Optimizing energy consumption patterns of smart home using a developed elite evolutionary strategy artificial ecosystem optimization algorithm

In recent times, the tremendous progress in electronic devices and making them available to all consumers has led to an increase in demand for energy, the formation of a peak, and an increase in the electricity bill of the consumer. Buildings are considered one of the main sectors of energy consumption. Improving the energy efficiency of these buildings will achieve economic and environmental goals. This paper proposes a modified algorithm for scheduling electrical appliances in the smart home to reduce the electricity bill, improve the network, maximize customer comfort and reduce peak formation. This paper uses load conversion to change the shape of the load, as it is one of the types of demand side management (DSM) strategies. A modified algorithm, called elite evolutionary strategy artificial ecosystem-based optimization (EESAEO), is used in scheduling household appliances. The obtained results using the EESAEO algorithm reduce the electricity bill, improve the network, maximize customer comfort and reduce peak formation. The original algorithm is used to compare the simulated results and to verify the strength of the proposed EESAEO algorithm. Different price strategies such as TOU, CPP, and RTP are used, to ensure the efficiency of the studied system. These successful results prove that the applied method can be used for scheduling household appliances.