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2019 | OriginalPaper | Chapter

Models and Algorithms for Prediction of Electrical Energy Consumption Based on Canonical Expansions of Random Sequences

Authors : Igor Atamanyuk, Volodymyr Kondratenko, Yuriy Kondratenko, Vyacheslav Shebanin, Marina Solesvik

Published in: Green IT Engineering: Social, Business and Industrial Applications

Publisher: Springer International Publishing

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Abstract

The given chapter is devoted to the development of the mathematical support, in particular, mathematical models and algorithms, which can be successfully used for solving prediction tasks in various areas of human activity, including energetic and ecological management. The development peculiarities and the use of models and algorithms as elements of green technology to predict electric energy consumption based on mathematical apparatus of canonical expansions of random sequences are currently being discussed. Developed calculation method doesn’t impose any limitations on the qualities of random sequences of the change of electric energy consumption (requirement of linearity, Markovian behavior, monotony, stationarity etc.) and has maximal accuracy characteristics in this connection. Block diagrams of algorithms and results of the applied realization of the developed models and algorithms, for example the prediction of electric energy consumption by one of the local neighborhoods in Mykolaiv, Ukraine are introduced in the work. Comparative analysis of the results of a numerical experiment with the use of a Kalman filter and the linear prediction method confirms the high efficiency of the developed models and algorithms (relative error of prediction of electric energy consumption is 2–3%).

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Socolow, R.H. (ed.): Industrial Ecology and Global Change, 532 p. Cambridge University Press (2006)

Despeisse, M., Ball, P.D., Evans, S., Levers, A.: Industrial ecology at factory level—a conceptual model. J. Clean. Prod. 31, 30–39 (2012)CrossRef

Kalinichenko, A., Havrysh, V., Perebyynis, V.: Sensitivity analysis in investment project of biogas plant. J. Appl. Ecol. Environ. Res. 15(4), 969–985 (2017)CrossRef

Box, G.E.P., Jenkins, G.M.: Time–Series Analysis, Forecasting and Control, 575 p. Holden–Day, San Francisco (1970)

Pugachev, V.S.: Theory of Random Functions and its Application Control Problem, 833 p. Pergamon Press, London (2013)

Kolmogorov, A.N.: Foundations of the Theory of Probability. Chelsea Publishing Co., Oxford (1950)

Dorozhovets, M., Bubela, I.: Computing uncertainty of the extreme values in random samples. Int. J. Comput. 15(2), 127–135 (2016). http://computingonline.net/computing/article/view/845

Gomolka, Z., Dudek-Dyduch, E., Kondratenko, Y.P.: From homogeneous network to neural nets with fractional derivative mechanism. In: Rutkowski, L. et al. (eds.) International Conference on Artificial Intelligence and Soft Computing, ICAISC-2017, Part I, Zakopane, Poland, June 11–15 2017, LNAI 10245, pp. 52–63. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59063-9_5

Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME Ser. D, J. Basic Eng. 82(Series D), 35–45 (1960)CrossRef

10.

Simon, D.: Training fuzzy systems with the extended Kalman filter. Fuzzy Sets Syst. 132, 189–199 (2002)MathSciNetCrossRef

11.

Wiener, N.: Extrapolation, Interpolation, and Smoothing of Stationary Time Series: With Engineering Applications. MIT Press, New-York (1949)MATH

12.

Kharchenko, V., et al. (eds.): Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol. 74. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44162-7

13.

Kharchenko, V., et al. (eds.): Green IT Engineering: Components, Networks and Systems Implementation. Studies in Systems, Decision and Control, vol. 105. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55595-9

14.

Kharchenko, V., Illiashenko, O.: Concepts of green IT engineering: taxonomy, principles and implementation. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol 74, pp. 3–19. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44162-7_1

15.

Basile, D., Di Giandomenico, F., Gnesi, S.: Model-based evaluation of energy saving systems. In: Kharchenko V., Kondratenko Y., Kacprzyk J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol 74, pp. 187–208. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44162-7_10

16.

Palagin, A.V., Opanasenko, V.M., Kryvyi, S.I.: Resource and energy optimisation oriented development of FPGA-based adaptive logical networks for classification problem. In: Kharchenko V., Kondratenko Y., Kacprzyk J. (eds.) Green IT Engineering: Components, Networks and Systems Implementation. Studies in Systems, Decision and Control, vol. 105, pp. 195–218. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55595-9_16CrossRef

17.

Drozd, J., Drozd, A., Maevsky, D., Shapa, L.: The levels of target resources development in computer systems. In: Proceedings of IEEE East-West Design & Test Symposium (EWDTS 2014), 26–29 Sept 2014, Kiev, Ukraine, pp. 1–5. https://doi.org/10.1109/EWDTS.2014.7027104

18.

Huang, M., Li, X.: Optimal adaptive model for heating energy consumption forecasting. Revista de la Facultad de Ingeniería U.C.V. 32(10), 252–260 (2017)

19.

