Pathways to a Data-driven Green Future for Building Integrated, Intelligent Digital Sustainability
Abstract
The global energy sector is in the process of making a dual transition towards decarbonization and digitalization. This paper discusses how the fundamental digital technologies, Internet of Things (IoT), Artificial Intelligence (AI), machine learning, digital twins, edge computing, and blockchain facilitate environmental objectives in the energy sector by enhancing efficiency, integrating variable renewables, lowering emissions, and enhancing system resilience. Studies are reviewed to evaluate five pillars of applications, (i) integration of renewable energy through high‑fidelity forecasting, smart inverters, and AI‑aided dispatch, (ii) smart grids that employ ubiquitous sensing, automation, and edge intelligence for real‑time stability, demand response, and losses reduction, (iii) end‑use energy efficiency in buildings and industry through data‑driven controls and digital twin-based optimization, (iv) predictive maintenance of generation and network assets via condition monitoring and fault‑prediction models to reduce downtime and resource waste, and (v) emissions tracking and carbon management through IoT‑enabled monitoring, AI analytics, and blockchain‑backed certificates and markets. Observed benefits encompass double digit gains in energy efficiency, increased renewable penetration without reliability loss, quantifiable decreases in curtailment and peaking demand, and enhanced transparency in carbon accounting. Constrains such as cybersecurity, data privacy, interoperability, worker skills, and the energy profile of digital infrastructure are assessed with mitigation techniques such as privacy-preserving analytics, standard adoption, edge processing, and low-energy consensus mechanisms. Looking ahead, development in AI, IoT, digital-twin grids, 5G/6G‑facilitated edge coordination, sector coupling, and trusted decentralized markets will bring increasingly autonomous, adaptive, and verifiably low-carbon energy systems. Digitalization thereby presents itself as a catalyst and control layer for realizing scalable, equitable, and resilient decarbonization.
Keywords: Digital Sustainability; Green Energy Transition; Decarbonization; Internet of Things (IoT); Artificial Intelligence (AI); Digital Twin Technology; Energy Efficiency; Predictive.
References
- Agrawal, D. (2025). A comprehensive review of investing and financing for sustainable tourism projects. Environment, Sustainability, and Governance Insights, 1(1), 53-70.
- Ahsan, F., Dana, N. H., Sarker, S. K., Li, L., Muyeen, S. M., Ali, M. F., ... & Das, P. (2023). Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review. Protection and Control of Modern Power Systems, 8(3), 1-42.
- Alam, M. S., Al-Ismail, F. S., Salem, A., & Abido, M. A. (2020). High-level penetration of renewable energy sources into grid utility: Challenges and solutions. IEEE access, 8, 190277-190299.
- Arévalo, P., & Jurado, F. (2024). Impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. Energies, 17(17), 4501.
- Attico, N. (2020). Blockchain ecosystem. Driving mass adoption. goWare & Guerini Next.
- Benti, N. E., Chaka, M. D., & Semie, A. G. (2023). Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects. Sustainability, 15(9), 7087.
- Bergero, C., Gosnell, G., Gielen, D., Kang, S., Bazilian, M., & Davis, S. J. (2023). Pathways to net-zero emissions from aviation. Nature Sustainability, 6(4), 404-414.
- Bibri, S. E., & Krogstie, J. (2020). Environmentally data-driven smart sustainable cities: Applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism. Energy Informatics, 3(1), 29.
- Biswal, C., Sahu, B. K., Mishra, M., & Rout, P. K. (2023). Real-time grid monitoring and protection: A comprehensive survey on the advantages of phasor measurement units. Energies, 16(10), 4054.
- Byrne, N., Pierce, S., De Donatis, L., Kerrigan, R., & Buckley, N. (2024). Validating decarbonisation strategies of climate action plans via digital twins: a Limerick case study. Frontiers in Sustainable Cities, 6, 1393798.
