Impact of IoT-Based Energy Management Systems on Energy Use in Urban Commercial Buildings

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Sofa Sofiana
Alvino Octaviano

Abstract

The integration of Internet of Things (IoT) technologies into energy management systems has garnered significant attention due to its potential to optimize energy usage in urban environments. Despite the growing adoption of IoT in building management, there remains a lack of experimental research that rigorously tests the causal impact of IoT-based systems on energy consumption in real-world settings. This study aims to fill this gap by experimentally evaluating the effect of IoT-based energy management systems on energy efficiency in urban commercial buildings. An experimental study was conducted involving [N] urban commercial buildings, with [N1] assigned to the experimental group and [N2] to the control group, over a period of [X] months. Our experimental design uniquely combines controlled conditions and real-time data monitoring to assess the causal relationship between IoT system implementation and energy savings. We conducted an experimental study with both an experimental group, exposed to the IoT-based system, and a control group. The results showed a significant reduction in energy consumption in the experimental group, confirming the hypothesis that IoT systems can improve energy efficiency. The experimental design allowed for a precise isolation of the effects of IoT, ensuring the validity of the findings. These findings not only advance our understanding of the impact of IoT on energy consumption but also provide practical insights for the implementation of smart energy management systems in various sectors. The results are relevant for policymakers and building managers, offering evidence-based guidance for adopting IoT technologies to optimize energy usage in urban infrastructure.

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References

Alijoyo, F. A. (2024). AI-powered deep learning for sustainable industry 4.0 and internet of things: Enhancing energy management in smart buildings. Alexandria Engineering Journal, 104, 409–422. Scopus. https://doi.org/10.1016/j.aej.2024.07.110

Balasubramanian, C., & Ravi Singh, R. (2024). IOT based energy management in smart grid under price based demand response based on hybrid FHO-RERNN approach. Applied Energy, 361. Scopus. https://doi.org/10.1016/j.apenergy.2024.122851

Bebortta, S., Singh, A. K., Pati, B., & Senapati, D. (2021). A Robust Energy Optimization and Data Reduction Scheme for IoT Based Indoor Environments Using Local Processing Framework. Journal of Network and Systems Management, 29(1). Scopus. https://doi.org/10.1007/s10922-020-09572-7

Boiko, O., Komin, A., Malekian, R., & Davidsson, P. (2024). Edge-Cloud Architectures for Hybrid Energy Management Systems: A Comprehensive Review. IEEE Sensors Journal, 24(10), 15748–15772. Scopus. https://doi.org/10.1109/JSEN.2024.3382390

Bui, V., Le, N. T., van Hoa, V. H., Kim, J., & Jang, Y. M. (2021). Multi-behavior with bottleneck features lstm for load forecasting in building energy management system. Electronics (Switzerland), 10(9). Scopus. https://doi.org/10.3390/electronics10091026

Condon, F., Martínez, J. M., Eltamaly, A. M., Kim, Y.-C., & Ahmed, M. A. (2023). Design and Implementation of a Cloud-IoT-Based Home Energy Management System. Sensors, 23(1). Scopus. https://doi.org/10.3390/s23010176

Gupta, R., Al-Ali, A. R., Zualkernan, I. A., & Das, S. K. (2020). Big Data Energy Management, Analytics and Visualization for Residential Areas. IEEE Access, 8, 156153–156164. Scopus. https://doi.org/10.1109/ACCESS.2020.3019331

Hashmi, S. A., Ali, C. F., & Zafar, S. (2021). Internet of things and cloud computing-based energy management system for demand side management in smart grid. International Journal of Energy Research, 45(1), 1007–1022. Scopus. https://doi.org/10.1002/er.6141

Huang, Z., & Jin, G. (2024). Navigating urban day-ahead energy management considering climate change toward using IoT enabled machine learning technique: Toward future sustainable urban. Sustainable Cities and Society, 101. Scopus. https://doi.org/10.1016/j.scs.2023.105162

Karuna, K., Poornima, P., S, A., P, A., T, S., Mittal, A., Rajvanshi, S., & Habelalmateen, M. I. (2024). Smart energy management: Real-time prediction and optimization for IoT-enabled smart homes. Cogent Engineering, 11(1). Scopus. https://doi.org/10.1080/23311916.2024.2390674

Kuthadi, V. M., Selvaraj, R., Baskar, S., Pethuraj, P. M., & Ranjan, A. (2022). Optimized Energy Management Model on Data Distributing Framework of Wireless Sensor Network in IoT System. Wireless Personal Communications, 127(2), 1377–1403. Scopus. https://doi.org/10.1007/s11277-021-08583-0

