Genetic-programming-based multi-objective optimization of strategies for home energy-management systems

Home energy-management systems can optimize performance either by computing the next step dynamically - online, or rely on a precomputed strategy used to introduce the next decision - offline. Further, such systems can optimize based on only one or several objectives. In this paper, the multiobjective optimization of offline strategies for home energy-management systems is addressed. Two approaches are compared: the common timetable-based versus our approach based on decision trees. The timetable-based strategy is optimized using a multi-objective genetic algorithm, while the tree-based strategy is optimized using multi-objective genetic programming. As a result, a set of rules that comprise the trees for efficient management of an energy system is generated automatically. First, the approaches are addressed theoretically, with the finding that the tree-based approach is more powerful than the timetable-based approach. Second, the performance of the tree-based approach is compared with the performance of the timetable-based approach and manually defined strategies in an experiment involving real-world data. A performance increase of up to 17% in terms of the cost objective was confirmed for the tree-based approach. This is achieved without changing the user habits, i.e., there is no need of having to adapt the appliance usage to the energy-management system. (C) 2020 The Authors. Published by Elsevier Ltd.