Optimization Technique in Smart Home Environment: A Systematic Review
Keywords:
Smart Home, Optimization, Heuristic Algorithm, Meta-Heuristic AlgorithmAbstract
A smart home is a concept that seeks to use as little energy as possible while maximizing occupant comfort. The main difficulty encountered by power supply providers is optimising the power requirements for smart home devices in a smart grid, especially during peak times due to its significant impact on the reliability of a power grid. It gets harder for the user to manage or use each individual device in a smart home properly as the number of devices increases. Optimization models may be used to regulate smart appliances, but they must be effective and realistic To reduce power usage and increase user happiness, we describe heuristic and meta-heuristic optimization techniques in this work for home energy management systems. When faced with difficult, sophisticated optimisation challenges, such as those arising in the design of power electronics converters, metaheuristic techniques offer a practical option. An alternative strategy involves using optimisation heuristics like evolutionary algorithms, neural networks, genetic algorithms, tabu search, hybrid approaches, and many others.
Downloads
References
“Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments | IEEE Conference Publication | IEEE Xplore.” https://ieeexplore.ieee.org/abstract/document/9288505 (accessed Sep. 03, 2022).
A. S. Shah, H. Nasir, M. Fayaz, A. Lajis, and A. Shah, “A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments,” Information, vol. 10, no. 3, Art. no. 3, Mar. 2019, doi: 10.3390/info10030108.
M. R. Alam, M. B. I. Reaz, and M. A. M. Ali, “A Review of Smart Homes—Past, Present, and Future,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 42, no. 6, pp. 1190–1203, Nov. 2012, doi: 10.1109/TSMCC.2012.2189204.
S. Sohaib, I. Sarwar, M. H. Iftikhar, and A. Mahmood, “A low cost smart energy monitoring and control system for smart buildings,” in 5th IET International Conference on Renewable Power Generation (RPG) 2016, Sep. 2016, pp. 1–5. doi: 10.1049/cp.2016.0598.
S. N. Makhadmeh, A. T. Khader, M. A. Al-Betar, S. Naim, A. K. Abasi, and Z. A. A. Alyasseri, “Optimization methods for power scheduling problems in smart home: Survey,” Renew. Sustain. Energy Rev., vol. 115, p. 109362, Nov. 2019, doi: 10.1016/j.rser.2019.109362.
T. Logenthiran, D. Srinivasan, and T. Z. Shun, “Demand Side Management in Smart Grid Using Heuristic Optimization,” IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1244–1252, Sep. 2012, doi: 10.1109/TSG.2012.2195686.
S. Desale, A. Rasool, S. Andhale, and P. Rane, “Heuristic and Meta-Heuristic Algorithms and Their Relevance to the Real World: A Survey,” Int. J. Comput. Eng. Res. TRENDS, vol. 2, no. 5, p. 9, 2015.
“Local Search Algorithm - an overview | ScienceDirect Topics.” https://www.sciencedirect.com/topics/computer-science/local-search-algorithm (accessed Sep. 06, 2022).
“Divide-and-conquer algorithm,” Wikipedia. Apr. 03, 2022. Accessed: Sep. 06, 2022. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Divide-and-conquer_algorithm&oldid=1080787129
R. Martí, M. Laguna, and F. Glover, “Principles of scatter search,” Eur. J. Oper. Res., vol. 169, no. 2, pp. 359–372, Mar. 2006, doi: 10.1016/j.ejor.2004.08.004.
M. Laguna, “Tabu Search,” in Handbook of Heuristics, R. Martí, P. M. Pardalos, and M. G. C. Resende, Eds. Cham: Springer International Publishing, 2018, pp. 741–758. doi: 10.1007/978-3-319-07124-4_24.
A. Alsheddy, C. Voudouris, E. P. K. Tsang, and A. Alhindi, “Guided Local Search,” in Handbook of Heuristics, R. Martí, P. Panos, and M. G. C. Resende, Eds. Cham: Springer International Publishing, 2016, pp. 1–37. doi: 10.1007/978-3-319-07153-4_2-1.
S. Sakamoto, K. Ozera, M. Ikeda, and L. Barolli, “Implementation of Intelligent Hybrid Systems for Node Placement Problem in WMNs Considering Particle Swarm Optimization, Hill Climbing and Simulated Annealing,” Mob. Netw. Appl., vol. 23, no. 1, pp. 27–33, Feb. 2018, doi: 10.1007/s11036-017-0897-7.
