Integrating Smart Energy Monitoring and Energy Efficiency Measures to Reduce Carbon Emissions in Institutional Buildings: An Environmental Science and Sustainable Development Approach
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Purpose of the study: This study aims to evaluate the impact of implementing smart energy monitoring systems on energy efficiency, operational energy consumption, and carbon emissions in institutional buildings in Montenegro. The research seeks to quantify potential energy savings and emission reductions achieved through real-time monitoring and data-driven optimization.
Methodology: This study employs a quantitative case study design using a before–after comparative analysis applied using smart energy monitoring systems installed in public institutional buildings. Historical energy consumption data, energy audits, and building characteristics were analyzed. Energy Use Intensity (EUI) and CO₂ emissions were calculated, and data were processed using descriptive and comparative statistical methods with visualization tools to assess before-and-after impacts.
Main Findings: Implementation of the smart monitoring system reduced average EUI by approximately 20% (from 222.5 to 178.0 kWh/m²/year) across four institutional buildings. Daily energy consumption decreased across all time periods, with peak-hour reduction reaching 19%. CO₂ emissions declined by about 20%, mirroring energy savings. Statistical analysis confirmed that the reductions were significant (p < 0.01), demonstrating measurable improvements in operational efficiency.
Novelty/Originality of this study: This research provides the first empirical evidence from Montenegro on the effectiveness of smart energy monitoring in public institutional buildings. Unlike prior simulation-based studies, it uses full-year real data to show measurable energy and emission reductions. The findings offer actionable insights for policymakers and building managers, advancing knowledge on sustainable energy management in energy-transition countries.
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[1]“Integrating Smart Energy Monitoring and Energy Efficiency Measures to Reduce Carbon Emissions in Institutional Buildings: An Environmental Science and Sustainable Development Approach”, In. Sci. Ed. J, vol. 7, no. 3, pp. 580–587, May 2026, doi: 10.37251/isej.v7i3.2636. -
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- [1] F. Dinmohammadi, A. M. Farook, and M. Shafiee, “Improving energy efficiency in buildings with an iot-based smart monitoring system,” Energies, vol. 18, no. 5, 2025. doi: 10.3390/en18051269.
- [2] H. Haberl, M. Löw, A. Perez-Laborda, S. Matej, B. Plank, D. Wiedenhofer, F. Creutzig, K-H. Erb, and J. A. Duro, J. A. “Built structures influence patterns of energy demand and CO2 emissions across countries,” Nat. Commun., vol. 14, no. 1, pp. 3898, 2023, doi: 10.1038/s41467-023-39728-3.
- [3] J. Min, G. Yan, A. M. Abed, S. Elattar, M. A. Khadimallah, A. Jan, and H. E. Ali, “The effect of carbon dioxide emissions on the building energy efficiency,” Fuel, vol. 326, pp. 124842, 2022, doi: 10.1016/j.fuel.2022.124842.
- [4] N. K. Arora and I. Mishra, “Sustainable development goal 13: recent progress and challenges to climate action,” Environ. Sustain., vol. 6, no. 3, pp. 297–301, 2023, doi: 10.1007/s42398-023-00287-4.
- [5] W. L. Filho, T. Wall, A. L. Salvia, M. A. P. Dinis, and M. Mifsud, “The central role of climate action in achieving the United Nations’ Sustainable Development Goals,” Sci. Rep., vol. 13, no. 1, pp. 20582, 2023, doi: 10.1038/s41598-023-47746-w.
- [6] H. W. Kamran, M. Rafiq, A. Abudaqa, and A. Amin, “Interconnecting sustainable development goals 7 and 13: the role of renewable energy innovations towards combating the climate change,” Environ. Technol., vol. 45, no. 17, pp. 3439–3455, Jul. 2024, doi: 10.1080/09593330.2023.2216903.
