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 the differing physical structure of pure photovoltaic (PV), solar-thermal (ST) collector and hybrid photovoltaic/thermal (PV/T) installations. This paper presents a unified machine-learning ensemble that forecasts all three modalities through one pipeline: (i) a multi-scale convolutional–recurrent network with parallel branches of kernel sizes 3, 12 and 24 hours aligned with the daily solar cycle; (ii) an XGBoost regressor on exogenous-only meteorological, calendar and air-quality features; (iii) a per-horizon weighted combination of the two heads tuned offline on out-of-fold windows under a monotone “GBDT-first” prior; (iv) an adaptive retraining cycle that, on a threshold trigger, fine-tunes the LSTM-CNN, incrementally extends the XGBoost booster with additional trees, and re-tunes the per-horizon weights on a sliding history window; and (v) an opt-in inference-time computer-vision contamination derate from an EfficientNet-B0 panel-state classifier. The same code drives the PV-electric and ST-thermal heads, each with its own scaler, weight schedule and adaptive trigger. Training and validation use 23 days of one-minute São Mateus PV and PV/T testbed data aggregated to hourly resolution and joined with reanalysis-derived weather and air-quality features. On a rolling-horizon 24-hour test the ensemble attains R² of 0.86 ± 0.07 for PV electric power on the PV/T testbed and 0.79 ± 0.11 for ST thermal power; a stress-test of the three-step retraining cycle that fires on every test window lands within one across-window standard deviation of the non-adaptive baseline, indicating that the cycle operates as designed without destabilising the ensemble.