  Load forecasting is an integral part of power system operations and planning.
Due to the increasing penetration of rooftop PV, electric vehicles and demand
response applications, forecasting the load of individual and a small group of
households is becoming increasingly important. Forecasting the load accurately,
however, is considerable more difficult when only a few households are
included. A way to mitigate this challenge is to provide a set of scenarios
instead of one point forecast, so an operator or utility can consider a range
of behaviors. This paper proposes a novel scenario forecasting approach for
residential load using flow-based conditional generative models. Compared to
existing scenario forecasting methods, our approach can generate scenarios that
are not only able to infer possible future realizations of residential load
from the observed historical data but also realistic enough to cover a wide
range of behaviors. Particularly, the flow-based models utilize a flow of
reversible transformations to maximize the value of conditional density
function of future load given the past observations. In order to better capture
the complex temporal dependency of the forecasted future load on the condition,
we extend the structure design for the reversible transformations in flow-based
conditional generative models by strengthening the coupling between the output
and the conditional input in the transformations. The simulation results show
the flow-based designs outperform existing methods in scenario forecasting for
residential load by both providing more accurate and more diverse scenarios.
