Intra-hour cloud movement detection for solar forecasts based on ground imaging system

The penetration of solar energy continues to rise and becomes a central piece of the global energy mix. Thus, considering ways for more efficiently operated power systems to accommodate significant amounts of such a variable resource will be increasingly important. Improvements in solar forecasting methods and techniques will clearly be relevant. In addition to season and irradiation angle, the most important factor of influencing solar energy output is the effect of cloud movement on solar irradiation shadow on solar plate. This paper briefly analyzes the advantages and disadvantages of various moving target algorithms, and compares the typical feature matching algorithm (block motion estimation algorithm) and optical flow algorithm (CLG algorithm) against the collected cloud movement image sequence. The result shows that optical flow algorithm (CLG algorithm) is applied to cloud movement image. The calculation is very fast, with an accuracy above 96%. A comparison with CLG algorithm shows that direction and speed accuracy of block motion estimation algorithm based on hexagonal search pattern is 0.79 and 0.47, respectively.