- Castro, V., Georgiou, M., Jackson, T., Hodgkinson, I. R., Jackson, L., & Lockwood, S. (2024). Digital data demand and renewable energy limits: Forecasting the impacts on global electricity supply and sustainability. Energy Policy, 195, 114404.
- Cavalieri, S. (2021). Semantic interoperability between IEC 61850 and oneM2M for IoT-enabled smart grids. Sensors, 21(7), 2571.
- Chai, Z. Y., Shah, S. A., Draheim, D., Hameed, S., & Rathore, M. M. U. (2024). Guest Editorial: Smart cities 2.0: How Artificial Intelligence and Internet of Things are transforming urban living. IET Smart Cities, 6(3), 129-131.
- Citaristi, I. (2022). International energy agency—iea. In The Europa directory of international organizations 2022 (pp. 701-702). Routledge.
- Erdödy, N., O’Keefe, R., & Yule, I. (2023, September). What Does a Nation-Wide Digital Nervous System Use for an Operating System?. In Latin American High Performance Computing Conference (pp. 160-169). Cham: Springer Nature Switzerland.
- Fouad, M. M., Shihata, L. A., & Morgan, E. I. (2017). An integrated review of factors influencing the perfomance of photovoltaic panels. Renewable and Sustainable Energy Reviews, 80, 1499-1511.
- Ghulam, S. T., & Abushammala, H. (2023). Challenges and opportunities in the management of electronic waste and its impact on human health and environment. Sustainability, 15(3), 1837.
- Goudarzi, A., Ghayoor, F., Waseem, M., Fahad, S., & Traore, I. (2022). A survey on IoT-enabled smart grids: emerging, applications, challenges, and outlook. Energies, 15(19), 6984.
- Joselin, D., Singh, A., & Tiwary, K. S. (2025). The Nexus Between CSR Practices and Financial Performance: Empirical Evidence from the Manufacturing Sector in India. Environment, Sustainability, and Governance Insights, 1(1), 1-17.
- Kabeyi, M. J. B., & Olanrewaju, O. A. (2023). Smart grid technologies and application in the sustainable energy transition: a review. International Journal of Sustainable Energy, 42(1), 685-758.
- Khalid, M. (2024). Energy 4.0: AI-enabled digital transformation for sustainable power networks. Computers & Industrial Engineering, 193, 110253.
- Khan, M. A. (2025). AI And Machine Learning in Transformer Fault Diagnosis: A Systematic Review. SSRN 10.63125/sxb17553
- Kimani, K., Oduol, V., & Langat, K. (2019). Cyber security challenges for IoT-based smart grid networks. International journal of critical infrastructure protection, 25, 36-49.
- Kommineni, M., & Chundru, S. (2025). Sustainable Data Governance Implementing Energy-Efficient Data Lifecycle Management in Enterprise Systems. In Driving Business Success Through Eco-Friendly Strategies (pp. 397-418). IGI Global Scientific Publishing.
- Labeodan, T. M., De Bakker, C., Rosemann, A. L. P., & Zeiler, W. (2016). On the application of wireless sensors and actuators network in existing buildings for occupancy detection and occupancy-driven lighting control. Energy and Buildings, 127, 75-83.
- Lehtola, T., & Zahedi, A. (2019). Solar energy and wind power supply supported by storage technology: A review. Sustainable Energy Technologies and Assessments, 35, 25-31.
- Li, J., & Sun, C. (2018). Towards a low carbon economy by removing fossil fuel subsidies?. China Economic Review, 50, 17-33.
- Liu, Z., Feng, K., Davis, S. J., Guan, D., Chen, B., Hubacek, K., & Yan, J. (2016). Understanding the energy consumption and greenhouse gas emissions and the implication for achieving climate change mitigation targets. Applied Energy, 184, 737-741.
- Martinot, E. (2016). Grid integration of renewable energy: flexibility, innovation, and experience. Annual Review of Environment and Resources, 41(1), 223-251.