Ma, S., Ding, W., Liu, Y., Ren, S., & Yang, H. (2022). Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries. Applied Energy, 326. Scopus. https://doi.org/10.1016/j.apenergy.2022.119986

Marinakis, V., Doukas, H., Tsapelas, J., Mouzakitis, S., Sicilia, Á., Madrazo, L., & Sgouridis, S. (2020). From big data to smart energy services: An application for intelligent energy management. Future Generation Computer Systems, 110, 572–586. Scopus. https://doi.org/10.1016/j.future.2018.04.062

Márquez, S., Calvo-Gallego, J., Erbad, A., Ibrar, M., Hernandez Fernandez, J. H., Houchati, M., & Corchado Rodríguez, J. M. (2023). Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort †. Electronics (Switzerland), 12(19). Scopus. https://doi.org/10.3390/electronics12194179

Mohammad, F., Ahmed, M. A., & Kim, Y.-C. (2021). Efficient energy management based on convolutional long short‐term memory network for smart power distribution system. Energies, 14(19). Scopus. https://doi.org/10.3390/en14196161

Mukhlis, L. (2025a). A Phenomenological Study of Personal Spiritual Experiences in Navigating Religious Pluralism within Interfaith Communities. Irfana: Journal of Religious Studies, 1(6), 212–220.

Mukhlis, L. (2025b). Spiritual Grounds for Economic Growth: A Qualitative Exploration of Rural Indonesian Women’s Transformative Journeys Through Mosque-Led Empowerment Programs. Servina: Jurnal Pengabdian Kepada Masyarakat, 1(8), 289–298.

Mukhlis, L., & Abdullah, M. N. (2025). Hukum Keluarga Islam di Indonesia (1st ed.). Mukhlisina Revolution Center.

Mukhlis, L., Arifin, T., Ridwan, A. H., & Zulbaidah. (2024). Integrating Artificial Intelligenceand Maqāṣid al-Syarī‘ah: Revolutionizing Indonesia’s Sharia Online Trading System. Computer Fraud and Security, 2024(11), 301–309. https://doi.org/10.52710/cfs.238

Mukhlis, L., Arifin, T., Ridwan, A. H., & Zulbaidah. (2025). Reorientation of Sharia Stock Regulations: Integrating Taṣarrufāt al-Rasūl and Maqāṣid al-Sharī‘ah for Justice and Sustainability. Journal of Information Systems Engineering and Management, 10(10s), 58–66. https://doi.org/10.52783/jisem.v10i10s.1341

Mukhlis, L., Arifin, T., Ridwan, A. H., Zulbaidah, Rosadi, A., & Solehudin, E. (2025). Reformulation of Islamic Stock Law: The Application of Taṣarrufāt al-Rasūl and Maqāṣid al-Syarī‘ahto Develop a Dynamic and Sustainable Islamic Capital Market in Indonesia. Journal of Posthumanism, 5(3), 1–13. https://doi.org/10.63332/joph.v5i3.913

Mukhlis, L., Janwari, Y., & Syafe`i, R. (2023). INDONESIA STOCK EXCHANGE: THEORETICAL AND PHILOSOPHICAL ANALYSIS OF MUDHARABAH AND MUSYARAKAH CONTRACTS. Yurisprudentia: Jurnal Hukum Ekonomi, 9(2), 243–264. https://doi.org/10.24952/yurisprudentia.v9i2.8466

Mukhlis, L., Maryam, S., & Sormin, S. A. (2023). Model Pembelajaran Living History Berbasis PjBL Untuk Meningkatkan Keterampilan Histografi Mahasiswa. Jurnal Educatio FKIP UNMA, 9(4), 1800–1809. https://doi.org/10.31949/educatio.v9i4.5595

Mukhlis, L., & Saidah, Y. (2025). Dynamics of Nature-Based learning in Developing Children’s Motoricic Skills: Teacher and Parent Perspectives. HUMANISMA: Journal of Gender Studies, 9(1), 64–79. http://dx.doi.org/10.30983/humanisme.v4i2.9366

Mukhlis, L., Suradi, Janwari, Y., & Syafe`i, R. (2023). Sosialisasi Saham Syariah sebagai Instrumen Pengembangan Ekonomi Masyarakat di Badan Kontak Majelis Taklim (BKMT) Kabupaten Mandailing Natal. Jurnal Pengabdian Multidisiplin, 3(2), 2–9. https://doi.org/10.51214/japamul.v3i2.604

