Spatiotemporal Modeling

ConvLSTM Model

Developed a ConvLSTM-based model to learn spatiotemporal relationships between successive satellite images and regional photovoltaic production.

Satellite image sequences were combined with solar-position information to estimate clear-sky-normalized PV production.

Training Pipeline

Implemented the PyTorch training and validation pipeline for large image and time-series datasets, covering data preparation, model training, checkpointing, and application to unseen periods.

Evaluation

Model outputs were converted back to the PV production scale and compared with measured production and a persistence baseline using standard forecasting metrics.