A very short term solar power forecasting technology which uses ground-based visible wavelength imagery is presented. A sky camera system suitable for use as a solar power forecasting tool is described. Relevant imaging considerations are discussed, including the need for high dynamic range imaging of the daytime sky and an associated stray light assessment. To photogrammetrically calibrate this sky camera system, a general camera model applicable to a fixed focal length photo objective lens with significant radially symmetric distortion is developed, and an accurate calibration technique for a stationary, skyward pointing daytime camera using the sun's position is given. Remote sensing algorithms used in the solar forecasting process are detailed, including clear sky characterization, cloud detection, cloud velocity estimation, and cloud height estimation using stereography. A cloud stereo photogrammetry method which provides dense 3D cloud position is presented. Correspondence is automatically determined using intra- scanline dynamic programming applied to a normalized cross correlation matching metric; an ordering constraint is implicit in the approach used. Using the described remote sensing tools and methods, a complete solar power forecasting framework is detailed. The method is based on the estimation of cloud shadow position via ray tracing, and the forecast position of the cloud shadows relative to solar collectors. A ray tracing procedure that works with a planar mapping of cloud position is used to compute shadow position. Cloud transmissivity is characterized using past observations and applied to forecast cloud positions. The application of the procedure to two case studies: the UCSD DEMROES weather station network, and a 48MW solar photovoltaic power plant is presented. A comparison of the forecasting performance using a common Total Sky Imager is compared to the UCSD Sky Imager, where it is shown that the UCSD Sky Imager performs better overall
