Weekly Energy Management of a Smart Home based on the Internet of Things
Keywords:
smart home, Internet of Things, home energy resources, reducing the cost of energy consumption, reducing the energy not supplied.Abstract
Energy management and related issues are perhaps the most important challenges facing electricity distribution companies in the modern era. Optimizing energy consumption in smart homes has attracted the attention of advanced countries in recent years due to their ability to reduce carbon dioxide gas produced from fossil fuels. For this purpose, in this study, planning the energy management of a smart home based on the Internet of Things to investigate its effect on reducing the weekly cost of energy consumption of a smart home in normal mode and reducing the amount of energy not supplied when the smart home is disconnected from the network, with it is suggested to consider the behavior of household consumers for energy planning during a week. In this study, in addition to considering the role of controllable and uncontrollable household loads in improving the technical and economic goals of the smart home, from distributed energy resources such as energy storage system (ESS), plug in hybrid electric vehicle (PHEV), photovoltaic system (PV) and wind turbine (WT) have been used in smart home energy management. In this research, the linearization of non-linear mathematical equations has been used to reduce the calculation time and reduce the complexity of the calculations. In this study, the smart home is examined in normal mode and in the mode of disconnection from the main network. The results show that the presence of energy resources in smart homes and the optimal energy management of household loads can have an effective effect on reducing the cost of energy consumption and the energy not supplied.
Downloads
References
SoftMax. Available: https://towardsdatascience.com/softmax-activation-function-explained-a7e1bc3ad60
Hussain A, Shah SD, A. SM. Heuristic optimization-based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks. IET Smart Grid. Vol. 6, No. 2, 2019.
BS. Sami, N. Sihem, Z. Bassam, “Design and implementation of an intelligent home energy management system: A realistic autonomous hybrid system using energy storage”, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2018.09.001, 2018.
Park, C., Kim, Y. and Jeong, M. “Influencing factors on risk perception of IoT-based home energy management services”, Telematics and Informatics, 35(8), pp.2355-2365, 2018.
AlFaris, F., Juaidi, A. and Manzano-Agugliaro, F., 2017. Intelligent homes’ technologies to optimize the energy performance for the net zero energy home. Energy and Buildings, 153, pp.262-274.
Karthik, S. and Vennila, I., 2021. Power management in smart home based on IoT application. International Journal of Nonlinear Analysis and Applications, 12, pp.1703-1712.
Khan, M.A., Sajjad, I.A., Tahir, M. and Haseeb, A., 2022. IOT Application for Energy Management in Smart Homes. Engineering Proceedings, 20(1), p.43.
Jo, H. and Yoon, Y.I., 2018. Intelligent smart home energy efficiency model using artificial TensorFlow engine. Human-centric Computing and Information Sciences, 8(1), pp.1-18.
Affum, E.A., Agyekum, K.A.P., Gyampomah, C.A., Ntiamoah-Sarpong, K. and Gadze, J.D., 2021. Smart home energy management system based on the internet of things (IoT).
Condon, F., Martínez, J.M., Eltamaly, A.M., Kim, Y.C. and Ahmed, M.A., 2022. Design and Implementation of a Cloud-IoT-Based Home Energy Management System. Sensors, 23(1), p.176.
Tastan, M., 2019. Internet of things based smart energy management for smart home. KSII Transactions on Internet and Information Systems (TIIS), 13(6), pp.2781-2798.
Lokeshgupta, B. and Ravivarma, K., 2023. Coordinated smart home energy sharing with a centralized neighbourhood energy management. Sustainable Cities and Society, p.104642.
Alghtani, A.H., Tirth, V. and Algahtani, A., 2023. Lens-oppositional duck pack algorithm based smart home energy management system for demand response in smart grids. Sustainable Energy Technologies and Assessments, 56, p.103112.
Nakıp, M., Çopur, O., Biyik, E. and Güzeliş, C., 2023. Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network. Applied Energy, 340, p.121014.
Lin, Y.H., Tang, H.S., Shen, T.Y. and Hsia, C.H., 2022. 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, 10, pp.116747-116765.
Mehrabani, A., Mardani, H. and Ghazizadeh, M.S., 2022, February. Optimal energy management in smart home considering renewable energies, electric vehicle, and demand-side management. In 2022 9th Iranian Conference on Renewable Energy & Distributed Generation (ICREDG) (pp. 1-5). IEEE.
Huang, J., Koroteev, D.D. and Rynkovskaya, M., 2023. Machine learning-based demand response in PV-based smart home considering energy management in digital twin. Solar Energy, 252, pp.8-19.
