Solar Radiation Ramping Events Modeling Using Spatio-temporal Point Processes
This work addresses the challenge of predicting solar events for solar power generation systems, but it appears incremental as it builds on existing point process methods with domain-specific adaptations.
The paper tackled the problem of modeling solar radiation ramping events, which are critical for solar power systems, by proposing a novel spatio-temporal categorical point process model, demonstrating its interpretability and predictive power in real-data experiments.
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.