Smart Home Energy Management System Under Dynamic pricing for developing countries using Particle Swarm Optimization

Smart home energy management systems (SHEMS) have become widely popular to reduce a residential load profile’s cost and energy consumption. The optimal design of SHEMS is achieved with various meta-heuristic optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO) etc. In this paper, PSO has been utilized to execute and estimate the optimum solution analytically for a developing country’s general load profile. These algorithmic optimization techniques help find the optimal solution called the fitness function for continuous and differentiable functions. The proposed formulation has been solved on a test system using PSO in a MATLAB environment. The result obtained using the PSO technique demonstrate the effectiveness of the proposed formulation. Furthermore, the binary particle swarm optimization BPSO performed better in reducing energy consumption and thus reduced cost up to thirty-nine per cent (39%) in the obtained results.