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Domestic electricity usage estimation model using socio-economic factors

Authors:

Y. S. S. Ariyarathne,

University of Kelaniya, LK
About Y. S. S.
Department of Physics and Electronics
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N. W. K. Jayatissa ,

University of Kelaniya, LK
About N. W. K.
Department of Physics and Electronics
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D. S. M. De Silva

University of Kelaniya, LK
About D. S. M. De
Department of Chemistry
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Abstract

In this empirical study, socioeconomic factors that can easily be extracted from families have been used to build a "home electricity usage prediction" model based on two variables, family monthly income and family size. Each of these factors was evaluated individually. Two machine learning models were built using those factors as features. Models are based on “Linear regression” and “Random Forest” algorithms. This study revealed that the socioeconomic factors such as family size and family income are very effective in domestic electricity usage prediction model building, where the end usages are not known. Furthermore, the random forest algorithm was found to be more effective for unseen data than the linear regression algorithm. The accuracy of the models can be further improved by adding more data into the both models.
How to Cite: Ariyarathne, Y.S.S., Jayatissa, N.W.K. and Silva, D.S.M.D., 2021. Domestic electricity usage estimation model using socio-economic factors. Journal of Science of the University of Kelaniya Sri Lanka, 14, pp.17–30. DOI: http://doi.org/10.4038/josuk.v14i0.8031
Published on 18 Jun 2021.
Peer Reviewed

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