Kondratenko, Y., Korobko, O.V., Kozlov, O.V.: PLC-based systems for data acquisition and supervisory control of environment-friendly energy-saving technologies. In: Kharchenko V., Kondratenko Y., Kacprzyk J. (eds.), Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol 74, pp. 247–267. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44162-7_13

20.

Kondratenko, Y., Korobko, V., Korobko, O., Kondratenko, G., Kozlov, O.: Green-IT approach to design and optimization of thermoacoustic waste heat utilization plant based on soft computing. In: Kharchenko V., Kondratenko Y., Kacprzyk J. (eds.) Green IT Engineering: Components, Networks and Systems Implementation. Studies in Systems, Decision and Control, vol 105, pp. 287–311. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55595-9_14CrossRef

21.

Fesenko, H., Kharchenko, V., Brezhnev, E., Zaitseva, E., Levashenko, V.: Markov model of smart grid digital substations availability: multi-level degradation and recovery of power resources issues. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Components, Networks and Systems Implementation. Studies in Systems, Decision and Control, vol 105, pp. 339–355. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55595-9_16CrossRef

22.

Bodyanskiy, Y., Vynokurova, O., Pliss, I., Peleshko, D.: Hybrid adaptive systems of computational intelligence and their on-line learning for green IT in energy management tasks. In: Kharchenko V., Kondratenko Y., Kacprzyk J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol. 74, pp. 229–244. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44162-7_12

23.

Kudritsky, V.D.: Filtering, Extrapolation and Recognition Realizations of Random Functions. FADA Ltd., Kyiv (2001). (in Russian)

24.

Belan, V.V., Osadchiy, S.I.: Using canonical decomposition of spectral matrices to factor them. J. Autom. Inf. Sci. 27(2), 57–62 (1995)

25.

Atamanyuk, I., Kondratenko, Y.: Calculation method for a computer’s diagnostics of cardiovascular diseases based on canonical decompositions of random sequences. In: Batsakis, S., et al. (eds.) ICT in Education, Research and Industrial Applications: Integration, Harmonization and Knowledge Transfer. Proceedings of the 11th International Conference ICTERI-2015, CEUR-WS, vol. 1356, pp. 108–120, Lviv, Ukraine, May 14–16 (2015)

26.

Atamanyuk, I.P., Kondratenko, V.Y., Kozlov, O.V., Kondratenko, Y.P.: The algorithm of optimal polynomial extrapolation of random processes. In: Engemann, K.J., Gil-Lafuente, A.M., Merigo, J.L. (eds.) Modeling and Simulation in Engineering, Economics and Management. International Conference MS 2012, New Rochelle, NY, USA, 30 May–1 June 2012, Proceedings. Lecture Notes in Business Information Processing, vol. 115, pp. 78–87. Springer (2012). https://doi.org/10.1007/978-3-642-30433-0_9

27.

Atamanyuk, I., Kondratenko, Y., Shebanin, V., Mirgorod, V.: Method of polynomial predictive control of fail-safe operation of technical systems. In: Proceedings of XIIIth International Conference “The Experience of Designing and Application of CAD Systems in Microelectronics”, CADSM 2015, 19–23 Feb 2015, Polyana-Svalyava, Ukraine, pp. 248-251. https://doi.org/10.1109/CADSM.2015.7230848

28.

Atamanyuk, I.P.: Algorithm of extrapolation of a nonlinear random process on the basis of its canonical decomposition. J. Kibernetika i Sistemnyj Analiz, №2, 131–138 (2005)

29.

Atamanyuk, I., Kondratenko, Y.: Computer’s analysis method and reliability assessment of fault-tolerance operation of information systems. In: Batsakis, S., et al. (eds.) ICT in Education, Research and Industrial Applications: Integration, Harmonization and Knowledge Transfer. Proceedings of the 11th International Conference ICTERI-2015, CEUR-WS, 14–16 May, vol. 1356, pp. 507–522, Lviv, Ukraine (2015)

30.

Atamanyuk, I.P.: Optimal polynomial extrapolation of realization of a random process with a filtration of measurement errors. J. Autom. Inf. Sci. 41(8), 38–48 (2009)MathSciNetCrossRef

31.

Atamanyuk, I., Kondratenko, Y., Sirenko, N.: Management system for agricultural enterprise on the basis of its economic state forecasting, complex systems: solutions and challenges in economics, management and engineering. Berger-Vachon, C., et al. (eds.) Studies in Systems, Decision and Control, vol. 125, pp. 453–470. Springer International Publishing, Berlin (2018). https://doi.org/10.1007/978-3-319-69989-9_27

Title: Models and Algorithms for Prediction of Electrical Energy Consumption Based on Canonical Expansions of Random Sequences
Authors: Igor Atamanyuk
Volodymyr Kondratenko
Yuriy Kondratenko
Vyacheslav Shebanin
Marina Solesvik
Publisher: Springer International Publishing
Book: Green IT Engineering: Social, Business and Industrial Applications
Print ISBN: 978-3-030-00252-7

Electronic ISBN: 978-3-030-00253-4

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-00253-4_17

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