gist-ailab/transfer-learning-pv-forecasting

This repository implements photovoltaic power forecasting across sites, with a documented source-to-target transfer-learning workflow: refine large-scale source PV data, train a forecasting model, then adapt it to local sites and evaluate predictions. The main runtime entry point is run_longExp.py; the experiment runner coordinates dataset loading, model training/testing, and metrics. The README also documents preprocessing and multiple regional datasets. Some subsystems are represented by their exact enclosing directories because their source was not sampled; edges reflect documented workflow or visible imports/calls, not assumed internal wiring.