name
Zueva Victoria Nicolaevna
Scholastic degree
•
Academic rank
associated professor
Honorary rank
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Organization, job position
• Kuban State Technological University
доцент кафедры внутризаводского электрооборудования и автоматики
Research interests
системы искусственного интеллекта, нейронные сети, системы поддержки принятия решений, математическое моделирование в электроэнергетике
Web site url
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Current rating (overall rating of articles)
0
TOP5 co-authors
Articles count: 2
Сформировать список работ, опубликованных в Научном журнале КубГАУ
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NEURAL NETWORK PREDICTION MODULE FOR ELECTRICITY CONSUMPTION
DescriptionIn 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
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REGRESSIVE METHODS OF PROGNOSTICATION OF THE LOAD-GRAPH OF ELECTRICAL EQUIPMENT
DescriptionThe 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