A genetic evolutionary task scheduling method for energy efficiency in smart homes

For electricity consumers, there are power loads which need to be processed in a predefined time interval. The electricity price could vary between peak and off-peak time. In that case, the intelligent task scheduling module in a smart home can minimize the entire energy expense if the task control module could schedule the electrical equipments' start times, which are determined by their power consumptions and operation time constraints. In Smart Grid environments, this Advanced Metering Infrastructure (AMI) could automatically schedule the operation time of each equipment to minimize the residential overall power consumption while satisfying the equipment's operation constraint such as the equipment needs to be started at a time between two predefined time instants, and the power system is not overloaded at any time instant. In this research, the paper formulates the situation as an optimization problem and proposes a Genetic Algorithm (GA) based algorithm to find the optimum schedule arrangement for all the tasks in a smart home to reduce the energy cost. The performance of the GA based method is evaluated with the previous research works such as SA based method and greedy search method. The simulation results show that the GA based scheduling algorithm can efficiently and optimally minimize customers' electricity cost.