Multiobjective Unfolding of Shared Power Consumption Pattern Using Genetic Algorithm for Estimating Individual Usage in Smart Cities

In smart cities residential homes are fully equipped with information networking and computing technologies and are connected to the power grid via intelligent meters. Connectivity of meters allows formation of groups of residents, which are physically close, and as a result individual consumptions can be aggregated into a shared consumption. In this paper an approach of unfolding shared consumption and making inferences about resident personal usage is presented. The proposed approach tackles the problem of unfolding as a multiobjective problem in which a set of residential profiles is fitted to the measured consumption. A solution to the multiobjective problem is sought by using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) that utilizes the Pareto optimality theory to identify an optimal solution. The approach is applied to a set electricity consumption signals for making inferences about the personal energy usage of residential participants in the shared consumption pattern.