Optimizing Energy Consumption of Household Appliances Using PSO and GWO

Due to the increasing electricity consumption in the residential sector, new control systems emerged to control the demand side. Some techniques have been developed, such as shaping the curve's load peaks by planning and shifting the electricity demand for household appliances. This paper presents a comparative analysis for the energy consumption optimization of two household appliances using two Swarm Intelligence (SI) algorithms: Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). This problem's main objective is to minimize the energy cost according to both machines' energy consumption, respecting the restrictions applied. Three scenarios are presented: changing the energy market price during the day according to three types of energy tariffs. The results show that the user in the cheapest periods could switch on both machines because both techniques presented the highest energy consumption values. Regarding the objective function analysis, PSO and GWO obtained the best (more economical) values for the simple tariff due to its lower energy consumption. The GWO technique also presented more diverging values from the average objective function value than the PSO algorithm.