NDLI: Integrating evolving fuzzy neural networks and tabu search for short term load forecasting

Content Provider	IEEE Xplore Digital Library
Author	Gwo-Ching Liao Ta-Peng Tsao
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan (Gwo-Ching Liao; Ta-Peng Tsao)
Abstract	An integrated evolving fuzzy neural network and tabu search (IEFNN-TS) for short term load forecasting method is presented in this paper. In this paper, short-term load forecasting is presented first using fuzzy hyperrectangular composite neural networks (FHRCNNs). Then, we use evolutionary programming (EP) and tabu search (TS) to find the optimal solution of the parameters of the FHRCNNs (that parameters include such as synaptic weights (w/sub jk/), biases (/spl theta//sub jk/), membership function (m/sub j/(_/sub t/)), sensitivity factor in membership function (s/sub j/) and adjustable synaptic weight (M/sub ij/ and m/sub ij/). We know that the EP has a good capability at search globe optimal value, but has poor capability at search local optimal. But the TS has good capability at local optimal search. So, here, we combine this two methods advantages to improve the shortcoming of the tradition ANN training that the weights and biases always trapped into a local optimal. Finally, we use this (IEFNN-TS) to improve the solution quality. Actually, we can reduce the error of load forecasting. The proposed IEFNN-TS load forecasting scheme was test using data obtained from a sample study include one year, one month and 24 hours. The result demonstrated the accuracy of the proposed load forecasting scheme.
Starting Page	755
Ending Page	762
File Size	441420
Page Count	8
File Format	PDF
ISBN	0780381106
DOI	10.1109/TDC.2003.1335370
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Load forecasting Artificial neural networks Neural networks Convergence Artificial intelligence Multilayer perceptrons Costs Input variables Fuzzy sets
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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