A HYBRID MACHINE-LEARNING ENSEMBLE FOR SHORT-TERM FORECASTING OF PHOTOVOLTAIC, SOLAR-THERMAL, AND PV/T GENERATION

• Datum: 2026
• 1. Verfasser: Karpenko, Dmytro
• Format: Artikel
• Sprache: Englisch
• Veröffentlicht: General Energy Institute of the National Academy of Sciences of Ukraine 2026
• Schlagworte: short-term solar forecasting; photovoltaic/thermal (PV/T) generation; solar-thermal collector; multi-scale LSTM-CNN; XGBoost ensemble; per-horizon weight tuning; adaptive online retraining.
• Online Zugang: https://systemre.org/index.php/journal/article/view/959
• Назва журналу: System Research in Energy

Short-term forecasting of solar generation is a prerequisite for the operational integration of distributed solar plants into modern power systems, yet single-family approaches struggle with the asymmetric “bell” distribution of daily output, the limited observability of panel-thermal states, and th...