Improving energy efficiency in smart-houses by optimizing electrical loads management

In this work, the Genetic Algorithm is explored for solving a predictive based demand side management problem (a combinatorial optimization problem) and the main measures lbr performance evaluation are evaluated. In this context, we propose a smart energy scheduling approach for household appliances in real-time to achieve minimum consumption costs and a reduction in peak load. We consider a scenario of selfconsumption where the surplus from local power generation can be sold to the grid, and the existence of appliances that can be shiftable from peak hours to off-peak hours. Results confirm the importance of the tuning procedure and the structure of the genome and algorithm's operators determine the performance of such type of meta-heuristics. This fact is more decisive when there are several operational constraints on the system, as for example short-term optimal scheduling decision, time constraints and power limitations. Details about the scheduling problem, comparison strategies, metrics, and results are provided.European Union under the FEDER (Fundo Europeu de Desenvolvimento Regional) programEuropean Union (EU) [0076_AGERAR_6_E]FCT, through IDMEC under LAETA [UID/EMS/50022/2019