Solar Resource Assessment & Forecasting Laboratory

Solar developers are scrambling to meet Puerto Rico utility company PREPA’s new ramp-rate requirement. Any new utility-scale power plant operator must commit to limit changes in output (“ramps”) to 10% per minute — a tall order for PV, as a single PV panel could fluctuate over 70% per second.
What if solar developers could predict how passing clouds affect fluctuations in power output — and plan their plants accordingly?

Compute the monthly and annual solar energy impinging upon a 1 m2 tilted plane in San Diego. Support for additional regions will be available soon. Features a Google Maps interface and realtime computations. Computations for your customized tilting angles are required, and we apologize for the wait. Try it out here: Solar Energy Calculator

A Google Earth map of shadows generated from sky imagery. The cloud and shadow layers are superimposed on Google Earth along with weather station and PV output data. Combined with cloud motion tracking, this information will next be used to make solar irradiance forecasts in the sky imager coverage area. Shadow Mapping Movie 1 Shadow Mapping Movie 2 by Bryan Urquhart

Google Earth KMZ download... Using the Google Earth file, one can easily determine the optimum tilt and azimuth angles for any site in California, as well as the average annual increase in radiation at the optimum tilt and azimuth versus horizontally flat. These maps were created using the SUNY 10km Gridded Dataset and an algorithm described by J.

Google Earth KMZ download...
This color map illustrates the Mean Global Horizontal Solar Energy Density across the state of California, USA during a typical meteorological year (TMY). This is the energy that a horizontally oriented solar panel would receive in one year. The data for this map comes from the corrected National Solar Radiation Database, SUNY 10km Gridded Dataset.
Reference: Nottrott and Kleissl, 2010