Solar Forecasting: Small-Window Performance and Cross-Site Validation

Prosumers—i.e., households and small businesses that both produce and consume electricity—need short-term forecasts of their photovoltaic (PV) generation to plan storage, self-consumption and grid export, but rarely have the years of history that data-hungry deep models assume. We integrated seven forecasting models into our open-source toolkit, including gradient-boosted trees and recurrent, convolutional, attention-based, and basis-expansion networks; all models were evaluated on a newly released dataset from two grid-connected PV systems with 50 kW inverter ratings in southwestern Romania. Under a rolling-origin protocol in which every input, including day-ahead weather forecasts, is available when the forecast is issued, LightGBM and XGBoost reach 9.9–10.0% normalized RMSE one hour ahead, a skill of 7–9% over smart persistence, which they surpass in 11 of the 12 test months, whereas every deep model falls short of that reference, the best (a Transformer) reaching 14.2%. The boosted models perform better at all tested history lengths, from one month of data where the best reaches 12.6% against 16.9% for the best deep model, to 12 months where the best deep model still has 1.28 times the error of the best boosted one. The model ranking is identical at a second installation in the same municipality (Spearman $\rho =1.0$). In fixed-origin forecasts for the following days at the first site, day-ahead weather forecasts reduce the first-day error from 23.0% to 19.2%, and the benefit declines steadily with lead time, with no cutoff within the 7 days covered by the forecast archive.