Please use this identifier to cite or link to this item:
Title: Interval type-2 fuzzy logic systems for load forecasting: A comparative study
Authors: Khosravi, A.
Nahavandi, S.
Creighton, D.
Srinivasan, D. 
Keywords: Load forecasting
prediction interval
type 2 fuzzy logic system
Issue Date: 2012
Source: Khosravi, A., Nahavandi, S., Creighton, D., Srinivasan, D. (2012). Interval type-2 fuzzy logic systems for load forecasting: A comparative study. IEEE Transactions on Power Systems 27 (3) : 1274-1282. ScholarBank@NUS Repository.
Abstract: Accurate short term load forecasting (STLF) is essential for a variety of decision-making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with additional degrees of freedom, are an excellent tool for handling uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models precisely approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks and traditional type-1 Takagi-Sugeno-Kang (TSK) FLSs. © 2012 IEEE.
Source Title: IEEE Transactions on Power Systems
ISSN: 08858950
DOI: 10.1109/TPWRS.2011.2181981
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Mar 19, 2018


checked on Mar 19, 2018

Page view(s)

checked on Mar 11, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.