Rigo-Mariani, R. and Ahmed, A., 2023. Smart Home Energy Management with Mitigation of Power Profile Uncertainties and Model Errors. Energy and Buildings, p.113223.
U. ur Rehman, P. Faria, L. Gomes, and Z. Vale, Future of Energy Management Systems in Smrat Cities: A Systematic Literature Review. Sustainable Cities and Society, p.104720, 2023.
Youssef, H., Kamel, S., Hassan, M.H. and Nasrat, L., 2023. Optimizing energy consumption patterns of smart home using a developed elite evolutionary strategy artificial ecosystem optimization algorithm. Energy, 278, p.127793.
Lee, K.P., Chng, C.W., Tong, D.L. and Tseu, K.L., 2023. Optimizing Energy Consumption on Smart Home Task Scheduling using Particle Swarm Optimization. Procedia Computer Science, 220, pp.195-201.
Rohde, F. and Santarius, T., 2023. Emerging sociotechnical imaginaries–How the smart home is legitimised in visions from industry, users in homes and policymakers in Germany. Futures, p.103194.
Rokonuzzaman, M., Akash, M.I., Mishu, M.K., Tan, W.S., Hannan, M.A. and Amin, N., 2022, May. IoT-based Distribution and Control System for Smart Home Applications. In 2022 IEEE 12th Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 95-98). IEEE.
Majumdar, S., Thakur, C. and Chatterjee, S., 2023, January. Cooperative Spectrum Sensing based Hybrid Smart Home Energy Management System with Energy Harvesting. In 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-5). IEEE.
Shan, G., Lee, H. and Roh, B.H., 2022, November. Indoor Localization-based Energy Management for Smart Home. In 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) (pp. 1-5). IEEE.
Arab, M.B., Rekik, M. and Krichen, L., 2022, March. A Smart Home Energy Consumption Optimisation Based on Multi-Constraints PSO Strategy. In 2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET) (pp. 544-549). IEEE.
Khemakhem, S., Rekik, M. and Krichen, L., 2022, December. Smart home power monitoring based on Internet of Things paradigm. In 2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) (pp. 731-735). IEEE.
AlKassem, A. and AlKabi, M., 2022, September. A TOPSIS Model to Support Smart Appliance Decision Energy Management in Smart Grid. In 2022 11th International Conference on Renewable Energy Research and Application (ICRERA) (pp. 534-539). IEEE.
Lee, S. and Choi, D.H., 2020. Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources. IEEE Transactions on Industrial Informatics, 18(1), pp.488-497.
Khan, M., Silva, B.N., Khattab, O., Alothman, B. and Joumaa, C., 2022. A Transfer Reinforcement Learning Framework for Smart Home Energy Management Systems. IEEE Sensors Journal.
Zenginis, I., Vardakas, J., Koltsaklis, N.E. and Verikoukis, C., 2022. Smart Home’s Energy Management through a Clustering-based Reinforcement Learning Approach. IEEE Internet of Things Journal, 9(17), pp.16363-16371.
Wang, R., Jiang, S., Ma, D., Sun, Q., Zhang, H. and Wang, P., 2022. The Energy Management of Multiport Energy Router in Smart Home. IEEE Transactions on Consumer Electronics, 68(4), pp.344-353.
R. Fathi, B. Tousi, and S. Galvani. "Allocation of renewable resources with radial distribution network reconfiguration using improved salp swarm algorithm" Applied Soft Computing, vol.132, 2023.
Hemmati R, Saboori H. Stochastic optimal battery storage sizing and scheduling in home energy management systems equipped with solar photovoltaic panels. Energy and Buildings. Vol. 52, 2017.
N. Rugthaicharo, S. Sirisumrukul, Feeder Reconfiguration for Loss Reduction in Distribution System with Distributed Generators by Tabu Search. GMSARN International Journal, Vol. 3, pp. 47-54, 2009.
Wang Y, Yang Z, Mourshed M, Guo Y, Niu Q, Zhu X. Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method. Energy conversion and management. Vol.15, 2019.
Luo L, Abdulkareem SS, Rezvani A, Miveh MR, Samad S, Aljojo N, Pazhoohesh M. Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty. Journal of Energy Storage. Vol. 28, 2020.
Mirzamohammadi S, Jabarzadeh A, Shahrabi MS. Long-term planning of supplying energy for greenhouses using renewable resources under uncertainty. Journal of Cleaner Production. Vol. 16, 2020.
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.
