"Blockchain-Enabled Intrusion Detection and Load Forecasting in Electrical Transmission Systems"

Abstract

The growing complexity of modern electrical transmission systems has made them increasingly vulnerable to cyber-attacks and inefficiencies arising from inaccurate load predictions. This paper explores an integrated framework that leverages blockchain technology for secure, tamper-resistant data logging and artificial intelligence (AI)-based techniques for real-time intrusion detection and load forecasting. A hybrid model combining Recurrent Neural Networks (RNNs) and Isolation Forests is proposed to identify anomalies within operational and cybersecurity parameters. Simultaneously, load forecasting is achieved through Long Short-Term Memory (LSTM) networks trained on historical and real-time energy consumption data. The adoption of blockchain ensures data integrity, transparency, and decentralized authentication, thereby reinforcing trust in multi-party communication systems within the power grid. Experimental simulations using synthetic and real-world datasets reveal high detection accuracy, improved load prediction, and minimal latency in blockchain data propagation. The results suggest that the fusion of blockchain and AI presents a resilient and intelligent infrastructure for securing and optimizing electrical transmission systems.