Analysis of Internet of Things Based Smart Home Systems for Electricity Consumption Efficiency
DOI:
https://doi.org/10.35870/ijsecs.v5i3.5668Keywords:
Smart Home, Internet of Things (IoT), Energy Efficiency, Electricity Consumption, Home AutomationAbstract
The IoT technology has opened up new horizons in household energy management through smart home systems. Smart home systems are based on the integration of electronic appliances with sensors and actuators, which provide automated and remote control of domestic devices. This article assesses IoT-based smart home systems as a tool for enhancing electricity consumption efficiency in residential domains. The research uses a literature-based approach complemented by prototype development using current sensors, motion sensors, and internet-connected microcontroller modules to collect real-time data about the usage of electrical energy to recognize the patterns of energy consumption among household appliances. A comparative analysis between normal operating conditions and those enabled by smart home automation is carried out. Results show that IoT-based smart homes lower electricity consumption by controlling device operation according to real usage conditions such as turning off idle devices, adjusting lighting levels based on human presence, and allowing remote control of appliances. These results prove that IoT-based smart home systems can be effectively used for reducing household electricity demand in compliance with energy sustainability efforts within digitally connected residential environments.
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Ghasemian, S., Faridzad, A., Abbaszadeh, P., Taklif, A., Ghasemi, A., & Hafezi, R. (2024). An overview of global energy scenarios by 2040: Identifying the driving forces using cross-impact analysis method. International Journal of Environmental Science and Technology, 21(11), 7749–7772. https://doi.org/10.1007/s13762-020-02738-5
González-Torres, M., Pérez-Lombard, L., Coronel, J. F., Maestre, I. R., & Yan, D. (2022). A review on buildings energy information: Trends, end-uses, fuels and drivers. Energy Reports, 8, 626–637. https://doi.org/10.1016/j.egyr.2021.11.280
Mrówczyńska, M., Skiba, M., Bazan-Krzywoszańska, A., & Sztubecka, M. (2020). Household standards and socio-economic aspects as a factor determining energy consumption in the city. Applied Energy, 264, 114680. https://doi.org/10.1016/j.apenergy.2020.114680
Huang, Y. H. (2020). Examining impact factors of residential electricity consumption in Taiwan using index decomposition analysis based on end-use level data. Energy, 213, 119067. https://doi.org/10.1016/j.energy.2020.119067
Auffhammer, M., & Mansur, E. T. (2014). Measuring climatic impacts on energy consumption: A review of the empirical literature. Energy Economics, 46, 522–530. https://doi.org/10.1016/j.eneco.2014.04.017
Sepasgozar, S., Karimi, R., Farahzadi, L., Moezzi, F., Shirowzhan, S., Ebrahimzadeh, S. M., Hui, F., & Aye, L. (2020). A systematic content review of artificial intelligence and the Internet of Things applications in smart home. Applied Sciences, 10(9), 3074. https://doi.org/10.3390/APP10093074
Paredes-Valverde, M. A., Alor-Hernández, G., García-Alcaráz, J. L., Salas-Zárate, M. del P., Colombo-Mendoza, L. O., & Sánchez-Cervantes, J. L. (2020). IntelliHome: An Internet of Things-based system for electrical energy saving in smart home environment. Computational Intelligence, 36(1), 203–224. https://doi.org/10.1111/coin.12252
Ehsanifar, M., Dekamini, F., Spulbar, C., Birau, R., Khazaei, M., & Bărbăcioru, I. C. (2023). A sustainable pattern of waste management and energy efficiency in smart homes using the Internet of Things (IoT). Sustainability, 15(6), 5081. https://doi.org/10.3390/su15065081
Taiwo, O., & Ezugwu, A. E. (2021). Internet of Things-based intelligent smart home control system. Security and Communication Networks, 2021, 9928254. https://doi.org/10.1155/2021/9928254
Ayan, O., & Turkay, B. (2020). IoT-based energy efficiency in smart homes by smart lighting solutions. In Proceedings of the 21st International Symposium on Electrical Apparatus and Technologies (SIELA). https://doi.org/10.1109/SIELA49118.2020.9167065
Hasan, M. K., Ahmed, M. M., Pandey, B., Gohel, H., Islam, S., & Khalid, I. F. (2021). Internet of Things-based smart electricity monitoring and control system using usage data. Wireless Communications and Mobile Computing, 2021, 6544649. https://doi.org/10.1155/2021/6544649
Hafeez, G., Wadud, Z., Khan, I. U., Khan, I., Shafiq, Z., Usman, M., & Khan, M. U. A. (2020). Efficient energy management of IoT-enabled smart homes under price-based demand response program in smart grid. Sensors, 20(11), 3155. https://doi.org/10.3390/s20113155
Machorro-Cano, I., Alor-Hernández, G., Paredes-Valverde, M. A., Rodríguez-Mazahua, L., Sánchez-Cervantes, J. L., & Olmedo-Aguirre, J. O. (2020). HEMS-IoT: A big data and machine learning-based smart home system for energy saving. Energies, 13(5), 1097. https://doi.org/10.3390/en13051097
Alowaidi, M. (2022). Fuzzy efficient energy algorithm in smart home environment using Internet of Things for renewable energy resources. Energy Reports, 8, 2462–2471. https://doi.org/10.1016/j.egyr.2022.01.177
Kaur, H., Singh, S. P., Bhatnagar, S., & Solanki, A. (2021). Intelligent smart home energy efficiency model using artificial intelligence and Internet of Things. In Artificial intelligence to solve pervasive Internet of Things issues (pp. 183–210). https://doi.org/10.1016/B978-0-12-818576-6.00010-1
Wang, D., Zhong, D., & Souri, A. (2021). Energy management solutions in the Internet of Things applications: Technical analysis and new research directions. Cognitive Systems Research, 67, 33–49. https://doi.org/10.1016/j.cogsys.2020.12.009
Wang, X., Mao, X., & Khodaei, H. (2021). A multi-objective home energy management system based on Internet of Things and optimization algorithms. Journal of Building Engineering, 33, 101603. https://doi.org/10.1016/j.jobe.2020.101603
Saleem, M. U., Shakir, M., Usman, M. R., Bajwa, M. H. T., Shabbir, N., Shams Ghafaroki, P., & Daniel, K. (2023). Integrating smart energy management system with Internet of Things and cloud computing for efficient demand side management in smart grids. Energies, 16(12), 4835. https://doi.org/10.3390/en16124835
Shafik, W., Matinkhah, S. M., & Ghasemzadeh, M. (2020). Internet of Things-based energy management, challenges, and solutions in smart cities. Journal of Communications Technology, Electronics and Computer Science, 27, 1–11. https://doi.org/10.22385/JCTECS.V27I0.302
Chenguang, L. (2023). An efficient vision-based approach for optimizing energy consumption in Internet of Things and smart homes. International Journal of Advanced Computer Science and Applications, 14(6). https://www.ijacsa.thesai.org.
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