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|Title:||Interval type-2 fuzzy logic systems for load forecasting: A comparative study||Authors:||Khosravi, A.
type 2 fuzzy logic system
|Issue Date:||2012||Citation:||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. https://doi.org/10.1109/TPWRS.2011.2181981||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||URI:||http://scholarbank.nus.edu.sg/handle/10635/82565||ISSN:||08858950||DOI:||10.1109/TPWRS.2011.2181981|
|Appears in Collections:||Staff Publications|
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