(untitled) (Comprehensive Journal of Science, 2025)
Accurate forecasting of solar spectral irradiance remains a critical challenge in the operational stability and energy dispatch planning of smart solar grids. Conventional machine learning models often fail to capture the high-frequency spectral dynamics and non-stationary behavior inherent in solar irradiance data, especially under variable atmospheric conditions. To address this limitation, we propose a novel hybrid metaheuristic–neural architecture that integrates deep learning with bio-inspired optimization and spectral signal decomposition. The core innovation lies in the formulation of Fourier-transformed matrix embeddings (FTMEs), which encode time-series irradiance measurements into structured spectral–temporal representations. These embeddings serve as input to a deep recurrent neural network (RNN) whose hyperparameters are dynamically tuned via an enhanced grey wolf optimizer (GWO). Deployed within an Internet of Things (IoT)-enabled monitoring framework, the proposed system enables real-time, high-resolution irradiance forecasting across multiple spectral bands. Experimental validation using ground-based spectral irradiance datasets from the National Renewable Energy Laboratory (NREL) demonstrates a mean absolute percentage error (MAPE) of 2.13% and a normalized root mean square error (nRMSE) of 0.018 outperforming state-of-the-art benchmarks by 12–19%. The architecture further exhibits robust generalization across diverse climatic zones, supporting its deployment in nextgeneration smart solar grids for efficient energy prediction and grid integration. Keywords: Hybrid metaheuristic–neural algorithms, solar spectral irradiance, IoT-enabled smart grids, deep learning, Fourier series, matrix transformation, energy forecasting, grey wolf optimizer, recurrent neural networks.