Logo

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

Hassan, S. and Khosravi, A. and Jaafar, J. and Khanesar, M.A. (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, 82 . pp. 1-10.

Full text not available from this repository.

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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. © 2016 Elsevier Ltd. All rights reserved.

Item Type:Article
Impact Factor:cited By 46
Uncontrolled Keywords:Commerce; Computer circuits; Design; Electric industry; Electric load forecasting; Electric power transmission networks; Forecasting; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Knowledge acquisition; Learning algorithms; Learning systems; Membership functions; Power markets; Reconfigurable hardware; Smart power grids, Adaptive neuro-fuzzy inference system; Electricity load forecasting; Extreme learning machine; Interval type-2 fuzzy logic systems; Moore Penrose pseudo inverse; National electricity markets; Singlehidden layer feed-forward neural network (SLFN); Smart grid, Fuzzy inference
ID Code:25700
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:27 Aug 2021 09:40
Last Modified:27 Aug 2021 09:40

Repository Staff Only: item control page