Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/12126
Title: Short Term Load Forecasting using Functional Link Network
Authors: Muthukumaran, Sithambaram
Thyagarajah, K.
Paulraj, Murugesa Pandiyan, Prof. Madya Dr.
muthu2kumaran@gmail.com
paul@unimap.edu.my
Keywords: Load forecasting
Back propagation
Functional Link Network
Slope parameter
Issue Date: Apr-2011
Publisher: EuroJournals Publishing
Citation: European Journal of Scientific Research , vol.51(3), 2011 , pages 315-320
Abstract: Short Term Load Forecasting (STLF) is an important tool for successful planning and operation of power generating stations. This paper proposes neural network algorithm for STLF using Functional Link Neural Network (FLN) with Slope Parameter. This Neural Network Algorithm includes peak and minimum loads and load factors as additional inputs for the final forecast. The Proposed Functional Link Network with Slope Parameter (FLNSP) is tested for the Tamilnadu state, (India) grid data and the results are compared with the Conventional Back Propagation (CBP) method. Simulation results indicate that the proposed forecasting techniques are effective.
Description: Link to publisher's homepage at http://www.eurojournals.com/
URI: http://www.eurojournals.com/EJSR_51_3_03.pdf
http://dspace.unimap.edu.my/123456789/12126
ISSN: 1450-216X
Appears in Collections:School of Mechatronic Engineering (Articles)
Paulraj Murugesa Pandiyan, Assoc. Prof. Dr.

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