Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Prediction of India's Electricity Demand Using Anfis


Affiliations
1 Department of Electrical and Electronics Engineering, Kalasalingam University, India
2 Department of Electrical and Electronics Engineering, Ramco Institute of Technology, India
3 Vignan University, India
     

   Subscribe/Renew Journal


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).

Keywords

ANFIS, ANN, Exports, GDP, GNI, Imports, Load Forecasting, MLR.
Subscription Login to verify subscription
User
Notifications
Font Size

Abstract Views: 243

PDF Views: 0




  • Prediction of India's Electricity Demand Using Anfis

Abstract Views: 243  |  PDF Views: 0

Authors

S. Saravanan
Department of Electrical and Electronics Engineering, Kalasalingam University, India
S. Kannan
Department of Electrical and Electronics Engineering, Ramco Institute of Technology, India
C. Thangaraj
Vignan University, India

Abstract


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).

Keywords


ANFIS, ANN, Exports, GDP, GNI, Imports, Load Forecasting, MLR.