UC San Diego

Changing cloud cover is a major source of solar radiation variability and poses challenges for the integration of solar energy. A compact and economical system that measures cloud shadow motion vectors to estimate power plant ramp rates and provide short-term forecasting is presented. The Cloud shadow Speed Sensor (CSS) is constructed using an array of luminance sensors and a high -speed data acquisition system to resolve the progression of cloud passages across the sensor footprint. An embedded microcontroller acquires the sensor data and uses a cross- correlation algorithm to determine cloud shadow motion vectors. The CSS was validated against an artificial shading test apparatus, an alternative method of cloud motion detection from ground-measured irradiance (linear cloud edge, LCE), and a UC San Diego sky imager (USI). The CSS detected artificial shadow directions and speeds to within 15° and 6 % accuracy, respectively. The CSS detected (real) cloud shadow directions and speeds with average weighted root-mean-square difference of 22° and 1.9 m s⁻¹ when compared to USI and 33 and 1.5 m s⁻¹ when compared to LCE results