Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
Predicting solar power output before any real data exists
When a solar farm first opens, operators have no historical data to train forecasting models—but this research shows they can generate fake production histories from basic site information and weather patterns, then feed those into artificial intelligence models to make accurate predictions. On real data, this approach cut forecast error by 1.7 to 2 times compared to traditional methods, with one model achieving an error rate of just 0.514 kilowatt-hours per kilowatt of capacity per day.
Solar operators currently make blind decisions about maintenance, storage, and grid commitments at a plant's launch. Better cold-start forecasts let them optimize operations immediately rather than waiting months for real data to accumulate, reducing waste and improving grid reliability. The method works across different climates and plant types, making it practical for rapid deployment worldwide.