A Lightweight Privacy-Preserving Load Forecasting and Monitoring Scheme Supporting Dynamic Billing for Smart Grids: No KDC Required

Load forecasting (LF) is a crucial process of predicting future energy load and demand in smart grids, allowing for mitigating equipment failures and power outages, besides facilitating effective power dispatching and infrastructure planning. Methods used in LF range from traditional statistical and mathematical models to modern machine learning (ML) algorithms, and it has been proven that the latter has a better ability to predict future loads. These techniques leverage the consumers’ energy consumption readings for use in the LF process; however, revealing these readings exposes sensitive consumer lifestyle information, hence violating their privacy. The majority of the existing works focus on obtaining precise LF and addressing consumers’ privacy violations during the LF process in the deployment phase has not been well-investigated yet. Moreover, the existing methods that can be employed to preserve privacy introduce high overhead and rely on a trusted third party, which undermines the trust assumption, making them less robust. Therefore, this article proposes a novel and efficient privacy-preserving LF scheme, called privacy-preserving LF and monitoring and billing (PLFMB), that utilizes inner product functional encryption (IPFE) and eliminates the need for a trusted key distribution center. PLFMB allows smart meters to encrypt their consumption readings while enabling the system operator (SO) to 1) evaluate a hybrid deep learning-based LF model developed to predict future loads accurately; 2) monitor the grid load; and 3) compute consumers’ bills following dynamic pricing, without revealing or learning consumers’ readings to protect their privacy. Our proposed scheme has been evaluated on a real energy consumption data set, demonstrating its feasibility, proficiency in LF, and robustness in preserving consumer’s privacy while maintaining reasonable overhead.