Adaptive Hybrid GWO–SSA Optimized Deep Learning Framework for Accurate Power Forecasting of Next-Generation Perovskite Photovoltaic Systems Under Desert Climate Conditions
APPLIED SCIENCES, cilt.16, sa.18, ss.1-14, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 18
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/app16189371
- Dergi Adı: APPLIED SCIENCES
- Derginin Tarandığı İndeksler: Applied Science & Technology Source, Scopus, Science Citation Index Expanded (SCI-EXPANDED), Compendex, INSPEC, Directory of Open Access Journals
- Sayfa Sayıları: ss.1-14
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet
Özet
Accurate photovoltaic (PV) power forecasting is essential for enhancing grid stability, optimizing energy management, and facilitating the large-scale integration of renewable energy resources. Although deep learning techniques have demonstrated promising results in PV forecasting, their predictive performance is highly dependent on effective hyperparameter optimization. Furthermore, studies dedicated to field-deployed perovskite photovoltaic systems operating under semi-arid desert climatic conditions remain limited. To address this gap, this study proposes an Adaptive Hybrid Grey Wolf Optimizer–Sparrow Search Algorithm (AH-GWOSSA) deep learning framework for single-step-ahead (5 min lead time) and multi-step power forecasting of a perovskite PV system installed in Mosul, Iraq. A real-world dataset comprising 21,456 valid daytime observations (filtered for solar irradiance > 5.0 W/m2) was collected between November 2025 and April 2026 and partitioned strictly chronologically (70% training, 10% validation, 20% held-out testing). The proposed framework was benchmarked against Persistence, an unoptimized Base LSTM, Standard GRU, CNN-LSTM, standalone GWO, SSA, Random Search, and an ablation Fixed-Weight Hybrid under an equivalent evaluation budget of 160 candidate network trainings. Over 30 independent optimization runs, AH-GWOSSA achieved the lowest mean RMSE of 15.55 W (std. 0.34 W), an MAE of 8.79 W (std. 0.25 W), and the highest mean R2 of 0.885 (std. 0.008). Non-parametric Wilcoxon signed-rank testing across 30 paired run-wise RMSE values confirmed a statistically significant improvement over the unoptimized Base LSTM (, at ). Multi-horizon evaluations (5, 15, 30, 60, and 120 min) demonstrated consistent superiority over Persistence and Base LSTM, with RMSE improvements of up to 39.8% at the 30 min horizon. The findings demonstrate that adaptive hybrid metaheuristic optimization provides a competitive framework for ultra-short-term perovskite PV forecasting, with RMSE improvements of up to 39.8% over Persistence at the 30 min horizon, offering a solid foundation for future smart-grid and battery storage management applications.