Digital Transformation in the Energy Sector Using Internet of Things Artificial Intelligence and Machine Learning Solutions
DOI:
https://doi.org/10.29304/jqcsm.2026.18.32915Keywords:
Digital transformation, Machine Learning (ML), Artificial Intelligence (AI), Internet of Things (IoT), Smart Grid (SG)Abstract
To increase energy efficiency. reduce losses . support the integration of renewable energy sources , and improve the performance of smart grids, the digital transformation of the energy sector is essential . This research paper examines the role of machine learning (ML), artificial intelligence (AI), and the Internet of Things (IoT) in accelerating the digital transformation of smart energy systems. The study explores how IoT-enhanced monitoring and sensor-based automation can enhance the accuracy of machine learning-based decisions in smart grids and smart meters. The study employed a questionnaire-based research methodology , distributing data to 113 participants from the energy sector . SPSS software and structural equation modeling (SEM) were used for data analysis. The results showed good agreement with factor analysis, with a Kaiser-Meier-Olkin (KMO) index of 0.894 and a statistically significant Bartlett test result . The survey also demonstrated very good internal reliability with a Cornbrash’s alpha coefficient of )0.943 ( The structural model results showed that enabling factors had the greatest impact on the behavioral intention to adopt smart energy technologies (FC → BI = 0.791) followed by effort expectations (EE → BI = 0.661) then performance expectations (PE → BI = 0.617) and at last social influence (SI → BI = 0.457) . The success of implementing the Internet of Things and artificial intelligence in the energy sector depends not only on technical work but also on user support and infrastructure readiness, as clearly demonstrated by the research findings. By linking technology acceptance factors with smart energy technologies. this research offers practical applications for smart grid developers and organizations working to accelerate digital transformation .
Downloads
References
Munirathinam, S. Industry 4.0: Industrial Internet of Things (I.I.O.T.). In Computer Advances; Elsevier: Amsterdam, The Netherlands, 2020; pp. 129–164.
Bhatia, M.S.; Kumar, S. Critical success factors of Industry 4.0 in automotive manufacturing. IEEE Trans. Eng. Manag. 2020, 69, 2439–2453.
Eini, R.; Linkous, L.; Zohrabi, N.; Abdelwahed, S. Smart building management system: Performance specifications and design requirements. J. Build. Eng. 2021, 39, 102222.
Minoli, D.; Occhiogrosso, B. IoT-Driven Advances in Commercial and Industrial Building Lighting. In Industrial IoT; Springer: Berlin/Heidelberg, Germany, 2020; pp. 97–159.
Ahsan, M.; Based, M.A.; Haider, J.; Rodrigues, E.M. Smart monitoring and controlling appliances using LoRa-based IoT system. Designs 2021, 5, 17.
Awotunde, J.B.; Ogundokun, R.; Misra, S. Cloud and IoMT-based extensive data analytics system during COVID-19 pandemic. In Efficient Data Handling for Massive Internet of Medical Things; Springer: Berlin/Heidelberg, Germany, 2021; pp. 181–201.
Daissaoui, A.; Boulmakoul, A.; Karim, L.; Lbath, A. IoT and big data analytics for smart buildings: A survey. Procedia Comput. Sci. 2020, 170, 161–168.
Mofidi, F.; Akbari, H. Intelligent buildings: An overview. Energy Build. 2020, 223, 110192.
Lv, Z.; Qiao, L.; Kumar Singh, A.; Wang, Q. AI-empowered IoT security for smart cities. ACM Trans. Internet Technol. 2021, 21, 1–21.
Aguilar, J.; Ardila, D.; Avendaño, A.; Macias, F.; White, C.; Gomez-Pulido, J.; Garces-Jimenez, A. An autonomic cycle of data analysis tasks for the supervision of HVAC systems of smart building. Energies 2020, 13, 3103.
Saini, K.; Kalra, S.; Sood, S. Disaster emergency response framework for smart buildings. Future Gener. Comput. Syst. 2022, 131, 106–120.
Shih, C.-S.; Chou, J.-J.; Lin, K.-J. WuKong: Secure Run-Time environment and data-driven IoT applications for Smart Cities and Smart Buildings. J. Internet Serv. Inf. Secur. 2018, 8, 1–17.
Dong, B.; Prakash, V.; Feng, F.; O'Neill, Z. A review of smart building sensing system for better indoor environment control. Energy Build. 2019, 199, 29–46.
Benavente-Peces, C. On the energy efficiency in the next generation of smart buildings—Supporting technologies and techniques. Energies 2019, 12, 4399.
Asaad, S.M.; Maghdid, H. A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives. Comput. Netw. 2022, 212, 109041.
Molzahn, D.K.; Dörfler, F.; Sandberg, H.; Low, S.H.; Chakrabarti, S.; Baldick, R.; Lavaei, J. A survey of distributed optimization and control algorithms for electric power systems. IEEE Trans. Smart Grid 2017, 8, 2941–2962.
León-Trigo, L.I.; Reyes-Archundia, E.; Gutiérrez-Gnecchi, J.A.; Méndez-Patiño, A.; Chávez-Campos, G.M. Smart Grids en México: Situación actual, retos y propuesta de implementación. Ing. Investig. Tecnol. 2019, 20.
Farzaneh, H.; Malehmirchegini, L.; Bejan, A.; Afolabi, T.; Mulumba, A.; Daka, P.P. Artificial intelligence evolution in smart buildings for energy efficiency. Appl. Sci. 2021, 11, 763.
