Scientific Journal of KubSAU

Polythematic online scientific journal
of Kuban State Agrarian University
ISSN 1990-4665
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Name

Zueva Victoria Nicolaevna

Scholastic degree


Academic rank

associated professor

Honorary rank

—

Organization, job position

Kuban State Technological University
   

Web site url

—

Email

victoria_zueva@list.ru


Articles count: 2

703 kb

NEURAL NETWORK PREDICTION MODULE FOR ELECTRICITY CONSUMPTION

abstract 1321708107 issue 132 pp. 1323 – 1332 31.10.2017 ru 395
In this work, we consider the design and development of neural network software module for prediction of electricity consumption in the system of support of decision-making power control. Two prediction models support the software module: regression model and neural network model, based on multilayer perceptron. Software development to predict power consumption in the system of decision-making today is one of the priority directions in the Russian power industry. Therefore, the work associated with the development of methods and algorithms of forecasting of power consumption in the power sector, is surely relevant
296 kb

REGRESSIVE METHODS OF PROGNOSTICATION OF THE LOAD-GRAPH OF ELECTRICAL EQUIPMENT

abstract 1261702008 issue 126 pp. 119 – 130 28.02.2017 ru 640
The article discusses the use of regression methods of forecasting the deterministic time series on the example of the load curve. Forecasts of the load curve of electrical equipment are the demands of consumers and their security in EPS. All predictive tasks are based on prediction models. Electricity consumption is happening on an electronic level; storing electricity on an industrial scale is impossible, the consumption depends on many random factors. Therefore, generally, we use a combination of mathematical and heuristic models. This is the daily task of power systems and many technical, economic and commercial decisions on the management regimes depend on its solutions. Development of methods of forecasting of the energy consumption in the system of decision-making today is one of the priority directions in the Russian power industry. Therefore, the work associated with the development of methods and algorithms of forecasting of power consumption in the power sector is still relevant
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