DocumentCode :
2171153
Title :
Application of adaptive network-based fuzzy inference system in short term load forecasting
Author :
Saha, A.K. ; Chowdhury, S.P. ; Chowdhury, S.
Author_Institution :
Jadavpur Univ., Kolkata
fYear :
2007
fDate :
20-22 Dec. 2007
Firstpage :
168
Lastpage :
174
Abstract :
A number of computing models based on adaptive network- based fuzzy inference system (ANFIS) are proposed in this paper to forecast peak load demands of an electric power utility. The models are capable of forecasting the peak demands both week-days and weekend-days i.e. Sundays and holidays as well. At the same time the models possess adaptability to the changing data pattern which may occur in case the load demand pattern changes or the weather parameters change. The paper involves forecasting models with zero order and first order Sugeno model of ANFIS with various types of membership functions and optimization method combinations. The proposed models are validated using load demand data of a real power utility to forecast its peak demand.
Keywords :
fuzzy set theory; inference mechanisms; load forecasting; adaptive network-based fuzzy inference system; electric power utility; first order Sugeno model; optimization method combinations; short term load forecasting; zero order forecasting models; Adaptive network; Sugeno models; adaptive neuro fuzzy inference system (ANFIS); artificial neural network (ANN); short-term load forecasting;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Information and Communication Technology in Electrical Sciences (ICTES 2007), 2007. ICTES. IET-UK International Conference on
Conference_Location :
Tamil Nadu
ISSN :
0537-9989
Type :
conf
Filename :
4735789
Link To Document :
بازگشت