Renewable energy is clean — but it’s also inherently variable. Solar PV generation can change dramatically within minutes due to cloud cover and weather conditions, making accurate short-term forecasts essential for grid stability, energy trading, and smart-home optimisation.
Open Climate Fix builds open and high-impact forecasting tools to accelerate the transition to a low-carbon energy system. One of these projects is Open Quartz Solar Forecast: an open-source model that uses public PV generation data, site metadata, and numerical weather prediction variables to forecast solar power for any location.
In this talk, I’ll present a real case study from my Google Summer of Code project where I implemented and trained a Temporal Fusion Transformer for multi-horizon solar forecasting. I’ll cover the practical engineering challenges behind making transformer forecasting work in Python: building continuous training windows, aligning weather forecast steps with observations, separating static vs time-varying features, and stabilising training using PyTorch Forecasting and PyTorch Lightning.
Attendees will leave with reusable patterns for real-world time-series forecasting pipelines.