The pandemic scenario caused by COVID-19 is an event with no precedent. Therefore, it<br>is a phenomenon that can be studied to observe how electricity loads have changed during the stayat-home order weeks. The data collection process was done through online surveys and using<br>publicly available data. This study is focusing on analyzing household energy units such as<br>appliances, HVAC, lighting systems. However, collecting this data is expensive and timeconsuming since dwellings would have to be studied individually. As a solution, previous studies<br>have shown success in characterizing residential electricity using surveys with stochastic models.<br>This characterized electricity consumption data allows the researchers to generate a predictive<br>model, make a regression and understand the data. In that way, the data collection process will not<br>be as costly as installing measuring instruments or smart meters. The input data will be the<br>behavioral characteristics of each participant; meanwhile, the output of the analysis will be the<br>estimated electricity consumption "kWh." After generating the "kWh" target, a sensitivity analysis<br>will be done to observe the electricity consumption through time and examine how people evolved<br>their load during and after the stay-at-home order.<br>This research can help understand the change in electricity consumption of people who<br>worked at home during the pandemic and generate energy indicators and costs such as home office<br>electricity cost kWh/year. In addition to utilities and energy, managers can benefit from having a<br>clear understanding of domestic consumers during emergency scenarios as pandemics. <br>