- [7] M. Y. Orhan and A. U. Yerden, “IoT-Based systems for energy efficiency in smart buildings: present, trends and sustainability perspective,” Int. J. Adv. Nat. Sci. Eng. Res., vol. 9, no. 12 SE-Articles, pp. 130–136, 2025, https://as-proceeding.com/index.php/ijanser/article/view/2947
- [8] L. Williams, B. K. Sovacool, and T. J. Foxon, “The energy use implications of 5G: Reviewing whole network operational energy, embodied energy, and indirect effects,” Renew. Sustain. Energy Rev., vol. 157, pp. 112033, 2022, doi: 10.1016/j.rser.2021.112033.
- [9] N. Papadakis and D. A. Katsaprakakis, “A review of energy efficiency interventions in public buildings,” Energies, vol. 16, no. 17, 2023. doi: 10.3390/en16176329.
- [10] P. Rajaram and G. S. O․V․, “Data-driven predictive models for sustainable smart buildings,” Results Eng., vol. 27, p. 105916, 2025, doi: 10.1016/j.rineng.2025.105916.
- [11] A.-M. O. Mohamed, D. Mohamed, A. Fayad, and M. T. Al Nahyan, “Enhancing decision making and decarbonation in environmental management: a review on the role of digital technologies,” Sustainability, vol. 16, no. 16, pp. 7156, 2024. doi: 10.3390/su16167156.
- [12] F. Dinmohammadi, A. M. Farook, and M. Shafiee, “Improving energy efficiency in buildings with an IoT-based smart monitoring system,” Energies, vol. 18, no. 5, pp. 1269, 2025. doi: 10.3390/en18051269.
- [13] S. F. A. Shah, M. Iqbal, Z. Aziz, T. A. Rana, A. Khalid, Y. N. Cheah, and M. Arif, “The role of machine learning and the internet of things in smart buildings for energy efficiency,” Applied Sciences, vol. 12, no. 15, pp. 7882, 2022, doi: 10.3390/app12157882.
- [14] S. Bresciani, F. Rizzo, and A. Deserti, “Toward a comprehensive framework of social innovation for climate neutrality: a systematic literature review from business/production, public policy, environmental sciences, energy, sustainability and related fields,” Sustainability, vol. 14, no. 21, pp. 13793, 2022. doi: 10.3390/su142113793.
- [15] S. Krupnik et al., “Beyond technology: A research agenda for social sciences and humanities research on renewable energy in Europe,” Energy Res. Soc. Sci., vol. 89, pp. 102536, 2022, doi: https://doi.org/10.1016/j.erss.2022.102536.
- [16] A. I. Almulhim and T. Yigitcanlar, “Understanding Smart Governance of Sustainable Cities: A Review and Multidimensional Framework,” Smart Cities, vol. 8, no. 4, pp. 113, 2025. doi: 10.3390/smartcities8040113.
- [17] L. Djordjević, J. Pekez, B. Novaković, M. Bakator, M. Djurdjev, D. Ćoćkalo, and S. Jovanović, “Increasing energy efficiency of buildings in serbia—a case of an urban neighborhood,” Sustainability, vol. 15, no. 7, pp. 6300, 2023. doi: 10.3390/su15076300.
- [18] I. Jovetić, I. Vojinović, M. Vukotić, and S. Tinaj, “Montenegro 2.0: The European Vision of Montenegro BT - Western Balkans and the Future of Europe: Between an Enlargement and a Commitment Fatigue,” A. Stojkov and T. Warin, Eds., Cham: Springer Nature Switzerland, 2025, pp. 179–197. doi: 10.1007/978-3-031-86465-0_9.
- [19] U. N. E. C. for E. (UNECE), “Smart sustainable city profile of podgorica,” Geneva, Switzerland, 2023.
- [20] Governance of the energy Union and climate Action, “Draft of the integrated national energy and climate plan,” pp. 143, 2024.
- [21] L. Zhu, Z. Lian, and M. Engström, “Use of a flipped classroom in ophthalmology courses for nursing, dental and medical students: A quasi-experimental study using a mixed-methods approach,” Nurse Educ. Today, vol. 85, pp. 104262, 2020, doi: 10.1016/j.nedt.2019.104262.
- [22] M. Serreqi and L. Shahini, “Unlocking the first fuel: Energy efficiency in public buildings across the Western Balkans,” Sustainability, vol. 17, no. 22, pp. 9969, 2025. doi: 10.3390/su17229969.
