Open AccessGeneration of a rational training sample when predicting power consumption for train traction
Author(s) -
Valeriya Е. Osipova,
Dmitrij А. Yakovlev
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/760/1/012040
Subject(s) - artificial neural network , consumption (sociology) , sample (material) , energy consumption , computer science , division (mathematics) , power consumption , power balance , power (physics) , approximation error , fuel efficiency , electric power , traction (geology) , automotive engineering , artificial intelligence , engineering , electrical engineering , mathematics , algorithm , mechanical engineering , social science , chemistry , physics , arithmetic , chromatography , quantum mechanics , sociology
The paper considers peculiarities of forecasting in the field of railroad facilities based on approximation of time series and neural networks. The objects of traction power supply of the Transbaikal Railway are considered as the object of the study. Forecasting on the basis of approximation for consumers with maximum power consumption increases the accuracy of forecasting the electric energy consumption by 4…7%. The application of neural networks in forecasting of power consumption allows reducing the value of error to 2%, thus leading to significant reduction of costs for fuel and energy balance of a structural division or an enterprise as a whole.