Solar forecasting is essential for efficiently managing the energy generated by solar farms. One method involves using special cameras called all-sky imagers (ASIs) to predict sudden changes in sunlight, known as ramp events, which can affect energy production.
Researchers tested five types of ASIs as part of an international energy study. The results showed that some ASIs were better at identifying real ramp events, with success rates ranging from 26% to 92%. However, some ASIs also falsely detected ramp events—ranging from fewer than 10% of cases to as high as 85%. Additionally, the more accurate ASIs in spotting real events sometimes struggled with missing events entirely.
It’s important to note that certain ASIs were only tested on small areas, which may have limited their performance. Despite these challenges, the study suggests ASIs are a valuable tool for improving solar energy predictions. Combining traditional forecasting methods with advanced techniques like deep learning could make these predictions even more reliable in the future.
