A Data-Driven Approach to Smart Home Automation and Energy Efficiency: Integrating IoT Analytics for Sustainable Residential Systems in Morocco
AUTHORS
Abdelali El Aroudi,Abdelmalek Essaâdi University, Tetouan, Morocco
Yassine Maleh,Sultan Moulay Slimane University, Morocco
Mohamed Bakhouya,International University of Rabat, Morocco
ABSTRACT
This work addresses the growing importance of smart home technologies for improving residential energy efficiency and automation, particularly in emerging and developing contexts. Driven by advancements in the Internet of Things (IoT) and artificial intelligence (AI), smart home systems offer significant potential for enhancing operational efficiency, user comfort, and sustainability. However, several challenges persist, including inefficient data utilization, limited adaptability of automation mechanisms, and interoperability constraints among heterogeneous devices. In this context, the present study explores a comprehensive smart home dataset collected over four years to analyze energy consumption patterns and device usage behaviors. While the dataset is not geographically bound, its analytical interpretation is framed in relation to residential energy practices typical of the Moroccan context, where energy efficiency and resource optimization are of growing importance. Based on this analysis, the study proposes a set of adaptive, data-driven automation strategies designed to align system operations with user behavior and dynamic environmental conditions. The findings reveal notable inefficiencies in energy usage, particularly during peak demand periods, as well as the disproportionate contribution of certain appliances to overall consumption. The proposed automation framework demonstrates the potential to reduce energy consumption by approximately 15% while improving system responsiveness and user satisfaction. From a practical perspective, the study provides insights for both technology developers and decision-makers seeking to promote sustainable, scalable smart home solutions. Accordingly, this work contributes to advancing smart home systems by offering a structured, data-driven approach that supports energy optimization, enhances interoperability, and reinforces user-centric design principles within the broader context of sustainable residential development.
KEYWORDS
Internet of Things (IoT), Intelligent residential systems, Sustainable energy management, Data-driven automation, Energy efficiency
REFERENCES
[1] C. K. Rao, S. K. Sahoo, and F. F. Yanine, “A literature review on an IoT-based intelligent smart energy management systems for PV power generation,” Hybrid Advances, vol. 5, pp. 100136, (2024) DOI:10.1016/j.hybadv.2023.100136(CrossRef)(Google Scholar)
[2] A. M. Norouzzadeh, S. P. Toufighi, J. Vang, and A. Edalatipour, “Adoption of internet of things in residential smart homes: A structural equation modeling approach,” Sustainable Futures, vol. 9, pp. 100665, (2025) DOI:10.1016/j.sftr.2025.100665(CrossRef)(Google Scholar)
[3] N. Al-Oudat, A. Aljaafreh, M. Saleh, and M. Alaqtash, “IoT-based home and community energy management system in Jordan,” Procedia Computer Science, vol. 160, pp. 142–148, (2019) DOI:10.1016/j.procs.2019.09.454(CrossRef)(Google Scholar)
[4] M. Poyyamozhi, B. Murugesan, N. Rajamanickam, M. Shorfuzzaman, and Y. Aboelmagd, “IoT—A promising solution to energy management in smart buildings: A systematic review, applications, barriers, and future scope,” Buildings, vol. 14, no. 11, pp. 3446, (2024) DOI:10.3390/buildings14113446(CrossRef)(Google Scholar)
[5] W. Alayed, A. Akhtar, W. Ul Hassan, and A. Zeeshan, “Maximizing energy savings in smart homes through artificial neural network-based artificial intelligence solutions,” Clean Energy, vol. 9, no. 2, pp. 140–149, (2025) DOI:10.1093/ce/zkae113(CrossRef)(Google Scholar)
[6] D. D. Furszyfer Del Rio, B. K. Sovacool, and S. Griffiths, “Culture, energy and climate sustainability, and smart home technologies: A mixed methods comparison of four countries,” Energy and Climate Change, vol. 2, pp. 100035, (2021) DOI:10.1016/j.egycc.2021.100035(CrossRef)(Google Scholar)
[7] Connectivity Standards Alliance, “Matter smart home connectivity standard,” (2025)
[8] A. H. Bagdadee, M. S. Rahman, I. Al Mamoon, D. A. Dewi, A. K. Muzahidul Islam, and L. Zhang, “Empowering smart homes by IoT-driven hybrid renewable energy integration for enhanced efficiency,” Scientific Reports, vol. 15, no. 1, pp. 41491, (2025) DOI:10.1038/s41598-025-25328-2(CrossRef)(Google Scholar)