- [23] J. D. Billanes, Z. G. Ma, and B. N. Jørgensen, “Data-driven technologies for energy optimization in smart buildings: A scoping review,” Energies, vol. 18, no. 2, pp. 290, 2025. doi: 10.3390/en18020290.
- [24] V. Marinakis, “Big data for energy management and energy-efficient buildings,” Energies, vol. 13, no. 7, pp. 1555, 2020, doi: 10.3390/en13071555.
- [25] S. E. Bibri and J. Krogstie, “Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism,” Energy Informatics, vol. 3, no. 1, pp. 29, 2020, doi: 10.1186/s42162-020-00130-8.
- [26] E. López-García, J. Lizana, A. Serrano-Jiménez, C. Díaz-López, and Ángela Barrios-Padura, “Monitoring and analytics to measure heat resilience of buildings and support retrofitting by passive cooling,” J. Build. Eng., vol. 57, pp. 104985, 2022, doi: 10.1016/j.jobe.2022.104985.
- [27] G. Wojciechowska, Ł. J. Bednarz, N. Dolińska, P. Opałka, M. Krupa, and N. Imnadze, “Intelligent monitoring system for integrated management of historical buildings,” Buildings, vol. 14, no. 7, pp. 2108, 2024. doi: 10.3390/buildings14072108.
- [28] D. Ma, X. Li, B. Lin, Y. Zhu, and S. Yue, “A dynamic intelligent building retrofit decision-making model in response to climate change,” Energy Build., vol. 284, pp. 112832, 2023, doi: 10.1016/j.enbuild.2023.112832.
- [29] K. Sushko, P. Strachan, M. Butt, K. Nerenberg, and D. Sherifali, “Supporting self-management in women with pre-existing diabetes in pregnancy: a mixed-methods sequential comparative case study,” BMC Nurs., vol. 23, no. 1, pp. 1, 2024, doi: 10.1186/s12912-023-01659-1.
- [30] R. El Sherif, P. Pluye, Q. N. Hong, and B. Rihoux, “Using qualitative comparative analysis as a mixed methods synthesis in systematic mixed studies reviews: Guidance and a worked example,” Res. Synth. Methods, vol. 15, no. 3, pp. 450–465, 2024, doi: 10.1002/jrsm.1698.
- [31] B. Gajdzik, M. Jaciow, R. Wolniak, R. Wolny, and W. W. Grebski, “Diagnosis of the development of energy cooperatives in Poland—a case study of a renewable energy cooperative in the upper Silesian region,” Energies, vol. 17, no. 3, pp. 647, 2024. doi: 10.3390/en17030647.
- [32] R. Apanavičienė and M. M. Shahrabani, “Key factors affecting smart building integration into smart city: technological aspects,” Smart Cities, vol. 6, no. 4, pp. 1832-1857, 2023. doi: 10.3390/smartcities6040085.
- [33] X. Li and X. Zhang, “A comparative study of statistical and machine learning models on carbon dioxide emissions prediction of China,” Environ. Sci. Pollut. Res., vol. 30, no. 55, pp. 117485–117502, 2023, doi: 10.1007/s11356-023-30428-5.
- [34] L. Tawalbeh, J. Delgado, S. Solis, T. Juarez, J. D. Tietjen, and F. Muheidat, “Energy consumption and carbon emissions data analysis: Case study and future predictions,” Procedia Comput. Sci., vol. 220, pp. 616–623, 2023, doi: 10.1016/j.procs.2023.03.078.
- [35] R. Huang, X. Zhang, and K. Liu, “Assessment of operational carbon emissions for residential buildings comparing different machine learning approaches: A study of 34 cities in China,” Build. Environ., vol. 250, pp. 111176, 2024, doi: 10.1016/j.buildenv.2024.111176.
- [36] Q. Li, Z. Jiang, and F. Yuan, “Monitoring and visualization application of smart city energy economic management based on IoT sensors,” Neural Comput. Appl., vol. 34, no. 9, pp. 6695–6704, 2022, doi: 10.1007/s00521-021-06108-1.
