vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff07c80b000000608f010001000500 Power System planning starts with Electric load (demand) forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs) using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components) and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC) is more effective.
S. Saravanan1 , S. Kannan2, C. Thangaraj3
Kalasalingam University, India1 ,Kalasalingam University, India2 ,Anna University of Technology, Chennai3, India
Artificial Neural Networks, Electricity Load Forecasting, Regression Analysis, Principal Components
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 2 , Issue: 4 , Pages: 365-370 )
Date of Publication :
July 2012
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