Operational Real Time Intra-Day Solar Irradiance and Variability Forecasts based on Computer Vision of Processed Satellite Images Enhanced with Machine Learning Algorithms
This contribution seeks to present recent advances in this area based on multiple computer vision techniques such as Optical Flow, Particle Index Velocimetry and Difference Centroid Algorithm. These methods are applied to satellite imagery to test their capabilities in cloud movement detection and cloud tracking. The accuracy of these techniques is evaluated based on the performance of cloud movement and irradiance forecasts. To increase the skill of the forecasting engine, various other available inputs (e.g statistical cloud cover indices, prevalent local movement patterns and image features of cloud segmented images in proximity to the location of interest) are explored and optimized with different machine learning techniques. Generic Algorithms are applied to enable the efficient selection of input parameters and to reduce calculation costs.
Findings show that our approach is suitable to forecast the solar irradiance on multiple time horizons with significant optimization potential. Our progress and preliminary results are valuable for independent system operators, utility companies and system operators.