  Forecasting load at the feeder level has become increasingly challenging with
the penetration of behind-the-meter solar, as this self-generation (also called
total generation) is only visible to the utility as aggregated net-load. This
work proposes a methodology for creation of scenarios of solar penetration at
the feeder level for use by forecasters to test the robustness of their
algorithm to progressively higher penetrations of solar. The algorithm draws on
publicly available observations of weather \emph{condition} (e.g.,
rainy/cloudy/fair) for use as proxies to sky clearness. These observations are
used to mask and weight the interval deviations of similar native usage
profiles from which average interval usage is calculated and subsequently added
to interval net generation to reconstruct interval total generation. This
approach improves the estimate of annual energy generation by 23\%; where the
net generation signal currently only reflects 52\% of total annual generation,
now 75\% is captured via the proposed algorithm. This proposed methodology is
data driven and extensible to service territories which lack information on
irradiance measurements and geo-coordinates.
