Comparison of Multi-Source Weather Data for Short-Term PV Forecasting

Abstract

Accurate photovoltaic (PV) forecasting depends on both model structure and the meteorological information available at different forecast horizons. This study evaluates hourly generation from a 7.5 kWp rooftop PV system at Brunel University of London using local sensor, weather station, and NASA POWER data. Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GB) models were compared across seven input configurations and 0, 1, 3, 6, and 24 h horizons, with robustness assessed over five random seeds. For positive forecast horizons, the models used only meteorological observations available at or before the forecast issue time; no future weather forecasts corresponding to the target hour were supplied. Local sensor measurements provided the strongest same-hour estimation, whereas hybrid multi-source inputs were generally more effective for future forecasting. Under the native model formulations, LSTM achieved the lowest mean Root Mean Square Error at 1 and 3 h, while RF performed best at 6 and 24 h. An information-equivalent sensitivity analysis supplied tree-based models with the same 24 h predictor history as LSTM. History-aware boosting then matched the 1 h LSTM performance and substantially narrowed the difference at 3 h, indicating that part of the apparent short-horizon LSTM advantage was associated with access to recent temporal information rather than model architecture alone. Overall, the results demonstrate that meteorological data sources, forecast horizons, model family, and historical information representation should be considered jointly when designing short-term PV forecasting systems.