P33 Privacy Preserving Prediction of Real Time Energy Demands in EV Charging Networks

Abstract:

With the increasing adoption of Electric Vehicles (EVs), predicting real-time energy demands in charging networks has become paramount for efficient energy management. However, this task poses significant privacy concerns due to the sensitive nature of energy consumption data. This paper proposes P33, a privacy-preserving approach for predicting real-time energy demands in EV charging networks. P33 leverages advanced cryptographic techniques and machine learning algorithms to ensure data privacy while accurately forecasting energy requirements. The system employs homomorphic encryption and secure multi-party computation to enable collaborative learning without compromising the confidentiality of individual charging profiles. Additionally, P33 utilizes robust prediction models tailored for EV charging patterns, incorporating factors such as historical charging data, weather conditions, and traffic patterns. Experimental results demonstrate the effectiveness and privacy preservation capabilities of P33 compared to traditional prediction methods. By safeguarding the privacy of EV users' data while facilitating accurate energy demand prediction, P33 represents a significant advancement towards secure and efficient management of charging networks in the era of electric mobility.