Forecasting of electricity consumption in assam by using mathematical and time series models and comparison of their predictive performances
Abstract
Although energy exists in different forms in nature, the most important form of energy is Electricity. Considering the worldwide increasing demand for electricity with increasing population, rapid urbanization, rising incomes, changes in lifestyles and with infrastructural and industrial growth, forecasting electricity consumption becomes an important part of power sector of any country. In this paper, attempt is made to forecast the total electricity consumption in Assam with the help of two models, considering the time period from 1990-91 to 2016-17. The first one is Multiple Linear Regression model which considers the effects of Population and Per Capita Income on the electricity consumption in Assam while the second one is the Autoregressive Integrated Moving Average (ARIMA) model. By comparing the Average Relative Error of the two proposed models, the predictive performance of the ARIMA (0, 1, 2) model is found better than the Multiple Linear Regression model and ARIMA shows an annual average increasing rate of electricity consumption as 5.40% up to 2021.
Copyright © 2021 Nibedita Mahanta, Ruma Talukdar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article
N. Mahanta, R. Talukdar, Forecasting of electricity consumption in assam by using mathematical and time series models and comparison of their predictive performances, J. Math. Comput. Sci. 11 (2021), 1687–1703. https://doi.org/10.28919/jmcs/5345
BibTeX
@article{Mahanta2021,
author = {Mahanta, Nibedita and Talukdar, Ruma},
title = {Forecasting of electricity consumption in assam by using mathematical and time series models and comparison of their predictive performances},
journal = {J. Math. Comput. Sci.},
volume = {11},
year = {2021},
pages = {1687--1703},
doi = {10.28919/jmcs/5345}
}