  The novel coronavirus (COVID-19) pandemic has posed unprecedented challenges
for the utilities and grid operators around the world. In this work, we focus
on the problem of load forecasting. With strict social distancing restrictions,
power consumption profiles around the world have shifted both in magnitude and
daily patterns. These changes have caused significant difficulties in
short-term load forecasting. Typically algorithms use weather, timing
information and previous consumption levels as input variables, yet they cannot
capture large and sudden changes in socioeconomic behavior during the pandemic.
In this paper, we introduce mobility as a measure of economic activities to
complement existing building blocks of forecasting algorithms. Mobility data
acts as good proxies for the population-level behaviors during the
implementation and subsequent easing of social distancing measures. The major
challenge with such dataset is that only limited mobility records are
associated with the recent pandemic. To overcome this small data problem, we
design a transfer learning scheme that enables knowledge transfer between
several different geographical regions. This architecture leverages the
diversity across these regions and the resulting aggregated model can boost the
algorithm performance in each region's day-ahead forecast. Through simulations
for regions in the US and Europe, we show our proposed algorithm can outperform
conventional forecasting methods by more than three-folds. In addition, we
demonstrate how the proposed model can be used to project how electricity
consumption would recover based on different mobility scenarios.
