Abstract
With the advent of the smart grid, it has become possible to improve the energy systems. To optimize the energy consumption pattern of the appliances, home energy management system is proposed for smart homes. Energy management in smart homes is a challenging task, therefore, the concept of demand-side management was introduced. For the effective scheduling of smart appliance, we propose a metaheuristic optimization technique. The proposed technique is hybrid of two existing techniques: Tabu Search (TS) and Bacterial Foraging Algorithm (BFA). The aim of the proposed technique is to reduce energy consumption so that user electricity bill reduces. Also, improves user comfort in term of average waiting time. For electricity bill calculation and appliance scheduling, time of use price tariff is used. Simulation results demonstrate that proposed scheme outperformed existing schemes in cost reduction and the average waiting time minimization. However, TS outruns other scheduling schemes in peak to average ratio reduction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Demand Side Management (vol. 1, Overview of Key Issues), Final Rep. for RP2381-4, prepared by Battelle-Columbus Division and Synergic Resources Corp., EA/EM-3597. Electric Power Research institute, Palo Alto, CA, August 1984
U.S. Congress, Office of Technology Assessment 1993, Adapted From Battelle-Columbus Division and Synergic Resources Corp., Demand Side Management, Volume 3: Technology Alternatives and Market Implementation Methods EPRIIAVEM-3597. Eclectic Power Research Institute, Palo Alto, CA (1984)
Davito, B., Tai, H., Uhlaner, R.: The smart grid and the promise of demand-side management. McKinsey Smart Grid 3, 8–44 (2010)
Anvari-Moghaddam, A., Monsef, H., Rahimi-Kian, A.: Optimal smart home energy management considering energy saving and a comfortable lifestyle. IEEE Trans. Smart Grid 6(1), 324–332 (2015)
Zhu, Z., Lambotharan, S., Chin, W.H., Fan, Z.: A game theoretic optimization framework for home demand management incorporating local energy resources. IEEE Trans. Ind. Inform. 11(2), 353–362 (2015)
Paterakis, N.G., Erdinc, O., Bakirtzis, A.G., Catalão, J.P.: Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies. IEEE Trans. Ind. Inform. 11(6), 1509–1519 (2015)
Roh, H.T., Lee, J.W.: Residential demand response scheduling with multiclass appliances in the smart grid. IEEE Trans. Smart Grid 7(1), 94–104 (2016)
Zhang, D., Li, S., Sun, M., O’Neill, Z.: An optimal and learning-based demand response and home energy management system. IEEE Trans. Smart Grid 7(4), 1790–1801 (2016)
Graditi, G., Di Silvestre, M.L., Gallea, R., Sanseverino, E.R.: Heuristic-based shiftable loads optimal management in smart micro-grids. IEEE Trans. Ind. Inform. 11(1), 271–280 (2015)
Ma, J., Chen, H.H., Song, L., Li, Y.: Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7(2), 771–784 (2016)
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 8(2), 1–10 (2015)
Zhao, C., Dong, S., Li, F., Song, Y.: Optimal home energy management system with mixed types of loads. CSEE J. Power Energy Syst. 1(4), 29–37 (2015)
Joo, I.Y., Choi, D.H.: Optimal household appliance scheduling considering consumer’s electricity bill target. IEEE Trans. Consum. Electr. 63(1), 19–27 (2017)
Joo, I.Y., Choi, D.H.: Distributed optimization framework for energy management of multiple smart homes with distributed energy resources. IEEE Access 5, 15551–15560 (2017)
Shafiq, S., Fatima, I., Abid, S., Asif, S., Ansar, S., Abideen, Z.U., Javaid, N.: Optimization of home energy management system through application of tabu search. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 37–49. Springer, Cham, November 2017
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 2016
Passino, K.M.: Biomimicry of BFA for distributed optimization and control. Control Syst. IEEE 22(3), 52–67 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Khalid, M.U., Javaid, N., Iqbal, M.N., Jamil, A., Anwar, N., Haq, Q.M.F.E. (2019). Hybrid Bacterial Foraging Tabu Search Energy Optimization Technique in Smart Homes. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-93659-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93658-1
Online ISBN: 978-3-319-93659-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)