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Forecasting Sector-wise Electricity Consumption for India Using Various Regression Models


Affiliations
1 Department of Mechanical Engineering, Ramdeobaba College of Engineering and Management, Katol Road, Nagpur 440 013, India
2 CSIR-Central Institute of Mining and Fuel Research, Nagpur Research Centre (Fuel Sciences), 17/C, Telangkhedi Area, Civil Lines, Nagpur 440 001, India
 

Electricity is an important and one of the most dominant energy sources used in the world. It governs a major share in the Indian as well as world economy. Thus, forecasting its consumption can be useful in better planning of its future production and supply. In the present study, electricity consumption in seven different sectors, namely industry, domestic, agriculture, commercial, traction and railways, others along with total electricity consumed is forecasted using regression analysis. The study uses four regression modelling approaches to forecast electricity consumption by sectors in India. These are linear, logarithmic, power and exponential regression models. The accuracy of the models is tested using R2 (coefficient of determination) and MAPE (mean absolute percentage error) values. The model having the highest R2 and lowest MAPE value is selected for better accuracy results. The result/forecast is then compared with the available data published by the Central Electricity Authority, Government of India.

Keywords

Electricity Consumption, Energy Policy, Forecasting, Regression Analysis.
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  • Morales, A. and Acevedo, A.M., Forecasting future energy demand: electrical energy in Mexico as an example case. Energy Procedia, 2014, 57, 782–790.

  • Bianco, V., Manca, O. and Nardini, S., Linear regression models to forecast electricity consumption in Italy. Energy Sour., Part B, 2013, 8(1), 86–93.

  • Çunkas, M. and Taskiran, U., Turkey’s electricity consumption forecasting using genetic programming. Energy Sour., Part B, 2011, 6, 406–416.

  • Ghosh, S. and Das A., Short-run electricity demand forecasts in Maharashtra. Appl. Econ., 2002, 34(8), 1055–1059, doi:10.1080/00036840110064656.

  • Guerrero, V. M. and Berumen, E., Forecasting electricity consumption with extra-model information provided by consumers. J. Appl. Stat., 1998, 25(2), 283–299.

  • Panklib, K., Prakasvudhisarn, C. and Khummongkol, D., Electricity consumption forecasting in Thailand using an artificial neural network and multiple linear regression. Energy Sour., Part B, 2015, 10(4), 427–434; doi:10.1080/15567249.2011.559520.

  • Rao, S. R. and Ghosh, S., Forecasting monthly peak demand of electricity in India – a critique. Energy Policy, 2012, 45, 516–520.

  • Sigauke, C. and Chikobvu, D., Peak electricity demand forecasting using time series regression models: an application to South African data. J. Stat. Manage. Syst., 2016, 19(4), 567–586; doi:10.1080/ 09720510.2015.1086146.


Abstract Views: 390

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  • Forecasting Sector-wise Electricity Consumption for India Using Various Regression Models

Abstract Views: 390  |  PDF Views: 141

Authors

Renuka Rekhade
Department of Mechanical Engineering, Ramdeobaba College of Engineering and Management, Katol Road, Nagpur 440 013, India
D. K. Sakhare
CSIR-Central Institute of Mining and Fuel Research, Nagpur Research Centre (Fuel Sciences), 17/C, Telangkhedi Area, Civil Lines, Nagpur 440 001, India

Abstract


Electricity is an important and one of the most dominant energy sources used in the world. It governs a major share in the Indian as well as world economy. Thus, forecasting its consumption can be useful in better planning of its future production and supply. In the present study, electricity consumption in seven different sectors, namely industry, domestic, agriculture, commercial, traction and railways, others along with total electricity consumed is forecasted using regression analysis. The study uses four regression modelling approaches to forecast electricity consumption by sectors in India. These are linear, logarithmic, power and exponential regression models. The accuracy of the models is tested using R2 (coefficient of determination) and MAPE (mean absolute percentage error) values. The model having the highest R2 and lowest MAPE value is selected for better accuracy results. The result/forecast is then compared with the available data published by the Central Electricity Authority, Government of India.

Keywords


Electricity Consumption, Energy Policy, Forecasting, Regression Analysis.

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DOI: https://doi.org/10.18520/cs%2Fv121%2Fi3%2F365-371