- [37] J. An et al., “Analysis of the impact of energy consumption data visualization using augmented reality on energy consumption and indoor environment quality,” Build. Environ., vol. 250, pp. 111177, 2024, doi: 10.1016/j.buildenv.2024.111177.
- [38] O. Vera-Piazzini, M. Scarpa, and F. Peron, “Building energy simulation and monitoring: a review of graphical data representation,” Energies, vol. 16, no. 1, pp. 390, 2023. doi: 10.3390/en16010390.
- [39] A. Franco, E. Crisostomi, F. Leccese, A. Mugnani, and S. Suin, “Energy savings in university buildings: the potential role of smart monitoring and IoT technologies,” Sustainability, vol. 17, no. 1, pp. 111, 2025. doi: 10.3390/su17010111.
- [40] L. U. G. Ekanayaka Gunasinghalge, A. Alazab, and M. A. Talukder, “Artificial intelligence for energy optimization in smart buildings: A systematic review and meta-analysis,” Energy Informatics, vol. 8, no. 1, pp. 135, 2025, doi: 10.1186/s42162-025-00592-8.
- [41] J. Zhao, X. Qu, Y. Wu, M. Fowler, and A. F. Burke, “Artificial intelligence-driven real-world battery diagnostics,” Energy and AI, vol. 18, pp. 100419, 2024, doi: 10.1016/j.egyai.2024.100419.
- [42] M. Paniagua-Gómez, and M. Fernandez-Carmona, “Trends and challenges in real-time stress detection and modulation: The role of the iot and artificial intelligence,” Electronics, vol. 14, no. 13, pp. 2581, 2025, doi: 10.3390/electronics14132581.
- [43] Y. Qian, and K. L. Siau, “Advances in IoT, AI, and sensor-based technologies for disease treatment, health promotion, successful ageing, and ageing well,” Sensors, vol. 25, no. 19, pp. 6207, 2025, doi: 10.3390/s25196207.
- [44] R. Roka, A. Figueiredo, A. Vieira, and C. Cardoso, “A systematic review of sensitivity analysis in building energy modeling: Key factors influencing building thermal energy performance,” Energies, vol. 18, no. 9, pp. 2375, 2025, doi: 10.3390/en18092375.
- [45] P. Szałański, and P. Kowalski, “The impact of input data on the building energy performance gap: a case study of heating a single-family building in polish conditions,” Energies, vol. 18, no. 24, pp. 6396, 2025, doi: 10.3390/en18246396.
- [46] T. Dogan, C. Li, H. M. Tseng, A. J. Su, and P. Kastner, “A bottom-up urban building energy model for evaluating thermal load electrification measures,” Journal of Building Performance Simulation, vol. 19, no. 2, pp. 289-316, 2026, doi: 10.1080/19401493.2025.2536261.
- [47] I. Imran, 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.
- [48] M. A. Islam, and M. S. Akter, “Impact of IOT-Based energy monitoring systems on operational efficiency in industrial facilities: a qualitative evaluation,” American Journal of Interdisciplinary Studies, vol. 3, no. 4, pp. 770-806, 2022, doi: 10.63125/106zbv89.
- [49] 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, 2023, doi: 10.3390/buildings14113446.
- [50] I. Rojek, D. Mikołajewski, A. Mroziński, M. Macko, T. Bednarek, and K. Tyburek, “Internet of things applications for energy management in buildings using artificial intelligence—A case study,” Energies, vol. 18, no. 7, pp. 1706, 2025, doi: 10.3390/en18071706.
- [51] A. Mohamed, I. Ismail, and M. AlDaraawi, “IoT-Driven intelligent energy management: leveraging smart monitoring applications and artificial neural networks (ann) for sustainable practices,” Computers, vol. 14, no. 7, pp. 269, 2025, doi: 10.3390/computers14070269.
- [52] A. U. Rehman, H. M. Khalid, A. Ismail, R. Alhejaili, A. Gulraiz, Z. Said, and G. A. Hussain, “An IoT integrated home energy management system: an optimized solution towards real-time energy resources monitoring and climate-based decision support,” International Journal of Electrical Power & Energy Systems, vol. 177, pp. 111836, 2026, doi: 10.1016/j.ijepes.2026.111836.