Secure and Resilient Demand Side Management Engine using Machine Learning for IoT-enabled Smart Grid

Abstract

The national security, economy, and healthcare heavily rely on the reliable distribution of electricity. The incorporation of communication technologies and sensors in the power structures, recognized as the smart grid which revolutionizes the model of the production, distribution, monitoring, and control of the electricity. To realize the applicability of smart grid, several issues need to be addressed. Securing the smart grid is a very challenging task and a pressing issue. In this article, a secure demand-side management (DSM) engine is proposed using machine learning (ML) for the Internet of Things (IoT)-enabled grid. The proposed DSM engine is responsible to preserve the efficient utilization of energy based on priorities. A specific resilient model is proposed to control intrusions in the smart grid. The resilient agent predicts the dishonest entities using the ML classifier. Advanced energy management and interface controlling agents are proposed to process energy information to optimize energy utilization. The efficient simulation is executed to test the efficiency of the proposed scheme. The analysis results reveal that the projected DSM engine is less vulnerable to the intrusion and effective enough to reduce the power utilization of the smart grid.