Ultra-short and short-term forecasting of photovoltaic solar energy using high temporal resolution data: Case study of Jaén (Spain)

Fernandez-Fabian K.M.; Pacheco-Salazar D.G.; Cano-Ortega A. · Energy Conversion and Management: X (2026) · DOI: 10.1016/j.ecmx.2026.101760

Resumen

Weather variations that affect photovoltaic (PV) generation increase the demand for high temporal resolution power predictions and very short-term forecasts (1 to 30 min) to ensure stability and efficient supply planning. There are several studies based on artificial intelligence (AI), but it is difficult to identify ultra-short- and short-term studies that address the performance evaluation of prediction architectures. Therefore, the strength of this study is its stress test-like design, which compares leading architectures (Transformers, CNN-GRU, Bio-inspired Optimization) under an analysis of accuracy and computational efficiency of PV energy predictions at different ultra-short and short-term time horizons against high-resolution weather variations (every 10 s), providing a solid foundation for future optimization, practical application, and scalability. For a sunny day, the forecasts reached R2 = 0.9986 and R2 = 0.9724 on 1-min and 24-h horizons, respectively. For these same time horizons, on a cloudy day, performance decreased by 3.29% and 18.90%, respectively; on a rainy day, the decrease was 7.9% for the 1-min time horizon and 44% for the 12-h time horizon. Likewise, prediction times relative to processing time on sunny days were reduced by 75.38%, 68.95%, 52.83%, and 48.52% for 1-min, 30-min, 3-h, and 12-h horizons, respectively; similar behaviors were observed in the remaining algorithms and day types.

Instituciones: Universidad Nacional de San Agustín de Arequipa; Universidad Nacional de Ingeniería