Multi-modal Generative Modeling of Event Sequences and Time Series for Solar PV Systems

This paper presents a multimodal learning model designed to simulate a solar energy plant system for predictive maintenance and fault prediction. We propose a novel approach to enhance system generation through conditional generation by leveraging both event sequences and time-series data. We employ a Transformer Hawkes process to encode event data and an iTransformer to encode time-series data. We then introduce a co-attention mechanism to effectively combine these two modalities, capturing dependencies and interactions between events and time-series signals. The integrated representations enable a conditional generation framework that iteratively predicts future system states and events over an extended time horizon. The simulation is based on a comprehensive collection of data from the Red Rock Solar Site in Arizona. Experimental evaluations demonstrate that our approach offers promising applications in predictive maintenance, fault detection, and energy optimization in solar power systems.