An Efficient Home Energy Optimization by Using Meta-heuristic Techniques While Incorporating Game-theoretic Approach for Real-time Coordination Among Home Appliances

For the optimization of home energy consumption, we proposed a system model based on scheduling techniques and also, incorporated real-time coordination among household appliances by using game theory (GT). Main objective of scheduling techniques is to decrease the electricity cost of consumers by efficiently managing the home energy consumption on the basis of real-time pricing (RTP) signal whereas, coordination is implemented among household appliances in order to increase the user comfort by decreasing the appliances delay. Scheduling techniques: cuckoo search algorithm (CSA), earthworm algorithm (EWA), bat algorithm (BA) and a proposed hybrid scheme (HCEO), originated from CSA and EWA, are implemented and results demonstrate that proposed hybrid scheme has reduced the total electricity cost by 49.09% as compared to unscheduled case and it outperformed other scheduling techniques in terms of cost reduction. Performance of scheduling techniques before and after coordination is evaluated and a comparison is performed with each other. Results indicate that a trade-off exists between electricity cost and appliances delay.