Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In International journal of energy research

Accurately quantifying the social distancing (SD) practice of a population is essential for governments and health agencies to better plan and adapt restrictions during a pandemic crisis. In such a scenario, the reduction of social mobility also has a significant impact on electricity consumption, since people are encouraged to stay at home and many commercial and industrial activities are reduced or even halted. This paper proposes a methodology to qualify the SD of a medium-sized city, located in the northwest of the state of Rio Grande do Sul (RS), Brazil, using data of electricity consumption measured by the municipality's energy utility. The methodology consists of combining a data set, and an average consumption profile of Sundays is obtained using data from 4-months, it is then defined as a high SD profile due to the typical lower social activities on Sundays. An supervised and an unsupervised artificial neural network (ANN) are trained with this profile and used to analyze electricity consumption of this city during the COVID-19 pandemic. Low, moderate, and high SD ranges are also created, and the daily population behavior is evaluated by the ANNs. The results are strongly correlated and discussed with government restrictions imposed during the analyzed period and indicate that the ANNs can correctly classify the intensity of SD practiced by people. The unsupervised ANN is used more easily and in different scenarios, so it can be indicated for use by public administration for purposes of assess the effectiveness of SD policies based on the guidelines established during the COVID-19 pandemic.

Sausen Airam T Z R, de Campos Maurício, Sausen Paulo S, Binelo Manuel O, Binelo Marcia F B, da Silva João M L V, Dos Santos Moises


COVID‐19, artificial neural network, energy demand, social distancing