Short term load forecasting using artificial neural networks

Type :

Term papers

Pages :

6 pages

Format :

.pdf

Published date :

06/08/2009

Category :

Science & technology

Sub category :

Medical studies

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Summary :

Introduction
Meaning of load forecasting
Importance of STLF
Review of STLF measures
Neutral network and its applications
Program development of STLF
Conclusion and future scope
Bibliography

Abstract

Today's electric power industry is undergoing many fundamental changes due to the process of Deregulation. In the new market environment, the power system operation will become more competitive. Therefore the Utilities are required to perform optimal planning in order to operate their system efficiently. Therefore the accuracy of future load forecast becomes crucial. The accuracy of the short-term load forecasts has a significant impact on an electric utility's operations and production costs. Many conventional statistical methods such as multiple linear regression, time series, general exponential smoothing etc. have been used for forecasting short-term load. Usually, these techniques are effective for the forecasting of short-term load on normal days but fail to yield good results on those days with special events. Further, because of their complexities, enormous computational efforts are required to produce acceptable results. A short-term load-forecasting (STLF) program that uses an integrated artificial neural Network (ANN) approach is capable of predicting load for basic generation scheduling functions, assessing power system security, and providing timely dispatcher information. This PAPER PRESENTS the development of an artificial neural Network-based short-term load forecasting (STLF). Keywords: artificial neural Network (ANN), short term load forecasting (STLF), Energy forecasting