A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

Hassan, Samia, Khosravi, Abbas, Jaafar, Jafreezal and Khanesar, Mojtaba Ahmadieh 2016, A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting, International journal of electrical power and energy systems, vol. 82, pp. 1-10, doi: 10.1016/j.ijepes.2016.03.001.

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Title A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting
Author(s) Hassan, Samia
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Jaafar, Jafreezal
Khanesar, Mojtaba Ahmadieh
Journal name International journal of electrical power and energy systems
Volume number 82
Start page 1
End page 10
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-11
ISSN 0142-0615
Keyword(s) Extreme learning machine
Interval type-2 fuzzy logic systems
Electricity load forecasting
Learning algorithm
Smart grid
Summary This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.
Language eng
DOI 10.1016/j.ijepes.2016.03.001
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
0906 Electrical And Electronic Engineering
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083614

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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