Munoz, O., Ruelas, A., Rosales-Escobedo, P., Ramírez, A., Suastegui, A., & Lara, F. (2022). Design and Development of an IoT Smart Meter with Load Control for Home Energy Management Systems. Sensors, 22(19). Scopus. https://doi.org/10.3390/s22197536

Ntafalias, A., Tsakanikas, S., Skarvelis-Kazakos, S., Papadopoulos, P., Skarmeta-Gómez, A. F., González-Vidal, A., Tomat, V., Ramallo-González, A. P., Marín-Pérez, R., & Vlachou, M. C. (2022). Design and Implementation of an Interoperable Architecture for Integrating Building Legacy Systems into Scalable Energy Management Systems. Smart Cities, 5(4), 1421–1440. Scopus. https://doi.org/10.3390/smartcities5040073

Pandiyan, P., Saravanan, S., Usha, K., Raju, R., Alsharif, M. H., & Kim, M.-K. (2023). Technological advancements toward smart energy management in smart cities. Energy Reports, 10, 648–677. Scopus. https://doi.org/10.1016/j.egyr.2023.07.021

Rao, C., Sahoo, S. K., & Yanine, F. F. (2024). IoT enabled Intelligent Energy Management System employing advanced forecasting algorithms and load optimization strategies to enhance renewable energy generation. Unconventional Resources, 4. Scopus. https://doi.org/10.1016/j.uncres.2024.100101

Sadeeq, M. A., & Zeebaree, S. R. (2021). Energy Management for Internet of Things via Distributed Systems. Journal of Applied Science and Technology Trends, 2(2), 80–92. Scopus. https://doi.org/10.38094/jastt20285

Sadeeq, M. A., & Zeebaree, S. R. (2023). Design and implementation of an energy management system based on distributed IoT. Computers and Electrical Engineering, 109. Scopus. https://doi.org/10.1016/j.compeleceng.2023.108775

Said, O., Almakhadmeh, Z., & Tolba, A. (2020). EMS: An Energy Management Scheme for Green IoT Environments. IEEE Access, 8, 44983–44998. Scopus. https://doi.org/10.1109/ACCESS.2020.2976641

Saleem, M. U., Shakir, M., Usman, M. R., Bajwa, M. H. T., Shabbir, N., Shams Ghahfarokhi, 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). Scopus. https://doi.org/10.3390/en16124835

Saleem, M. U., Usman, M. R., & Shakir, M. (2021). Design, Implementation, and Deployment of an IoT Based Smart Energy Management System. IEEE Access, 9, 59649–59664. Scopus. https://doi.org/10.1109/ACCESS.2021.3070960

Saleem, M. U., Usman, M. R., Usman, M. A., & Politis, C. (2022). Design, Deployment and Performance Evaluation of an IoT Based Smart Energy Management System for Demand Side Management in Smart Grid. IEEE Access, 10, 15261–15278. Scopus. https://doi.org/10.1109/ACCESS.2022.3147484

Sedhom, B. E., El-Saadawi, M. M., El Moursi, M. S., Hassan, M. A., & Eladl, A. A. (2021). IoT-based optimal demand side management and control scheme for smart microgrid. International Journal of Electrical Power and Energy Systems, 127. Scopus. https://doi.org/10.1016/j.ijepes.2020.106674

Sheela, M. S., Subburayalu, S., Parvin Begum, I. P., Hephzipah, J. J., Gopianand, M., & Harika, D. (2024). Enhancing Energy Efficiency with Smart Building Energy Management System Using Machine Learning and IOT. Babylonian Journal of Machine Learning, 2024, 80–88. Scopus. https://doi.org/10.58496/BJML/2024/008

Shreenidhi, S., & Ramaiah, N. S. (2022). A two-stage deep convolutional model for demand response energy management system in IoT-enabled smart grid. Sustainable Energy, Grids and Networks, 30. Scopus. https://doi.org/10.1016/j.segan.2022.100630

Ullah, M., Narayanan, A., Wolff, A., & Nardelli, P. H. J. (2022). Industrial Energy Management System: Design of a Conceptual Framework Using IoT and Big Data. IEEE Access, 10, 110557–110567. Scopus. https://doi.org/10.1109/ACCESS.2022.3215167

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. Scopus. https://doi.org/10.1016/j.jobe.2020.101603