Vestnik YuUrGU. Ser. Mat. Model. Progr., 2015, Volume 8, Issue 4, Pages 127–130
This article is cited in 3 scientific papers (total in 3 papers)
Modelling of the time series digressions by the example of the UPS of the Ural
V. G. Mokhov, T. S. Demyanenko
South Ural State University, Chelyabinsk, Russian Federation
The article oversees forecasting model for deviations of the balancing market index and day-ahead market index according to the maximum similarity sample for different levels of approximation in the context of positive and negative time-series value. The model is being tested on the factual data of the Integrated Power system of the Ural, Wholesale market for electricity and power of the Russian Federation. The offered model is based on the sample of maximum similarity of the daily digressions by “Day-ahead” market from balancing market index in the history data of 2009–2014 that was acquired from an official web-site of the wholesale electric power market. Testing of the mathematical model gave the prediction error of 3,3%. The offered toolkit for forecasting of the main day-ahead and balancing market parameters is recommended to use for operational work of the industrial enterprise.
forecasting models of the main parameters of Russia Energy Market; testing models for the Integrated Power system of the Ural.
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V. G. Mokhov, T. S. Demyanenko, “Modelling of the time series digressions by the example of the UPS of the Ural”, Vestnik YuUrGU. Ser. Mat. Model. Progr., 8:4 (2015), 127–130
Citation in format AMSBIB
\by V.~G.~Mokhov, T.~S.~Demyanenko
\paper Modelling of the time series digressions by the example of the UPS of~the~Ural
\jour Vestnik YuUrGU. Ser. Mat. Model. Progr.
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This publication is cited in the following articles:
V. G. Mokhov, T. S. Demyanenko, I. P. Ostanin, “Energy consumption modelling using neural networks of direct distribution on example of Russia united power system”, J. Comp. Eng. Math., 3:4 (2016), 73–78
T. S. Demyanenko, “Model of short-term forecast of electrical energy consumption of Ural United Power System by separating of a maximal similarity sample into the positive and negative levels”, J. Comp. Eng. Math., 4:3 (2017), 11–18
V. G. Mokhov, V. I. Tsimbol, “Electrical energy consumption prediction of the federal district of Russia on the based of the reccurent neural network”, J. Comp. Eng. Math., 5:2 (2018), 3–15
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