  This chapter proposes and provides an in-depth discussion of a scalable
solution for running ensemble simulation for solar energy production.
Generating a forecast ensemble is computationally expensive. But with the help
of Analog Ensemble, forecast ensembles can be generated with a single
deterministic run of a weather forecast model. Weather ensembles are then used
to simulate 11 10 KW photovoltaic solar power systems to study the simulation
uncertainty under a wide range of panel configuration and weather conditions.
This computational workflow has been deployed onto the NCAR supercomputer,
Cheyenne, with more than 7,000 cores.
  Results show that, spring and summer are typically associated with a larger
simulation uncertainty. Optimizing the panel configuration based on their
individual performance under changing weather conditions can improve the
simulation accuracy by more than 12%. This work also shows how panel
configuration can be optimized based on geographic locations.
