Title :
The hybrid model based on Hilbert-Huang Transform and neural networks for forecasting of short-term operation conditions of power system
Author :
Kurbatsky, Victor G. ; Sidorov, D.N. ; Spiryaev, V.A. ; Tomin, Nikita V.
Author_Institution :
Electr. Power Syst. Dept., Melentiev Energy Syst. Inst. SB RAS, Irkutsk, Russia
Abstract :
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.
Keywords :
Hilbert transforms; data analysis; load forecasting; neural nets; power engineering computing; HHT; Hilbert-Huang transform hybrid model; artificial neural networks; complex power systems; data analysis; data preprocessing; nonstationary process short-term forecasting; short-term operation conditions; two-stage intelligent approach; Adaptation models; Artificial neural networks; Forecasting; Load flow; Predictive models; Time series analysis; Transforms; Hilbert-Huang Transform; artificial neural networks; forecasting; hybrid model; operation conditions; power system;
Conference_Titel :
PowerTech, 2011 IEEE Trondheim
Conference_Location :
Trondheim
Print_ISBN :
978-1-4244-8419-5
Electronic_ISBN :
978-1-4244-8417-1
DOI :
10.1109/PTC.2011.6019155
