Estimation of Electric Power Demand Using Socioeconomic Variables: A Case of Indian Electricity Sector
The production and industrial growth of any country needs accurate forecasting of energy demand. In this research, per capita electricity demand is forecast using the Genetic Algorithm. As input data, socio-economic variables such as population, Gross Domestic Product, exports, and imports were used. Linear, quadratic and exponential models are used to forecast capacity. Performance parameters, including the Root Mean Square Error, R2 and modified R2, are determined for the models. For three separate potential scenarios, electricity demand is projected for the period 2017-2035. A strong agreement with the actual data with a high correlation coefficient (R2 = 99.45 percent) and low Root Mean Square Error ( RMSE = 0.1455) is shown by the projected demand for electricity. This method can, therefore, be used in India as an alternative energy forecasting technique.
Dr. Sanjeet Singh
Decision Sciences Area, Indian Institute of Management Lucknow, Prabandh Nagar, IIM Road, Lucknow-226013, India.
K. R. Ramakrishnan
ZS Associates India Private Limited, Pune, India.
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