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.

翻译：模拟和预测太阳事件,特别是太阳斜坡事件,对于提高太阳能发电系统的状况意识至关重要。太阳斜坡事件受到温度、湿度和云密度等天气条件的重大影响。发现不同地点和时间之间的相互关系是一项极具挑战性的任务,因为这个系统既复杂又吵闹。我们提出了一个新颖的方法,用以根据时空空间互动伯努利进程,从空间时序太阳辐射数据中模拟和预测斜坡事件。我们展示了我们在实际太阳辐射数据集方面的做法的良好表现。