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Interval type-2 fuzzy logic systems for load forecasting : a comparative study
journal contribution
posted on 2012-01-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas Creighton, D SrinivasanAccurate 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.
History
Journal
IEEE transactions on power systemsVolume
27Issue
3Pagination
1274 - 1282Publisher
IEEELocation
Piscataway, N. JISSN
0885-8950eISSN
1558-0679Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2012, IEEEUsage metrics
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