UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 3 Issue 5
May-2016
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1905R90


Registration ID:
220173

Page Number

608-614

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Title

SHORT-TERM LOAD FORECASTING WITH SPECIFIC MODEL OF FLN USING ARTFICIAL NEURAL NETWORK

Abstract

Load-forecasting has been tried out using most traditional forecasting models and artificial intelligence techniques and has become one of the major research fields. Artificial Neural Networks have lately received much attention, and a great number of papers have reported successful experiments and practical tests with them. This paper presents a new approach for short-term load forecasting on Artificial Neural Network (ANN). The short-term load forecasting has been done with the help of tensor model of Functional-Link Network (FLN). An attempt has been made for forecasting short-term load pattern of one hour ahead. This paper also helps to concretize the knowledge how Functional-Link Network is applied for forecasting load. The results of the Functional-Link Network show an improved forecast capability

Key Words

Short-term Load Forecasting, Functional-Link Network (FLN), Artificial Neural Network (ANN).

Cite This Article

"SHORT-TERM LOAD FORECASTING WITH SPECIFIC MODEL OF FLN USING ARTFICIAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 5, page no.608-614, May-2016, Available :http://www.jetir.org/papers/JETIR1905R90.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"SHORT-TERM LOAD FORECASTING WITH SPECIFIC MODEL OF FLN USING ARTFICIAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 5, page no. pp608-614, May-2016, Available at : http://www.jetir.org/papers/JETIR1905R90.pdf

Publication Details

Published Paper ID: JETIR1905R90
Registration ID: 220173
Published In: Volume 3 | Issue 5 | Year May-2016
DOI (Digital Object Identifier):
Page No: 608-614
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162


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