V.Yu. Ilichev – Ph. D. (Eng.), Associate Professor, 

«Heat Engines and Hydraulic Machines» Department, Kaluga Branch of Bauman MSTU

E-mail: patrol8@yandex.ru 

I.V. Chukhraev – Ph. D. (Eng.), Associate Professor, Head of «Information Systems and Networks» Department, Kaluga Branch of Bauman MSTU

E-mail: chukhraev@bmstu-kaluga.ru

E.A. Yurik – Ph. D. (Eng.), Associate Professor, 

«Heat Engines and Hydraulic Machines» Department, Kaluga Branch of Bauman MSTU

E-mail: patrol8@yandex.ru

In order to increase efficiency of power equipment use, ensure its most complete loading, reduce fuel consumption, it is necessary to predict electric power consumption most accurately for a certain period of time. This need arises from the fact that any units, particularly powerful ones, take a certain time to start or change the mode of operation when the electrical load changes. The forecasting of electricity consumption is also necessary to improve the production process of industrial enterprises, whose budget depends to a large extent on the volume and cost of energy supplied by sales companies. If there is a forecast, this cost item may be more accurately taken into account when forming the enterprise budget.

The article discusses the use of the methods included in the statistical numerical analysis program STATISTICA to predict the volume of energy consumption in electric networks. This software product allows to use almost all classical and modern methods of statistical information analysis for forecast preparation, as well as has means of visualization of initial data and forecast results.

To solve the problem, a means of «spectral analysis» is used, which implements an algorithm of decomposition of time data into a Fourier series, as well as a means of «neural network programming», which is indispensable in case in the initial information it is impossible to detect explicit periodic dependencies. This is the structure that differs between the data used as the array for the analysis. Based on the results of the analysis, conclusions were drawn on the solved task, recommendations were given on the application of the considered methods for forecasting energy consumption in electric networks.

The studies carried out are relevant, as they make it possible to increase efficiency and profitability of operation of both large and regional electric networks, energy companies and industrial enterprises of Russia. Taking into account further improvement, the described methodology should help to more successfully solve the tasks set by the governing bodies of the State within the framework of national projects.

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