Day-Ahead Short-Term Forecasting Electricity Load via Approximation

Content Provider	Scilit
Author	Khamitov, R. N. Gritsay, A. S. Tyunkov, Dmitry Sinitsin, G. E.
Copyright Year	2017
Description	Journal: Iop Conference Series: Materials Science and Engineering The method of short-term forecasting of a power consumption which can be applied to short-term forecasting of power consumption is offered. The offered model is based on sinusoidal function for the description of day and night cycles of power consumption. Function coefficients – the period and amplitude are set up is adaptive, considering dynamics of power consumption with use of an artificial neural network. The presented results are tested on real retrospective data of power supply company. The offered method can be especially useful if there are no opportunities of collection of interval indications of metering devices of consumers, and the power supply company operates with electrical supply points. The offered method can be used by any power supply company upon purchase of the electric power in the wholesale market. For this purpose, it is necessary to receive coefficients of approximation of sinusoidal function and to have retrospective data on power consumption on an interval not less than one year.
Related Links	http://iopscience.iop.org/article/10.1088/1757-899X/189/1/012005/pdf
ISSN	17578981
e-ISSN	1757899X
DOI	10.1088/1757-899x/189/1/012005
Journal	Iop Conference Series: Materials Science and Engineering
Issue Number	1
Volume Number	189
Language	English
Publisher	IOP Publishing
Publisher Date	2017-04-18
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Materials Science and Engineering Characterization and Testing of Materials Artificial Neural Network Power Consumption Term Forecasting Sinusoidal Function
Content Type	Text
Resource Type	Article