ISSN: 2349-5162 | Impact Factor: 5.87

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Published in:

Volume 4 Issue 4
April-2017
eISSN: 2349-5162

Unique Identifier

JETIR1704045

Page Number

181-184

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Title

Short-term Load Forecasting Using Artificial Neural Network

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Abstract

In electrical engineering load forecasting have been tried out using traditional forecasting models and artificial neural network and have become one of the major research fields. An accurate and efficient Short-term load forecasting (STLF) plays a vital role for economic operational planning of both regulated power systems and electricity markets. To develop a solution/methodology to demand forecast (Hourly load forecast) and by incorporating weather conditions. However, STLF it is pertinent to understand conventional methods. Therefore, popular conventional methods were implemented to learn methods for STLF. This paper presents Simple Moving Average, Weighted Moving Average, Exponential Moving Average, for short term load forecasting. Conventional technique approach is implemented on historical load data for forecasting the load.and one AI technique use for ANN. PGVCL hourly load data used for training and testing is collected from ALDC, Jetpur, Gujarat. Hence hereby in this paper I have compared four conventional methods for STLF and have come out with a result that forecasting errors of time series models gives reasonably accurate hour ahead load forecast.

Key Words

Short-Term Load forecasting, traditional methods, matlab coding results

Cite This Article

"Short-term Load Forecasting Using Artificial Neural Network ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.4, Issue 4, page no. pp181-184, April-2017, Available at : www.jetir.org & http://www.jetir.org/JETIR1704045

Publication Details

Published Paper ID: JETIR1704045
Registration ID: 170200
Published In: Volume 4 | Issue 4 | Year April-2017
DOI (Digital Object Identifier):
Page No: 181-184
ISSN Number: 2349-5162

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(Volume 4 | Issue 10 | October 2017 |Impact factor 5.87)

Call For Paper | Volume 4 | Issue 10 | Impact factor 5.87


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