RECURRENT ANALYSIS OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE

Authors

DOI:

https://doi.org/10.30837/ITSSI.2023.23.014

Keywords:

recurrent diagram; energy consumption; time series; non-linear dynamics

Abstract

The subject of the study is the study of models and methods for short-term forecasting of energy consumption in power systems based on recurrent analysis of time series. The aim of the work is a recurrent analysis of the time series of energy consumption of a metallurgical enterprise and the development of a program in the Matlab environment for automating calculations and experimental testing of data available for research in PJSC Electrometallurgical Plant "Dniprospetsstal" named after A. M. Kuzmin. The following tasks have been solved: the methodology for constructing recurrent diagrams and their quantitative analysis have been considered; a model of the time series and the phase trajectory of the time series was built to visualize the change in energy consumption during the day; software for constructing recurrent diagrams in the Matlab package was developed. Methods were used: analysis of time series based on recursive analysis to study the characteristics of the state of the system on the example of a metallurgical enterprise. The results were obtained: software was developed in the Matlab environment for short-term forecasting of energy consumption in power systems, and quantitative indicators were calculated that can be used to characterize the state of the system and analyze energy consumption in the summer and winter seasons. Conclusions: in the course of the study, software for constructing and quantitative analysis of recurrence diagrams in the Matlab package was developed, with the help of which patterns were discovered and information about the properties of the system under study was obtained. Based on the analysis of the average values of quantitative measures in the off-season for 2018–2021, it can be seen that the summer period is characterized by greater predictability, as well as a significantly higher latency indicator, which characterizes the average time when the system can spend in a more or less unchanged state. Confirmed on real data, the benefits of using the recursive analysis method for estimating electricity consumption, as well as more efficient modeling of this process, can lead to an increase in the accuracy of forecasting its future dynamics.

Author Biographies

Anna Bakurova, National University "Zaporizhzhia Polytechnic"

Doctor of Sciences (Economics)

Iryna Divocha, National University "Zaporizhzhia Polytechnic"

Master of the Department of Systems Analysis and Computational Mathematics

Sergiy Kiyko, Chairman of the Board PJSC Electrometallurgical Plant “Dniprospetsstal” named after A.M. Kuzmin, Zaporozhye

Doctor of Technical Sciences

Olesia Yuskiv, National University "Zaporizhzhia Polytechnic"

Postgraduate of the Department of Systems Analysis and Computational Mathematics

References

References

Mazur V. L. (2010), Metallurgy of Ukraine: camp, competitiveness, prospects, Metallurgical and mining industry, Vol. 2, P. 12–16.

Kiyko S. G. (2021), Methodology of predictive adaptation of energy saving project portfolio management at metallurgical enterprises. – Qualification of scientific work on the rights of a manuscript. Dissertation for the degree of doctor of technical sciences for the specialty 05.13.22 – project management and programs. – Private joint-stock company "Electrometallurgical plant "Dniprospetsstal" named after A. M. Kuzmin", Zaporizhzhya, 420 p.

New methods of analysis and forecasting of hourly series in financial markets, available at: // http://www.rusnauka.com/13_EISN_2013/Economics/4_136384.doc.htm (last accessed: 11.04.2022).

Eckmann, J., Kamphorst, S., Ruelle (1987), Recurrence Plots of Dynamical Systems. Europhysics Letters, No. 4 (9), P. 973 – 977.

Butko M. (2021), Model of thematic interpretation of view images, Innovative technologies and scientific solutions for industries, No. 2(16), P. 5 – 11. DOI: https://doi.org/10.30837/ITSSI.2021.16.005

Zou Y., Romano MC., Thiel M., Marwan M., Kurths J. (2011), Inferring direct coupling by means of recurrences, International Journal of Bifurcation and Chaos, Vol. 21, No. 21 (4), P. 1099 – 1111. DOI: 10.1142/S0218127411029033

Thiel M., Romano MC., Kurths J., Rolfs M., Klieg R. (2008), Generating surrogates from recurrences. Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences, No. 366, P. 545 –557.

Piskun O.V. (2011), Peculiarities of using recurrent charts and recurrent quantitative analysis for the study of financial time series, Financial space, No. 3 (4), P. 111 – 118.

Vasyuta K. S., Tansyura O. B., Revin O. V. (2013), Development of radio signal detection methods in radio engineering systems using recurrent analysis, Science and technology of the Air Force of the Armed Forces of Ukraine, No. 2 (11), P. 135 – 139.

Kiselev V. B. (2007), Determining the stability of the process trajectory in the phase space with the help of recurrent analysis, Scientific and technical bulletin of information technologies, mechanics and optics of SPbSU ITMO, No. 6 (40), P. 121 – 130.

Kaminsky R.M., Runner G.V. (2015), Construction of recurrent charts of short time series using MS Excel, Bulletin of Lviv Polytechnic National University, No. 2 (82), P. 152 – 161.

Takens F. (1981), Detecting strange attractors in turbulenc. Dynamical Systems and Turbulence. Lecture Notes in Mathematics, edited by D.A. Rand L.S. Young. Heidelberg: Springer-Verlag, 366 p.

Kirichenko L.O., Kobytskaya Yu.A., Demyna N.A. (2015), Analysis and recognition of realizations of signals possessing fractal properties, Bionics of intelligence, No. 1 (84), P. 49 – 56.

Marwan N., Romano M. C., Thiel M., Kurths J. (2007), Recurrence Plots for the Analysis of Complex Systems, Physics Reports, 438 p.

Choi J. M., Bae B. H., Kim S. Y. (1999), Divergence in perpendicular recurrence plot; quantification of dynamical divergence from short chaotic time series, Physics Letters A, No. 26 (4 – 6), P. 299 – 306.

Bakurova A.V., Divocha I.O., Kiyko S.G., Yuskiv O.I., Recurrent analysis of energy consumption of a metallurgical enterprise, Science Week 2022: annual scientific-practical conf. teachers, scientists, young scientists, post-graduate students and students of higher education of Zaporizhzhya Polytechnic National University, April 18–22, 2022, P. 884 – 886.

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Published

2023-04-21

How to Cite

Bakurova, A., Divocha, I., Kiyko, S., & Yuskiv, O. (2023). RECURRENT ANALYSIS OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE. INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, (1 (23), 14–24. https://doi.org/10.30837/ITSSI.2023.23.014