By Topic

The hybrid model based on Hilbert-Huang Transform and neural networks for forecasting of short-term operation conditions of power system

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Kurbatsky, V.G. ; Electr. Power Syst. Dept., Melentiev Energy Syst. Inst. SB RAS, Irkutsk, Russia ; Sidorov, D.N. ; Spiryaev, V.A. ; Tomin, N.V.

The paper addresses the conventional approaches to the short-term forecasting of nonstationary processes in complex power systems using the methodology of artificial neural networks (ANNs). In many practical cases the application of different ANNs can provide a satisfactory forecast. But data preprocessing and analysis can significantly improve the forecast. In this paper the Hilbert-Huang Transform (HHT) is used as one of the most promising tools in this area. Here we focus on HHT since this transform underlies the proposed two-stage intelligent approach to short-term forecasting of nonstationary processes.

Published in:

PowerTech, 2011 IEEE Trondheim

Date of Conference:

19-23 June 2011