An Intelligent Task Scheduling System for Electrical Appliances Using Particle Swarm Optimization

Abstract: Over the previous years, electric power systems have experienced progressively visit stress condition because of consistently expanding power request, wasteful utilization of electric power age and transmission assets. Transmission line blackouts have been a typical reason for system stress conditions, which are conceivable to happen amid critical peak hours. Such occasions will cause a supply limit circumstance where falling disappointments and extensive territory power outages are conceivable. This research develops an intelligent task scheduling system for electrical appliances using particle swarm optimization. Object-oriented design methodology was used for system development. Particle Swarm Optimization (PSO) has been used to schedule domestic appliance to reduce consumption rate. PSO technique helps to balance load for each domestic appliance by scheduling load to the appliances. The load balance of domestic appliances such as freezer, water pump, water heater, tumble dryer and washing machine (energy consumption appliances), was modelled based on scheduled operation of several appliances at specific time according to customer lifestyle and priority of devices. System was implemented in Java programming language. The system was evaluated using weekly, monthly and yearly timeframe. The consumption rate of domestic appliances before and after optimization for weekly, monthly and yearly shows that optimization of the power consumed by domestic appliances reduced in all time frames, weekly with 12.8KWH, monthly with 51.2KWH and yearly with 665.6KWH.

Keywords: Energy consumption scheduling, inclining blockrates, price prediction, real-time pricing, wholesale electricitymarket, Task scheduling, Electrical appliances, Particle swam ptimization