Akademik Veri Yönetim Sistemi
Applied Information Systems and Technologies in the Digital Society (AISTDS 2025 ), Kyyiv, Ukrayna, 01 Ekim 2025, cilt.4133, ss.190-200, (Tam Metin Bildiri)
Accurate forecasting of photovoltaic (PV) power generation is essential for optimizing the operation and stability of renewable-dominated smart grids. However, the stochastic nature of solar irradiance, temperature-dependent derating, and nonlinear PV conversion dynamics pose significant challenges to model reliability and generalization. This study presents a novel deep neural network architecture, DNNv4, designed for short-term PV power forecasting using high-resolution SCADA telemetry. The proposed model integrates GELU activation, Layer Normalization, and a skip-fusion mechanism that merges multiscale dense representations to enhance feature propagation and gradient stability. Optimization is conducted through the AdamW algorithm combined with a Cosine Decay Restarts learning-rate schedule and Huber loss to improve robustness against outliers. The model was t rained on a real-world dataset comprising 118,865 SCADA records with environmental and electrical features such as irradiance, temperature, wind speed, and DC/AC currents. Experimental results demonstrate superior performance with RMSE = 1.741 kW, MAE = 0.992 kW, MAPE = 1.12 %, sMAPE = 1.14 % and R² = 0.9996, significantly outperforming conventional and hybrid baselines. Beyond predictive accuracy, DNN-v4 preserves physical consistency between irradiance, temperature, and current, offering a computationally efficient and interpretable framework for real-time PV forecasting in smart-grid operations.