Enhancing privacy of electricity consumption in smart cities through morphing of anticipated demand pattern utilizing self-elasticity and genetic algorithms

Abstract

The vision of smart cities has emerged from the integration of advances in information technologies to a city’s infrastructures and assets. The use of information will improve the daily operations, and lead to greener, safer, and more human friendly cities. The most preeminent city asset is the electrical power grid, upon which the modernization of human life is built. However, integration of power grid with information technologies, known as smart grid, comes at a cost of reduced privacy. Electricity consumer behavior expressed via daily consumption patterns may be visible to third parties, which can make critical inferences over the consumers’ personal life. In this paper, a new method is proposed that aims at enhancing consumer privacy in smart cities by proposing an intelligent aggregation of anticipated demand patterns of multiple consumers as a mean to hide individual features. To that end, the method utilizes consumers self-elasticities matrices and a genetic algorithm to create an aggregated pattern that masks individual consumption data. The proposed method is tested on real world electricity demand patterns, and morphing performance is recorded with respect to symmetric mean average percentage error (SMAPE) attaining morphing over 60% in all tested cases.