Solar forecasting provides valuable information for grid management. Satellite-based forecasting tools account for the short-term intra-day time horizons, typically outperforming numerical weather predictions up to 4–5 h ahead. The method consist of three separated stages, namely, cloud motion estimation, motion extrapolation and satellite-to-irradiation conversion. In this work we compare different satellite-based proposals for hourly irradiation forecast up to 5 h ahead using a 2-years data set. The widely-used Lorenz’s block matching technique and four optical flow (OF) algorithms are assessed, both at image and irradiation levels. All the methods are locally optimized to obtain their peak performance. It is found that the OF algorithm which combines an L 1 data penalty term on the optical flow equation with total variation regularization (TVL1) outperforms the rest. Different image extrapolation approaches and spatial smoothing are also tested. It is found that changing the extrapolation technique does not have much impact in the overall performance and that important gains can be obtained by optimally smoothing the predicted images previous to solar irradiation conversion. By doing this, all methods outperform the exigent convex persistence benchmark, achieving positive forecasting skills. The tests are performed using GOES-East satellite images of south-east South America, and the methods’ optimal parameters are given.