vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff03ad1a000000527c000001000600 This study aims to provide an accurate and realistic prediction model for electricity demand using population, imports, exports, per capita Gross Domestic Product (GDP) and per capita Gross National Income (GNI) data for India. Four different models were used for different combinations of the above five input variables and the effect of input variables on the estimation of electricity demand has been demonstrated. In order to train the network 29 years data and to test the network 9 years data have been used. The future electricity demand for a period of 8 years from 2013 to 2020 has been predicted. The performance of the ANFIS technique is proved to be better than Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN).
S. Saravanan1, S. Kannan2, C. Thangaraj3
Kalasalingam University, India1, Ramco Institute of Technology, India2, Vignan University, India3
ANFIS, ANN, Exports, GDP, GNI, Imports, Load Forecasting, MLR
| January | February | March | April | May | June | July | August | September | October | November | December |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 5 , Issue: 3 , Pages: 985-990 )
Date of Publication :
April 2015
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213
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