  The COVID-19 crisis is profoundly influencing the global economic framework
due to restrictive measures adopted by governments worldwide. Finding real-time
data to correctly quantify this impact is very significant but not as
straightforward. Nevertheless, an analysis of the power demand profiles
provides insight into the overall economic trends. To accurately assess the
change in energy consumption patterns, in this work we employ a multi-layer
feed-forward neural network that calculates an estimation of the aggregated
power demand in the north of Italy, (i.e, in one of the European areas that
were most affected by the pandemics) in the absence of the COVID-19 emergency.
After assessing the forecasting model reliability, we compare the estimation
with the ground truth data to quantify the variation in power consumption.
Moreover, we correlate this variation with the change in mobility behaviors
during the lockdown period by employing the Google mobility report data. From
this unexpected and unprecedented situation, we obtain some intuition regarding
the power system macro-structure and its relation with the overall people's
mobility.
