Environmental Sensor-Less Hybrid Analytical-Machine Learning (ESHAML) framework for ultra-fast solar irradiance estimation in climate-sensitive real-time applications: experimental validation

Accurate real-time solar irradiance (SI) estimation is pivotal for optimizing climate-sensitive system, yet existing methods often rely on costly environmental sensors or complex measurements, limiting their applicability in resource-constrained regions. This study introduces the Environmental Sensor-less Hybrid Analytical-Machine Learning (ESHAML) Framework, a novel solution for ultra-fast SI estimation tailored for climate-sensitive applications, such Renewable Energy Systems. ESHAML synergizes an analytical Photovoltaic (PV) model, derived from electrical characteristics, with a Machine Learning (ML) based- Bayesian regularization-trained neural network for temperature approximation, eliminating environmental sensor dependencies. A fractional-area Maximum Power Point Tracking (MPPT) algorithm achieves rapid convergence (under 20 ms) and stable PV operation, outperforming metaheuristic methods like Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Improved Differential Evolution (IDE), and Salp Swarm Optimization (SSO) in speed and stability. The framework leverages a novel synthetic data generation model with adaptive resolution for low-irradiance regimes, enhancing ML robustness in data-scarce environments. Validated through simulations and experimental studies using the IoT-based Trio-PV Monitor in Cameroon's solar-rich Dschang region, ESHAML achieves a Mean Absolute Percentage Error (MAPE) of 2.67-4.94 %, surpassing sensor-dependent approaches (e.g., Carrasco et al.'s analytical methods) and measurement-intensive neural networks (e.g., Chouay & Quassaid's dual-variable models). Comparative analyses highlight ESHAML's superior attributes: zero environmental sensors, seamless integration into existing PV infrastructures, and millisecond-level irradiance estimation. By addressing sensor reliance, data scarcity, and integration complexity, ESHAML emerges as a transformative tool for advancing solar energy utilization in Sub-Saharan Africa and beyond, aligning with global sustainable energy objectives.