Home Energy Management System with Improved Binary PSO

Abstract

AbstractHome energy management systems (HEMS) have been proposed to manage energy consumption in smart houses. To reduce the amount of power utilized by the homes, an optimal home energy management strategy (OHEM) has presented in this paper. The OHEM algorithm is applied to determine when electric tasks are performed to optimize customer satisfaction and electric cost. There are two disadvantages to the existing scheduling methods. Most of the current techniques do not take into account the user’s comfort, and secondly, there is a lack of an effective optimization algorithm. In this paper, an improved binary particle swarm optimization (IPSO) is used to get accurate, optimal, and desirable solutions for the power consumption that is taking place in smart homes. The representation is done in a time slots manner. The main objective of the algorithms described here is to lower the cost of electricity and the conformity of the user. The mentioned IBPSO technique is then used to find the best working model that achieves the objectives mentioned above. Moreover, the improvement and progress range in minimizing the cost and consumer’s comfort is regulated and managed using a weighting parameter.KeywordsHome energy management systemElectricity billCustomer satisfactionImproved binary particle swarm optimization