (ed.): Data Stream: Models and Algorithms. Springer Science+Bussiness Media, LLC, N.Y. (2007)
24.
Jang, R.J.-S.: ANFIS: Adaptive Network Based Fuzzy Inference Systems. IEEE Trans. Syst. Man Cybern. 23(3), 116–132 (1993)
25.
Hastie, T., Tibshirani, R.: Generalized Additive Models. Chapman and Hall, London (1990)MATH
26.
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2013)
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)
28.
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)
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)CrossRef
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)
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)
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)
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)
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)
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)
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)
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)
39.
Ye, Bodyanskiy, Vynokurova, O.: Hybrid type-2 wavelet-neuro-fuzzy network for businesses process prediction. Bus. Inform. 21, 9–21 (2011)
40.
Metadaten
Titel
Hybrid Adaptive Systems of Computational Intelligence and Their On-line Learning for Green IT in Energy Management Tasks
verfasst von
Yevgeniy Bodyanskiy
Olena Vynokurova
Iryna Pliss
Dmytro Peleshko
Copyright-Jahr
2017
Buch
Green IT Engineering: Concepts, Models, Complex Systems Architectures

Print ISBN: 978-3-319-44161-0

Electronic ISBN: 978-3-319-44162-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-44162-7_12