  Modeling and predicting solar events, in particular, the solar ramping event
is critical for improving situational awareness for solar power generation
systems. Solar ramping events are significantly impacted by weather conditions
such as temperature, humidity, and cloud density. Discovering the correlation
between different locations and times is a highly challenging task since the
system is complex and noisy. We propose a novel method to model and predict
ramping events from spatial-temporal sequential solar radiation data based on a
spatio-temporal interactive Bernoulli process. We demonstrate the good
performance of our approach on real solar radiation datasets.