T. Stützle, “Iterated local search for the quadratic assignment problem,” Eur. J. Oper. Res., vol. 174, no. 3, pp. 1519–1539, Nov. 2006, doi: 10.1016/j.ejor.2005.01.066.
“Stochastic Search Algorithm - an overview | ScienceDirect Topics.” https://www.sciencedirect.com/topics/engineering/stochastic-search-algorithm (accessed Sep. 09, 2022).
A. A. Butt, N. Javaid, S. Mujeeb, S. Ahmed, M. M. S. Ali, and W. Ali, “Foged energy optimization in smart homes,” Adv. Intell. Syst. Comput., vol. 773, pp. 265–275, 2019, doi: 10.1007/978-3-319-93554-6_24.
P. U. B. Albuquerque, D. K. A. Ohi, N. S. Pereira, B. A. Prata, and G. C. Barroso, “Proposed Architecture for Energy Efficiency and Comfort Optimization in Smart Homes: Smart Home Architecture for Energy Efficiency,” J. Control Autom. Electr. Syst., vol. 29, no. 6, pp. 718–730, 2018, doi: 10.1007/s40313-018-0410-y.
X. Jiang and L. Wu, “A Residential Load Scheduling Based on Cost Efficiency and Consumer’s Preference for Demand Response in Smart Grid,” Electr. Power Syst. Res., vol. 186, p. 106410, Sep. 2020, doi: 10.1016/j.epsr.2020.106410.
H. T. Haider, O. H. See, and W. Elmenreich, “Dynamic residential load scheduling based on adaptive consumption level pricing scheme,” Electr. Power Syst. Res., vol. 133, pp. 27–35, Apr. 2016, doi: 10.1016/j.epsr.2015.12.007.
A. Khan, N. Javaid, and M. I. Khan, “Time and device based priority induced comfort management in smart home within the consumer budget limitation,” Sustain. Cities Soc., vol. 41, pp. 538–555, Aug. 2018, doi: 10.1016/j.scs.2018.05.053.
A.-H. Mohsenian-Rad and A. Leon-Garcia, “Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments,” IEEE Trans. Smart Grid, vol. 1, no. 2, pp. 120–133, Sep. 2010, doi: 10.1109/TSG.2010.2055903.
D. N. Mekuria, P. Sernani, N. Falcionelli, and A. F. Dragoni, “Smart home reasoning systems: a systematic literature review,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 4, pp. 4485–4502, 2021, doi: 10.1007/s12652-019-01572-z.
M. Li, G.-Y. Li, H.-R. Chen, and C.-W. Jiang, “QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles,” Energies, vol. 11, no. 9, Art. no. 9, Sep. 2018, doi: 10.3390/en11092304.
O. Samuel, S. Javaid, N. Javaid, S. H. Ahmed, M. K. Afzal, and F. Ishmanov, “An efficient power scheduling in smart homes using jaya based optimization with time-of-use and critical peak pricing schemes,” Energies, vol. 11, no. 11, 2018, doi: 10.3390/en11113155.
A. Basit, G. A. S. Sidhu, A. Mahmood, and F. Gao, “Efficient and Autonomous Energy Management Techniques for the Future Smart Homes,” IEEE Trans. Smart Grid, vol. 8, no. 2, pp. 917–926, Mar. 2017, doi: 10.1109/TSG.2015.2504560.
S. Kumar, V. Pavithra, R. Banu, and G. Supriya, “Smart Home Energy Management System including Renewable Energy Based on Zigbee and ARM9 Microcontroller.” Rochester, NY, Mar. 28, 2017. doi: 10.2139/ssrn.2942428.
Y. F. Du, L. Jiang, Y. Li, and Q. Wu, “A Robust Optimization Approach for Demand Side Scheduling Considering Uncertainty of Manually Operated Appliances,” IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 743–755, Mar. 2018, doi: 10.1109/TSG.2016.2564159.
“Multi-objective demand side scheduling considering the operational safety of appliances - ScienceDirect.” https://www.sciencedirect.com/science/article/pii/S0306261916309576 (accessed Sep. 08, 2022).
S.-V. Oprea, A. Bâra, and A. Reveiu, “Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders,” Energies, vol. 11, no. 1, Art. no. 1, Jan. 2018, doi: 10.3390/en11010138.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.