- Meng, X. L. (2024). Data Democratization: An Ecosystemic Contemplation and Coordination. Harvard Data Science Review, (Special Issue 4).
- Mortaji, H., Ow, S. H., Moghavvemi, M., & Almurib, H. A. F. (2017). Load shedding and smart-direct load control using internet of things in smart grid demand response management. IEEE Transactions on Industry Applications, 53(6), 5155-5163.
- Mutambik, I., Lee, J., Almuqrin, A., & Zhang, J. Z. (2023). Transitioning to smart cities in Gulf Cooperation Council countries: The role of leadership and organisational culture. Sustainability, 15(13), 10490.
- Newlands, G., Lutz, C., Tamò-Larrieux, A., Villaronga, E. F., Harasgama, R., & Scheitlin, G. (2020). Innovation under pressure: Implications for data privacy during the Covid-19 pandemic. Big Data & Society, 7(2), 2053951720976680.
- Prakash, A., & Tiwari, A. K. (2025). The role of institutional factors in achieving SDG 16: A thematic review of drivers of corporate anti-corruption action. Environment, Sustainability, and Governance Insights, 1(1), 34-52.
- Rehman, A. U., Wadud, Z., Elavarasan, R. M., Hafeez, G., Khan, I., Shafiq, Z., & Alhelou, H. H. (2021). An optimal power usage scheduling in smart grid integrated with renewable energy sources for energy management. IEEE Access, 9, 84619-84638.
- Ricciardi Celsi, M., & Ricciardi Celsi, L. (2024). Quantum computing as a game changer on the path towards a net-zero economy: A review of the main challenges in the energy domain. Energies, 17(5), 1039.
- Ringenson, T., Höjer, M., Kramers, A., & Viggedal, A. (2018). Digitalization and environmental aims in municipalities. Sustainability, 10(4), 1278.
- Saad, M., Qin, Z., Ren, K., Nyang, D., & Mohaisen, D. (2021). e-PoS: Making proof-of-stake decentralized and fair. IEEE Transactions on Parallel and Distributed Systems, 32(8), 1961-1973.
- Sarker, E., Halder, P., Seyedmahmoudian, M., Jamei, E., Horan, B., Mekhilef, S., & Stojcevski, A. (2021). Progress on the demand side management in smart grid and optimization approaches. International Journal of Energy Research, 45(1), 36-64.
- Sharma, H., Kumar, P., & Sharma, K. (2025). Advanced Security for IoT and Smart Devices: Addressing Modern Threats and Solutions. Emerging Threats and Countermeasures in Cybersecurity, 191-216.
- Singh, R., Kumar, K., & Khan, S. (2024). A comprehensive view of artificial intelligence (AI)–based technologies for sustainable development goals (SDGs). Artificial intelligence enabled management: an emerging economy perspective, 183, 9783111172408-012.
- Singh, R., Tiwary, K. S., Kumar, K., & Kumar, A. (2025). Empowering a Sustainable Future: Blockchain’s Role in Economy, Organizations, and Finance Towards Smart Circular Economy. In Blockchain Technologies for Smart Circular Economy and Organisational Sustainability (pp. 271-283). Cham: Springer Nature Switzerland.
- Talaat, F. M. (2025). Revolutionizing cardiovascular health: integrating deep learning techniques for predictive analysis of personal key indicators in heart disease. Neural Computing and Applications, 37(1), 1-24.
- Waqar, A., Barakat, T.A.H., Almujibah, H.R. et al. Analytical approach to smart and sustainable city development with IoT. Sci Rep 15, 23617 (2025). https://doi.org/10.1038/s41598-025-08861-y
- Zanella, A., Mason, F., Pluchino, P., Cisotto, G., Orso, V., & Gamberini, L. (2020). Internet of things for elderly and fragile people. arXiv preprint arXiv:2006.05709
