Data Assimilation of Rooftop Solar Power Data to Improve Satellite Derived Irradiance Nowcasts

Estimates of irradiance are essential to many stages of solar system deployment and operation including siting and design, power generation forecasting, and estimates of behind the meter, distributed generation. Nowcasts produced from satellite images provide broad spatial coverage but lack the accuracy of point sensors on the ground. We present a method that improves irradiance nowcasts produced from the GOES-W satellite using data from a handful of rooftop solar systems in a data assimilation framework [1].

We use optimal interpolation (OI) to incorporate the more accurate, but spatially sparse, data from rooftop systems into the less accurate satellite derived irradiance estimates with broad spatial coverage. We discuss our implementation of OI, including recommendations for correlation methods and parameter optimization. Analysis of three months of satellite images over Tucson, AZ, shows a reduction in root-mean squared error by 50% along with a nearly eliminated mean bias error. We also discuss improvements in the method since the publication of [1], such as a new method to asses the quality of the parallax correction. We also discuss and present preliminary results of extending the coverage from the city-scale of Tucson to the scale of Southern and Central Arizona.

[1] A. T. Lorenzo, M. Morzfeld, W. F. Holmgren, and A. D. Cronin, “Optimal interpolation of satellite and ground data for irradiance nowcasting at city scales,” Sol. Energy, vol. 144, pp. 466–474, 2017.