Privacy‐Preserving Smart Grid Stability Prediction Using Homomorphic Encryption Enabled Random Vector Function Link Networks

The integration of renewable energy sources and smart technologies has enhanced the efficiency of electrical grids but introduced new cybersecurity vulnerabilities. This study presents a novel privacy‐preserving machine learning framework that combines Random Vector Functional Link (RVFL) networks with Fully Homomorphic Encryption (FHE) for smart grid stability prediction. The framework enables end‐to‐end encrypted training and inference, ensuring data confidentiality throughout the ML pipeline. Experimental results using an enhanced smart grid dataset demonstrate that the standard RVFL model achieves 96.49% accuracy and an AUC of 1.0, while the encrypted RVFL(FHE) model attains 92.20% accuracy and an AUC of 0.92. The encryption introduces approximately 4.5× higher training time and increased memory consumption but offers significant privacy protection. This trade‐off is essential in scenarios where data sensitivity outweighs minor performance drops. The proposed method is applicable to real‐world smart grid systems, especially where privacy is critical, such as consumer load forecasting or demand response programs. Future work includes optimizing computational efficiency for edge deployment and validating the model in large‐scale, distributed smart grid environments.