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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Jonnadula Narasimharao
CMR Technical Campus
Control & Systems Engineering Electrical & Electronics Engineering

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A privacy-preserving IoT-machine learning framework for optimized and secure demand-side management in smart grids S. Pushpa; Jonnadula Narasimharao; K. L. Kishore; T. Sathish Kumar; M. Bhoopathi; R. Kalaivani
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2004-2012

Abstract

This paper presents an integrated internet of things (IoT) and machine learning-based framework for secure and efficient demand-side management (DSM) in modern smart grids. The proposed approach combines long short-term memory (LSTM) networks for accurate load forecasting, federated learning (FL) for decentralized privacy-preserving model training, and blockchain technology for secure and tamper-proof communication. In addition, a digital twin (DT)-assisted architecture is incorporated to enable predictive decision-making and system-level optimization. The framework explicitly considers renewable energy integration, electric vehicle (EV) charging loads, distributed energy storage, and power electronic converter constraints. Simulation results demonstrate a reduction in forecasting error by 15-25%, a 25% decrease in daily energy cost, and a 23.7% reduction in peak demand. The proposed system achieves improved voltage stability with reduced deviation and enhances overall efficiency up to 88%. Furthermore, cybersecurity performance is validated with an anomaly detection AUC of 0.95 and reduced data transmission through FL by 87%. The results confirm that the proposed framework provides a scalable, secure, and intelligent solution for next-generation smart grid applications.
Co-Authors K. L. Kishore M. Bhoopathi R. Kalaivani S. Pushpa T. Sathish Kumar