  Distributed, small-scale solar photovoltaic (PV) systems are being installed
at a rapidly increasing rate. This can cause major impacts on distribution
networks and energy markets. As a result, there is a significant need for
improved forecasting of the power generation of these systems at different time
resolutions and horizons. However, the performance of forecasting models
depends on the resolution and horizon. Forecast combinations (ensembles), that
combine the forecasts of multiple models into a single forecast may be robust
in such cases. Therefore, in this paper, we provide comparisons and insights
into the performance of five state-of-the-art forecast models and existing
forecast combinations at multiple resolutions and horizons. We propose a
forecast combination approach based on particle swarm optimization (PSO) that
will enable a forecaster to produce accurate forecasts for the task at hand by
weighting the forecasts produced by individual models. Furthermore, we compare
the performance of the proposed combination approach with existing forecast
combination approaches. A comprehensive evaluation is conducted using a
real-world residential PV power data set measured at 25 houses located in three
locations in the United States. The results across four different resolutions
and four different horizons show that the PSO-based forecast combination
approach outperforms the use of any individual forecast model and other
forecast combination counterparts, with an average Mean Absolute Scaled Error
reduction by 3.81% compared to the best performing individual model. Our
approach enables a solar forecaster to produce accurate forecasts for their
application regardless of the forecast resolution or horizon.
