AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (591.8 KB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Adaptive Electric Load Forecaster

Department of Electrical and Computer Engineering, University of Macau, Macao 999078, China.
Show Author Information

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.

References

[1]
Mendel, M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper-Saddle River, NJ, USA: Prentice-Hall, 2001.
[2]
Liang Q. L. and Mendel, J. M. Interval type-2 fuzzy logic system: Theory and design, IEEE Trans. on Fuzzy Systems, vol. 8, no. 5, pp. 535-550, 2000.
[3]
Mendel M. and John, R. I. Type-2 fuzzy sets made simple, IEEE Trans. on Fuzzy Systems, vol. 10, no. 2, pp. 117-127, 2002.
[4]
Mendel, M. John, R. I. and Liu, F. L. Interval type-2 fuzzy logic systems made simple, IEEE Trans. on Fuzzy Systems, vol. 14, no. 6, pp. 808-821, 2006.
[5]
Juang C. F. and Tsao, Y. W. A self-evolving interval type-2 fuzzy neural network with online structure and parameter learning, IEEE Trans. on Fuzzy Systems, vol. 16, no. 6, pp. 1411-1424, 2008.
[6]
Liu Z. Q. and Liu, Y. K. Type-2 fuzzy variables and their arithmetic, Soft Computing, vol. 14, pp. 729-747, 2010.
[7]
Liao G. C. and Tsao, T. P. Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting, IEEE Trans. Evolutionary Computing, vol. 10, no. 3, pp. 330-340, 2006.
[8]
Yun, Z. Quan, Z. Xin, S. C. Lan, L. S. Ming, L. Y. and Yang, S. RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment, IEEE Trans. Power Syst., vol. 23, no. 3, pp. 853-858, 2008.
[9]
Hanmandlu M. and Chauhan, B. K. Load forecasting using hybrid models, IEEE Trans. Power Syst., vol. 26, no. 1, pp. 20-29, 2011.
[10]
Xu, L. Bayesian Ying-Yang machine, clustering and number of clusters, Pattern Recognition Letters, vol. 18, pp. 1167-1178, 1997.
[11]
Xu, L. RBF nets, mixture experts, and Bayesian Ying-Yang learning, Neurocomputing, vol. 1, pp. 223-257, 1998.
[12]
Xu, L. Best harmony, unified RPCL and automated model selection for unsupervised and supervised learning on Gaussian mixtures, three-layer nets and ME-RBF-SVM models, International Journal of Neural Systems, vol. 11, no. 1, pp. 43-69, 2001.
[13]
Xu, L. BYY harmony learning, independent state space, and generalized APT financial analysis, IEEE Trans. on Neural Networks, vol. 12, no. 4, pp. 822-849, 2001.
[14]
Xu, L. BYY harmony learning, structural RPCL, and topological self-organizing on mixture models, Neural Networks, vol. 15, pp. 1125-1151, 2002.
[15]
Xu, L. Ying-Yang LearningThe Handbook of Brain Theory and Neural Networks, 2nd ed. The MIT Press, 2002, pp. 1231–1237.
[16]
Ma, J. W. Wang, T. J. and Xu, L. An annealing approach to BYY harmony learning on Gaussian mixture with automated model selection, presented in IEEE Int. Conf. Neural Networks & Signal Processing, Nanjing, China, 2003, pp. 23–28.
[17]
Ma J. W. and Liu, J. F. The BYY annealing learning algorithm for Gaussian mixture with automated model selection, Pattern Recognition, vol. 40, pp. 2029-2037, 2007.
[18]
Ma J. W. and He, X. F. A fast fixed-point BYY harmony learning algorithm on Gaussian mixture with automated model selection, Pattern Recognition Letters, vol. 29, pp. 701-711, 2008.
[19]
Takagi T. and Sugeno, M. Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst., Man, Cybern., vol. 15, no. 1, pp. 116-132, 1985.
[20]
Wang, L. Fuzzy systems are universal approximators, in Proc. 1st IEEE Conf. Fuzzy Syst., San Diego, CA, USA, 1992, pp. 1163-1169.
[21]
Jang, J. S. R. ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. Syst., Man, Cybern., vol. 23, pp. 665-684, 1993.
[22]
Juang C. F. and Lin, C. T. An on-line self-constructing neural fuzzy inference network and its applications, IEEE Trans. on Fuzzy Systems, vol. 6, no. 1, pp. 12-32, 1998.
[23]
Lughofer, E. D. FLEXFIS—A robust incremental learning approach for evolving Takagi-Sugeno fuzzy models, IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1393-1409, 2008.
[24]
Wang, D. Zeng, X. J. and Keane, J. A. An evolving-construction scheme for fuzzy systems, IEEE Trans. on Fuzzy Systems, vol. 18, no. 4, pp. 755-770, 2010.
[25]
Chiu, S. L. Fuzzy model identification based on cluster estimation, J. Intell. Fuzzy Syst., vol. 2, pp. 267-278, 1994.
[26]
Wang L. and Langari, R. Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques, IEEE Trans. Fuzzy Syst., vol. 3, no. 4, pp. 454-458, 1995.
[27]
Shi, Y. Mizumoto, M. and Shi, P. Fuzzy if-then rule generation based on neural network and clustering algorithm techniques, in Proceedings of IEEE Tencon’02, 2002.
Tsinghua Science and Technology
Pages 164-174
Cite this article:
Dong M, Lou C. Adaptive Electric Load Forecaster. Tsinghua Science and Technology, 2015, 20(2): 164-174. https://doi.org/10.1109/TST.2015.7085629

413

Views

10

Downloads

2

Crossref

N/A

Web of Science

1

Scopus

0

CSCD

Altmetrics

Received: 17 December 2012
Revised: 07 July 2014
Accepted: 15 November 2014
Published: 23 April 2015
© The author(s) 2015
Return