Li, J.; Wang, F. Non-technical loss detection in power grids with statistical profile images based on semi-supervised learning. Sensors 2019, 20, 236.
Yang, Z.; Shi, Z.; Jin, C. SACRB-MAC: A high-capacity M.A.C. protocol for cognitive radio sensor networks in smart Grid. Sensors 2016, 16, 464.
Zhong, W.; Xie, K.; Liu, Y.; Yang, C.; Xie, S.; Zhang, Y. Online control and near-optimal algorithm for distributed energy storage sharing in smart grid. IEEE Trans. Smart Grid 2019, 11, 2552–2562.
Dharmadhikari, S.C.; Gampala, V.; Rao, C.M.; Khasim, S.; Jain, S.; Bhaskaran, R. A smart grid incorporated with ML and IoT for a security management system. Microprocess. Microsyst. 2021, 83, 103954.
Si, C.; Xu, S.; Wan, C.; Chen, D.; Cui, W.; Zhao, J. Electric load clustering in smart Grid: Methodologies, applications, and future trends. J. Mod. Power Syst. Clean Energy 2021, 9, 237–252.
Astill, J.; Dara, R.A.; Fraser, E.D.; Roberts, B.; Sharif, S. Smart poultry management: Smart sensors, big data, and the IoT. Comput. Electron. Agric. 2020, 170, 105291.
Aliero, M.S.; Asif, M.; Ghani, I.; Pasha, M.F.; Jeong, S.R. Systematic Review Analysis on Smart Building: Challenges and Opportunities. Sustainability 2022, 14, 3009.
Sierra, S.; Ihasalo, H.; Vyatkin, V. A Review of Reinforcement Learning Applications to Control of Heating, Ventilation, and Air Conditioning Systems. Energies 2022, 15, 3526.
Ahmad, T.; Madonski, R.; Zhang, D.; Huang, C.; Mujeeb, A. Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renew. Sustain. Energy Rev. 2022, 160, 112128.
Zhang, H.; Feng, H.; Hewage, K.; Arashpour, M. Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework. Buildings 2022, 12, 829.
Demirezen, G.; Fung, A.; Deprez, M. Development and optimization of artificial neural network algorithms for the prediction of building specific local temperature for HVAC control. Int. J. Energy Res. 2020, 44, 8513–8531.
Sammak, S. Using Artificial Intelligence in Renewable Energies. Energy 2021, 2.
Mazhar, T.; Malik, M.A.; Haq, I.; Rozeela, I.; Ullah, I.; Khan, M.A.; Adhikari, D.; Ben Othman, M.T.; Hamam, H. The Role of ML, AI and 5G Technology in Smart Energy and Smart Building Management. Electronics 2022, 11, 3960.
Gupta, D.; Juneja, S.; Nauman, A.; Hamid, Y.; Ullah, I.; Kim, T.; Tag eldin, E.M.; Ghamry, N.A. Energy Saving Implementation in Hydraulic Press Using Industrial Internet of Things (IIoT). Electronics 2022, 11, 4061.
Khan, R.; Yang, Q.; Ullah, I.; Rehman, A.U.; Tufail, A.B.; Noor, A.; Rehman, A.; Cengiz, K. 3D convolutional neural networks based automatic modulation classification in the presence of channel noise. IET Commun. 2022, 16, 497–509.
Raza, M.; Barket, A.R.; Rehman, A.U.; Rehman, A.; Ullah, I. Mobile crowdsensing based architecture for intelligent traffic prediction and quickest path selection. In Proceedings of the 2020 International Conference on UK-China Emerging Technologies (UCET), Glasgow, UK, 20–21 August 2020; pp. 1–4.
Muralitharan, K.; Sakthivel, R.; Vishnuvarthan, R. Neural network-based optimization approach for energy demand prediction in smart Grid. Neurocomputing 2018, 273, 199–208.
A. R. Singh, R. S. Kumar, M. Bajaj, C. B. Khadse, and I. Zaitsev, 'Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources,' Scientific Reports, vol. 14, art. 19207, 2024, doi: 10.1038/s41598-024-70336-3.
Rashid, K.M.; Louis, J.; Fiawoyife, K. Wireless electric appliance control for smart buildings using indoor location tracking and BIM-based virtual environments. Autom. Constr. 2019, 101, 48–58.
Djenouri, D.; Laidi, R.; Djenouri, Y.; Balasingham, I. Machine learning for smart building applications: Review and taxonomy. ACM Comput. Surv. 2019, 52, 1–36.
Li, R.; Zhang, X.; Liu, L.; Li, Y.; Xu, Q. Application of neural network to building environmental prediction and control. Build. Serv. Eng. Res. Technol. 2020, 41, 25–45.
Zhao, Y.; Du, X.; Xia, G.; Wu, L. A novel algorithm for wavelet neural networks with application to enhanced P.I.D. controller design. Neurocomputing 2015, 158, 257–267.
Alkhatib, H.; Lemarchand, P.; Norton, B.; O'Sullivan DT, J. Deployment and control of adaptive building facades for energy generation, thermal insulation, ventilation and daylighting: A review. Appl. Therm. Eng. 2021, 185, 116331.
Aste, N.; Manfred, M.; Marenzi, G. Building Automation and Control Systems and performance optimization: A framework for analysis. Renew. Sustain. Energy Rev. 2017, 75, 313–330.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Rana Talib Hakim
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
