Home Energy Management Using Fish Swarm Optimization Bacterial Foraging Algorithm and Genetic Algorithm in Smart Grid
Energy crises are serious issues due to exponential increase in demand of energy. To tackle the issue of increase in demand, an integration of traditional grid with Demand Side Management (DSM). As need to resolve energy crises issues in residential areas smart homes are introduced; contains Smart Meters (SM), which allows bidirectional communication between utilities and end users. Different heuristic techniques are used to overcome these issues. The energy management is more necessary in residential area as there is verity of different appliances and power rates to schedule. The heuristics techniques provide most optimal solution. The purpose of our implementation is to reduce the total cost and Peak to Average Ratio (PAR) value and while keeping in mind the trade-off with waiting time up to an acceptable limit.
- 1.Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear programming based optimization for home demand-side management in smart grid. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1–5. IEEE, January 2012Google Scholar
- 8.Huang, Y., Wang, L., Guo, W., Kang, Q., Wu, Q.: Chance constrained optimization in a home energy management system. IEEE Trans. Smart Grid (2017)Google Scholar
- 11.Roh, H.-T., Lee, J.-W.: Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid. IEEE Trans. Smart Grid TSG.2015.2445491. IEEEGoogle Scholar
- 12.Basit, A., Sidhu, G.A.S., Mahmood, A., Gao, F.: Efficient and autonomous energy management techniques for the future smart homes. IEEE Trans. Smart Grid (2015)Google Scholar
- 14.Rocha, A., Costa, M., Fernandes, E.: An artificial fish swarm filter-based method for constrained global optimization. In: Computational Science and Its Applications ICCSA 2012, pp. 57–71 (2012)Google Scholar
- 16.Khalid, A., Javaid, N., Mateen, A., Khalid, B., Khan, Z. A., Qasim, U.: Demand side management using hybrid bacterial foraging and genetic algorithm optimization techniques. In: 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), pp. 494–502. IEEE, July 2016Google Scholar