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

Accurate short-term forecasting of photovoltaic (PV) generation, long a concern for grid operators, is increasingly relevant to prosumers as well: the households and small businesses that both produce and consume electricity and must plan storage, self-consumption, and grid export. This paper studies solar forecasting under the constraint that users rarely possess years of historical generation data that data-hungry deep models assume. We release an open-source toolkit that integrates 7 forecasting models, spanning gradient-boosted trees, recurrent and convolutional sequence models, and attention- and basis-expansion architectures. We evaluate the toolkit’s performance on a newly released dataset of two grid-connected 50 kWp PV systems in southwestern Romania, using a strict rolling-origin walk-forward protocol. Across all experiments, XGBoost and LightGBM lead: on the primary site, they achieve 10.4% normalized RMSE against 12.9% for the best deep model, and they already beat a naive persistence baseline after a single month of training data. A dedicated small-window study shows that the boosting advantage is concentrated in the cold-start regime and narrows steadily as history accumulates, from a factor of 2.1 at one month to 1.08 at twelve, with no model saturating inside the available record. A cross-site replication on a second, independent installation reproduces the full model ranking almost exactly (Spearman ρ=0.964), strong evidence that the finding is not a single-dataset artifact. The resulting forecaster is deliberately lightweight and cost-efficient: a few hundred shallow trees over a seven-feature lag/rolling core that trains in under a second per month on a commodity CPU. We publicly release the toolkit and the anonymized two-site dataset to support reproducible prosumer-scale PV forecasting research.