Volume 15 , Issue 2 , PP: 17-26, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Osama Mohammed 1 , Marwa Ibrahim 2 , Abdalrahman Fatikhan Ataalla 3 *
Doi: https://doi.org/10.54216/JCIM.150202
The integration of sensing technologies with residential buildings raises the concept of a smart home, which has facilitated the life of the habitant nowadays. This technology helps us to track and understand the behavior of the client in the house to give him maximum comfort. A neighborhood area is an interconnected set of houses that exist in the same geographical region and share the same energy resources. The most important component in the process of decision-making is the energy usage in the smart building. The energy optimization problem in the smart building created a challenge for enterprises and the government for a long time. A lot of research were made to solve this energy optimization problem. One of these problems is the organization of energy usage within a neighborhood area network. The main challenges are to maintain the user comfort in each house and to not exceed the total energy offered to the network. For this, we proposed a technique that predicts, based on historical data of each house, its future behavior and created for each one a weekly schedule with hourly annotated field with: high, normal, or low, where each one represents the amount of energy user is able to use at this time. At the end, an incentive-based program is created to give the client an incentive on his bill if he used the daily high energy consumption in the annotated high in his schedule. To create the schedules, we extracted some features from the data, then we used the genetic algorithm to create schedules, then we did an improvement to the technique using dynamic programming that stores the features of a house with created schedule, later when we meet a similar house we can directly give a schedule that fits the need.
Smart home , energy management , incentive-based program , dynamic programming , genetic algorithm
[1] H. Farhangi, “The path of the smart grid,” IEEE Power Energy Mag., vol. 8, no. 1, pp. 18–28, 2010, doi: 10.1109/MPE.2009.934876.
[2] KETSEAS, D. (2024). Stochastic Response of an Airfoil and Its Effects on Lco’s Behavior Under Stall Flutter Regime. International Journal of Mathematics, Statistics, and Computer Science, 2, 168–172. https://doi.org/10.59543/ijmscs.v2i.8663
[3] H. Merdanoğlu, E. Yakıcı, O. T. Doğan, S. Duran, and M. Karatas, “Finding optimal schedules in a home energy management system,” Electr. Power Syst. Res., vol. 182, no. January, p. 106229, 2020, doi: 10.1016/j.epsr.2020.106229.
[4] B. Celik, R. Roche, S. Suryanarayanan, D. Bouquain, and A. Miraoui, “Electric energy management in residential areas through coordination of multiple smart homes,” Renew. Sustain. Energy Rev., vol. 80, no. May, pp. 260–275, 2017, doi: 10.1016/j.rser.2017.05.118.
[5] M. N. Q. Macedo, J. J. M. Galo, L. A. L. De Almeida, and A. C. De, “Demand side management using artificial neural networks in a smart grid environment,” Renew. Sustain. Energy Rev., vol. 41, pp. 128–133, 2015, doi: 10.1016/j.rser.2014.08.035.
[6] Fatikhan, A. Abed, K. Hikmat, A. Yousif, M. Salim, A. Hatem, Q. Bader, S. "A Hybrid GA-GWO Method for Cyber Attack Detection Using RF Model," Journal of Cybersecurity and Information Management, vol. , no. , pp. 225-232, 2025. DOI: https://doi.org/10.54216/JCIM.150117
[7] N., S. Yousif, M. Bader, S. Mohammed, O. Hikmat, A. "A Public Key Infrastructure Based on Blockchain for IoT-Based Healthcare Systems," Journal of Cybersecurity and Information Management, vol. , no. , pp. 233-243, 2025. DOI: https://doi.org/10.54216/JCIM.150118.
[8] Salim, A. N., S. Abul, D. Awad, M. "Credit Card Fraud Detection Model Based on Correlation Feature Selection," Journal of Cybersecurity and Information Management, vol. , no. , pp. 334-342, 2024. DOI: https://doi.org/10.54216/JCIM.140224
[9] I. Ullah and D. Kim, “An improved optimization function for maximizing user comfort with minimum energy consumption in smart homes,” Energies, vol. 10, no. 11, 2017, doi: 10.3390/en10111818.
[10] S. Ali and D. H. Kim, “Effective and comfortable power control model using Kalman filter for building energy management,” Wirel. Pers. Commun., vol. 73, no. 4, pp. 1439–1453, 2013, doi: 10.1007/s11277-013-1259-9.
[11] Z. Wang, L. Wang, A. I. Dounis, and R. Yang, “Multi-agent control system with information fusion based comfort model for smart buildings,” Appl. Energy, vol. 99, pp. 247–254, 2012, doi: 10.1016/j.apenergy.2012.05.020.
[12] Y. H. Lin, H. S. Tang, T. Y. Shen, and C. H. Hsia, “A Smart Home Energy Management System Utilizing Neurocomputing-Based Time-Series Load Modeling and Forecasting Facilitated by Energy Decomposition for Smart Home Automation,” IEEE Access, vol. 10, pp. 116747–116765, 2022, doi: 10.1109/ACCESS.2022.3219068.
[13] R. Elazab, O. Saif, A. M. A. A. Metwally, and M. Daowd, “New smart home energy management systems based on inclining block-rate pricing scheme,” no. June, pp. 503–511, 2022.
[14] T. Roy, A. Das, and Z. Ni, “Optimization in load scheduling of a residential community using dynamic pricing,” 2017 IEEE Power Energy Soc. Innov. Smart Grid Technol. Conf. ISGT 2017, no. c, 2017, doi: 10.1109/ISGT.2017.8086087.
[15] D. Liu, Y. Xu, Q. Wei, and X. Liu, “Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming,” IEEE/CAA J. Autom. Sin., vol. 5, no. 1, pp. 36–46, 2018, doi: 10.1109/JAS.2017.7510739.
[16] G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Comput. Electr. Eng., vol. 40, no. 1, pp. 16–28, 2014, doi: 10.1016/j.compeleceng.2013.11.024.
