  Modeling and predicting solar events, particularly the solar ramping event,
is critical for improving situational awareness for solar power generation
systems. It has been acknowledged that weather conditions such as temperature,
humidity, and cloud density can significantly impact the emergence and position
of solar ramping events. As a result, modeling these events with complex
spatio-temporal correlations is highly challenging. To tackle the question, we
adopt a novel spatio-temporal categorical point process model, which
intuitively and effectively addresses correlation and interaction among ramping
events. We demonstrate the interpretability and predictive power of our model
on extensive real-data experiments.

Applications;Machine Learning