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In this paper, a methodology, Self-Developing and Self-Adaptive Fuzzy Neural Networks using Type-2 Fuzzy Bayesian Ying-Yang Learning (SDSA-FNN-T2FBYYL) algorithm and multi-objective optimization is proposed. The features of this methodology are as follows: (1) A Bayesian Ying-Yang Learning (BYYL) algorithm is used to construct a compact but high-performance system automatically. (2) A novel multi-objective T2FBYYL is presented that integrates the T2 fuzzy theory with BYYL to automatically construct its best structure and better tackle various data uncertainty problems simultaneously. (3) The weighted sum multi-objective optimization technique with combinations of different weightings is implemented to achieve the best trade-off among multiple objectives in the T2FBYYL. The proposed methods are applied to electric load forecast using a real operational dataset collected from Macao electric utility. The test results reveal that the proposed method is superior to other existing relevant techniques.


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Adaptive Electric Load Forecaster

Show Author's information Mingchui Dong( )Chinwang Lou
Department of Electrical and Computer Engineering, University of Macau, Macao 999078, China.

Abstract

In this paper, a methodology, Self-Developing and Self-Adaptive Fuzzy Neural Networks using Type-2 Fuzzy Bayesian Ying-Yang Learning (SDSA-FNN-T2FBYYL) algorithm and multi-objective optimization is proposed. The features of this methodology are as follows: (1) A Bayesian Ying-Yang Learning (BYYL) algorithm is used to construct a compact but high-performance system automatically. (2) A novel multi-objective T2FBYYL is presented that integrates the T2 fuzzy theory with BYYL to automatically construct its best structure and better tackle various data uncertainty problems simultaneously. (3) The weighted sum multi-objective optimization technique with combinations of different weightings is implemented to achieve the best trade-off among multiple objectives in the T2FBYYL. The proposed methods are applied to electric load forecast using a real operational dataset collected from Macao electric utility. The test results reveal that the proposed method is superior to other existing relevant techniques.

Keywords: load forecaster, Bayesian Ying-Yang learning algorithm, type-2 fuzzy theory

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Publication history

Received: 17 December 2012
Revised: 07 July 2014
Accepted: 15 November 2014
Published: 23 April 2015
Issue date: April 2015

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© The author(s) 2015

Acknowledgements

This study was supported by the Research Committee of University of Macau with Grant No. MYRG2014-00060-FST and the Science and Technology Development Fund (FDCT) of Macau S.A.R with Grant No. 016/2012/A1.

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