Artificial intelligence for smart solar energy monitoring: Genetic attention-based hybrid deep–handcrafted fusion for faulty solar panel image classification

With global photovoltaic (PV) capacity exceeding the terawatt scale, timely and accurate fault detection is pivotal to minimizing energy losses and accelerating the transition to net-zero energy systems. In this context, we introduce GA-HyFusionNet—a novel, interpretable, and lightweight hybrid learning framework for solar panel fault classification that combines the strengths of handcrafted and deep features with an optimization-driven fusion strategy. The proposed method begins by extracting low-frequency structural textures via a four-level Discrete Wavelet Transform (DWT), capturing multiscale spatial patterns from grayscale PV imagery. Concurrently, class-specific Bag-of-Visual-Words (BoVW) histograms are generated from Speeded-Up Robust Features (SURF) through modular k-means clustering, producing compact, mid-level descriptors. In parallel, high-level semantic embeddings are obtained from the avgₚool layer of a fine-tuned ResNet50 model. Each feature stream is dimensionally aligned using Principal Component Analysis (PCA), projecting all representations into a unified 300-dimensional latent space. A Genetic Algorithm (GA) then learns optimal attention weights by minimizing the five-fold cross-validation error of a Linear Discriminant Analysis (LDA) classifier, enabling a discriminatively guided and adaptive fusion process. Furthermore, by linking early and accurate detection of dust, cracks, and surface defects to timely cleaning or maintenance of PV modules, GA-HyFusionNet directly minimizes energy yield degradation and reduces downtime, thereby improving operational efficiency and energy sustainability. The framework achieves a peak accuracy of 94. 35 % with both LDA and Linear SVM, outperforming deep-only baselines while maintaining low model complexity. Importantly, it requires only 19–21 ms per image at inference and a compact deployment size of ∼87–107 MB, ensuring real-time feasibility on edge devices. By recovering up to 5 % of annual PV energy losses through early and reliable fault diagnosis, GA-HyFusionNet not only reduces the Levelized Cost of Electricity (LCOE) but also advances the practical realization of Sustainable Development Goal 7: Affordable and Clean Energy. Its interpretable architecture, energy-aware design, and real-time feasibility position it as a valuable contribution to next-generation solar diagnostics.