The global outbreak of the coronavirus disease 2019 (COVID-19) compelled various countries to implement societal lockdown to avoid further contagion. The mandatory lockdown forced numerous industries to adapt to a new operational norm, including the work from home (WFH) setup. As society embraces the WFH scheme, the energy consumption shifts from the industrial to the residential sector. This enabled a lesser and energy requirement from the transportation sector, thus, leading to a significant decrease in carbon dioxide emissions. However, the burden of shouldering the energy cost brought by the WFH scenario is now shifted to the employees rather than the employers. Hence, the impact of the rise of energy consumption in the residential sector needs to be quantified for decision-makers to develop appropriate policies to protect the employees from the surge of residential energy cost. The study presents the impact of implementing a lockdown on the rise of residential energy consumption using a machine learning energy consumption model.
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