Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors

• We report a new method to forecast variability in PV power generation due to clouds.
• Power measurements from 80 residential rooftop PV systems are used as an input.
• Forecasts outperform persistence model for horizons ranging from 15 to 90 min.
• We present a framework to model station-pair correlations of irradiance variability.
• We recommend optimal locations of irradiance sensors for forecasting.

We report a new method to forecast power output from photovoltaic (PV) systems under cloudy skies that uses measurements from ground-based irradiance sensors as an input. This work describes an implementation of this forecasting method in the Tucson, AZ region where we use 80 residential rooftop PV systems distributed over a 50 km × 50 km area as irradiance sensors. We report RMS and mean bias errors for a one year period of operation and compare our results to the persistence model as well as forecasts from other authors. We also present a general framework to model station-pair correlations of intermittency due to clouds that reproduces the observations in this work as well as those of other authors. Our framework is able to describe the RMS errors of velocimetry based forecasting methods over three orders of magnitude in the forecast horizon (from 30 s to 6 h). Finally, we use this framework to recommend optimal locations of irradiance sensors in future implementations of our forecasting method.