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Choice of the method for solving problems of short-term forecasting of CHPP auxiliary power consumption

A.S. Vedernikov, E.A. Yarygina, A.V. Gofman

Vestnik IGEU, 2018 issue 6, pp. 32—38

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Abstract in English: 

Background. The scientific problem of this research is prediction of power consumption of power plant auxiliary supply with a minimum error. Short-term forecasting problems have been previously solved at the level of electric power systems and industrial enterprises. As for forecasting auxiliary power consumption, retrospective data on power consumption have been used as forecast values. This problem remains relevant in accordance with RF Government Decree No. 1172 of December 27, 2010, which states that power plants assume responsibility for the consumed electricity exceeding the established limits. A 2 or more % deviation in consumption from the set value leads to additional costs. The aim of the study is, therefore, to select a method of predicting values of CHPP auxiliary power consumption with a low error.

Materials and methods. To solve the problems of short-range forecasting, we have chosen a method based on artificial neural networks and trained these networks by using numerical optimization methods: the Broyden-Fletcher-Goldfarb-Shanno learning algorithm, Conjugate gradient method, Gradient descent method which have been earlier used for solving various practical problems in the electric power industry. The software package Statistica Neural Networks has been used by us to determine the hourly values of the electric load of CHPP auxiliary needs.

Results. We have chosen the method based on the artificial neural networks «multilayer perceptron» and the Broyden-Fletcher-Goldfarb-Shanno algorithm for its training, which allows CHPPs to predict auxiliary power consumption with the mean absolute error of 0,43 %.

Conclusions. The proposed method of short-term prediction of auxiliary power consumption at CHPP has been tested and approved at the Branch of JSC «SO UES» of the Interregional Dispatching Office of the Middle Volga for estimating predicted power consumption values of power plants in the process of electricity balance planning.

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Key words in Russian: 
прогнозирование электропотребления, собственные нужды ТЭЦ, искусственные нейронные сети, алгоритмы обучения, ошибка прогноза
Key words in English: 
power consumption forecasting, CHPP auxiliary power, artificial neural networks, learning algorithms, forecast error
The DOI index: 
10.17588/2072-2672.2018.6.032-038
Downloads count: 
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