A multi-objective evolutionary solution to improve the quality of life in smart cities

In this paper, we investigate the joint optimization of different smart grid components. Unlike most of previous works that optimize only one objective function, this paper proposes a multi-objective optimization solution that includes the energy cost, users' comfort level, and the lifetime of storage devices. Our solution is based on the Multi-Objective Evolutionary Algorithm (MOEA) framework. Experimental results, based on Reference Energy Disaggregation Data Set (REDD) representing real power demand from different houses, show that our solution provides energy saving by up to 46% while keeping the comfort level above 70% and the lifetime of storage devices above 8 years by reducing the number of charging/discharging cycles.