Optimizing Energy Consumption on Smart Home Task Scheduling using Particle Swarm Optimization
Kah Poh Lee, Chern Wei Chng, Dong Tong, Kwan Lee Tseu
Abstract
The primary challenge that smart home users faced is the high energy consumption bills they received every month and they do not know how to optimize the energy consumption of their smart home devices. This increases the country's energy demand and accelerates the greenhouse effect worldwide. Additionally, when users run non-shiftable appliances unevenly, their energy consumption may surpass the maximum permissible power consumption limit. As a result, a short blackout happened in smart homes. To address such challenges, this study builds a mobile application that allows smart home users to control the energy consumption of their smart home appliances effectively. A scheduling task based on particle swarm optimization (PSO) is used to monitor and optimize energy consumption based on human activities in the home. Two experiments were conducted: with and without the implementation of the PSO algorithm. The result showed that the PSO algorithm performs better in optimizing energy consumption.