Scheduling of smart home appliances' energy consumption

Energy consumption increases, as the number of electrical appliances in households increase day by day. Electricity tariffs in which prices vary over time have been created, to distribute the load of increased electricity consumption throughout the day. These tariffs can be exploited optimally with the proper planning of smart home appliances used in homes to reduce the costs and to achieve balanced load distribution. In this thesis, approaches are proposed for the problem of scheduling of the smart home appliances. Heuristic algorithms are used for the solution of the problem. Genetic algorithm, differential evolution and simulated annealing have been customized for this work. In addition to these, modified genetic algorithm and modified differential evolution has been proposed. Also hybrid approaches are developed by applying simulated annealing to the results from these algorithms. As a result of the tests executed in various scenarios, it has been found that simulated annealing after genetic algorithm is the most efficient and all algorithms significantly reduces the cost.