[9] H. Jo and Y. I. Yoon, “Intelligent smart home energy efficiency model using artificial TensorFlow engine,” Human-centric Computing and Information Sciences, vol. 8, no. 9, pp. 1–12, (2018) DOI:10.1186/s13673-018-0132-y(CrossRef)(Google Scholar)
[10] A. E. Ezugwu, O. Taiwo, O. S. Egwuche, L. Abualigah, A. Van Der Merwe, J. Pal, A. K. Saha, A. I. Alzahrani, F. Alblehai, J. Greeff, and M. O. Olusanya, “Smart homes of the future,” Transactions on Emerging Telecommunications Technologies, vol. 36, no. 1, pp. e70041, (2024) DOI:10.1002/ett.70041(CrossRef)(Google Scholar)
[11] M. R. Alam, M. B. I. Reaz, and M. A. M. Ali, “A review of smart homes—Past, present, and future,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 42, no. 6, pp. 1190–1203, (2012) DOI:10.1109/TSMCC.2012.2189204(CrossRef)(Google Scholar)
[12] M. Umair, M. A. Cheema, B. Afzal, and G. Shah, “Energy management of smart homes over fog-based IoT architecture,” Sustainable Computing: Informatics and Systems, vol. 39, pp. 100898, (2023) DOI:10.1016/j.suscom.2023.100898(CrossRef)(Google Scholar)
[13] “Integrating machine learning techniques for enhanced energy management and sustainability in smart homes,” International Journal of Computations, Information and Manufacturing (IJCIM), vol. 4, no. 1, pp. 54–63, (2024) DOI:10.54489/ijcim.v4i1.396(CrossRef)(Google Scholar)
[14] M. P. Raju, E. S. Sagar, and A. J. Laxmi, “AI powered IoT-based home energy management system towards DG integration,” Procedia Computer Science, vol. 215, pp. 846–855, (2023) DOI:10.1016/j.procs.2022.12.087(CrossRef)(Google Scholar)
[15] M. Razghandi, H. Zhou, M. Erol-Kantarci, and D. Turgut, “Smart home energy management: VAE-GAN synthetic dataset generator and Q-learning,” IEEE Transactions on Smart Grid, vol. 15, no. 2, pp. 1562–1573, (2024) DOI:10.1109/TSG.2023.3288824(CrossRef)(Google Scholar)
[16] A. M. Eltamaly, M. A. Alotaibi, A. I. Alolah, and M. A. Ahmed, “IoT-based hybrid renewable energy system for smart campus,” Sustainability, vol. 13, no. 15, pp. 8555, (2020) DOI:10.3390/su13158555(CrossRef)(Google Scholar)
[17] W. Zegeye, A. Jemal, and K. Kornegay, “Connected smart home over Matter protocol,” Proceedings of IEEE ICCE, pp. 1–7, (2023) DOI:10.1109/ICCE56470.2023.10043520(CrossRef)(Google Scholar)
[18] R. Vadruccio, C. Siragusa, and A. Tumino, “Increasing energy efficiency in smart building through Internet of Things retrofitting intervention,” Procedia Computer Science, vol. 219, pp. 263–270, (2023) DOI:10.1016/j.procs.2023.01.289(CrossRef)(Google Scholar)
[19] D. Bouchabou, S. M. Nguyen, C. Lohr, B. LeDuc, and I. Kanellos, “A survey of human activity recognition in smart homes based on IoT sensors algorithms: taxonomies, challenges, and opportunities with deep learning,” Sensors, vol. 21, no. 18, pp. 6037, (2020) DOI:10.3390/s21186037(CrossRef)(Google Scholar)
[20] A. M. N., K. N., S. K. G., S. C., J. P., and V. V., “IoT-based smart home energy management system (SHEMS) using networking and automation,” Proceedings of ICDSBS, pp. 1–8, (2025) DOI:10.1109/ICDSBS63635.2025.11031693(CrossRef)(Google Scholar)
[21] J. Sievers and T. Blank, “A systematic literature review on data-driven residential and industrial energy management systems,” Energies, vol. 16, no. 4, pp. 1688, (2022) DOI:10.3390/en16041688(CrossRef)(Google Scholar)
[22] A. A. Adebiyi and M. Habyarimana, “Systematic review of optimization methodologies for smart home energy management systems,” Energies, vol. 18, no. 19, pp. 5262, (2024) DOI:10.3390/en18195262(CrossRef)(Google Scholar)
[23] M. Baqer, “Energy-efficient federated learning for Internet of Things: leveraging in-network processing and hierarchical clustering,” Future Internet, vol. 17, no. 1, pp. 4, (2024) DOI:10.3390/fi17010004(CrossRef)(Google Scholar)
[24] N. Iqbal and D. H. Kim, “IoT task management mechanism based on predictive optimization for efficient energy consumption in smart residential buildings,” Energy and Buildings, vol. 257, pp. 111762, (2022) DOI:10.1016/j.enbuild.2021.111762(CrossRef)(Google Scholar)
[25] J. Rey-Jouanchicot, E. Campo, J. Bouraoui, A. Bottaro, N. Vigouroux, and F. Vella, “Adaptation in smart home automation systems: a systematic review of decision-making and interaction,” Internet of Things, vol. 31, pp. 101588, (2025) DOI:10.1016/j.iot.2025.101588(CrossRef)(Google Scholar)