Intelligent Spectral Irradiance Forecasting in IoT-Driven Smart Solar Grids: A Hybrid Metaheuristic–Neural Architecture with Fourier- Transformed Matrix Embeddings for Energy Prediction (Comprehensive Journal of Science, 2025)
Accurate forecasting of solar spectral irradiance remains a critical challenge in the operational stability and energydispatch planning of smart solar grids. Conventional machine learning models often fail to capture the high-frequencyspectral dynamics and non-stationary behavior inherent in solar irradiance data, especially under variable atmosphericconditions. To address this limitation, we propose a novel hybrid metaheuristic–neural architecture that integrates deeplearning with bio-inspired optimization and spectral signal decomposition. The core innovation lies in the formulation ofFourier-transformed matrix embeddings (FTMEs), which encode time-series irradiance measurements into structuredspectral–temporal representations. These embeddings serve as input to a deep recurrent neural network (RNN) whosehyperparameters are dynamically tuned via an enhanced grey wolf optimizer (GWO). Deployed within an Internet ofThings (IoT)-enabled monitoring framework, the proposed system enables real-time, high-resolution irradianceforecasting across multiple spectral bands. Experimental validation using ground-based spectral irradiance datasets fromthe 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%. Thearchitecture further exhibits robust generalization across diverse climatic zones, supporting its deployment in nextgenerationsmart 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.
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