Abstract :
Urban overheating is an electro-physical problem because surface heat accumulation changes outdoor temperature conditions and increases the electrical energy required for cooling. This study develops a scenario-based electro-physical assessment model for analysing the relationship between urban surface heat transfer, surface material properties, green infrastructure and electrical cooling demand. The model links physical parameters such as albedo, emissivity, thermal mass, vegetation coverage and urban–rural temperature difference with engineering indicators such as cooling energy demand, energy-efficiency improvement and CO₂ emissions related to electricity use. The case-study application uses Sofia Municipality as a spatial reference environment. Real spatial and land-use indicators are combined with scenario-based calculations in order to compare a baseline urban condition with an optimized scenario. The results are interpreted as early-stage engineering estimates rather than direct measurements of Urban Heat Island reduction or electricity consumption. The contribution of the paper is an integrated framework that connects heat-transfer processes with electrical energy performance in smart urban environments.
Keywords :
Electrical cooling demand, electro-physical modelling, Energy efficiency, smart urban systems, Urban Heat Island.References :
- Bartesaghi-Koc, C., Osmond, P., & Peters, A. (2020). Quantifying the seasonal cooling capacity of “green infrastructure types” (GITs): An approach to assess and mitigate surface urban heat island in Sydney, Australia. Landscape and Urban Planning, 203, Article 103893. https://doi.org/10.1016/j.landurbplan.2020.103893
- Bortolini, R., Rodrigues, R., Alavi, H., Vecchia, L. F. D., & Forcada, N. (2022). Digital twins’ applications for building energy efficiency: A review. Energies, 15(19), Article 7002. https://doi.org/10.3390/en15197002
- Bowler, D. E., Buyung-Ali, L., Knight, T. M., & Pullin, A. S. (2010). Urban greening to cool towns and cities: A systematic review of the empirical evidence. Landscape and Urban Planning, 97(3), 147-155. https://doi.org/10.1016/j.landurbplan.2010.05.006
- (2023). Energy efficiency 2023. International Energy Agency.
- Koleva, N. (2024). Human in cooperation with AI: Next level of intelligent man-machine interaction. Proceedings of World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI Conference. https://doi.org/10.54808/WMSCI2024.01.292
- Li, D., & Bou-Zeid, E. (2013). Synergistic interactions between urban heat islands and heat waves: The impact in cities is larger than the sum of its parts. Journal of Applied Meteorology and Climatology, 52(9), 2051-2064. https://doi.org/10.1175/JAMC-D-13-02.1
- Marando, F., Heris, M. P., Zulian, G., Udias, A., Mentaschi, L., Chrysoulakis, N., Parastatidis, D., & Maes, J. (2022). Urban heat island mitigation by green infrastructure in European Functional Urban Areas. Sustainable Cities and Society, 77, Article 103564. https://doi.org/10.1016/j.scs.2021.103564
- Miller, C., Kathirgamanathan, A., Picchetti, B., Arjunan, P., Park, J. Y., Nagy, Z., Raftery, P., Hobson, B. W., Shi, Z., & Meggers, F. (2020). The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition. Scientific Data, 7, Article 368. https://doi.org/10.1038/s41597-020-00712-x
- National Statistical Institute. (2025). Sofia (stolitsa): Statistical data. National Statistical Institute of the Republic of Bulgaria.
- Mohammed, A., Khan, A., Khan, H. S., & Santamouris, M. (2023). Cooling energy benefits of increased green infrastructure in subtropical urban building environments. Buildings, 13(9), Article 2257. https://doi.org/10.3390/buildings13092257
- Norton, B. A., Coutts, A. M., Livesley, S. J., Harris, R. J., Hunter, A. M., & Williams, N. S. G. (2015). Planning for cooler cities: A framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landscape and Urban Planning, 134, 127-138. https://doi.org/10.1016/j.landurbplan.2014.10.018
- Peneva, G. & Andreev, O. (2023). Assessing Bulgarian SMEs’ Maturity for Industry 4.0 Implementing – the Case of Montana Hydraulics Ltd., Strategies for Policy in Science and Education, Vol. 31, No 3s, pp. 9-24, Az-buki National Publishing House for Education and Science, ISSN 1310-0270 (Print), ISSN 1314-8575 (Online), DOI: 10.53656/str2023-3s-1-ass
- PwC Advisory spółka z ograniczoną odpowiedzialnością sp.k. (2020). The Green City Action Plan: Sofia, Bulgaria. Prepared for Sofia Municipality; funded by the European Bank for Reconstruction and Development.
- Salvati, A., Kolokotroni, M., Kotopouleas, A., Watkins, R., Giridharan, R., & Nikolopoulou, M. (2022). Impact of reflective materials on urban canyon albedo, outdoor and indoor microclimates. Building and Environment, 207, Article 108459. https://doi.org/10.1016/j.buildenv.2021.108459
- Santamouris, M. (2014). Cooling the cities: A review of reflective and green roof mitigation technologies to fight heat island and improve comfort in urban environments. Solar Energy, 103, 682-703. https://doi.org/10.1016/j.solener.2012.07.003
- Santamouris, M. (2020). Recent progress on urban overheating and heat island research: Integrated assessment of the energy, environmental, vulnerability and health impact; synergies with global climate change. Energy and Buildings, 207, Article 109482. https://doi.org/10.1016/j.enbuild.2019.109482
- Sezer, N., Yoonus, H., Zhan, D., Wang, L., Hassan, I. G., & Rahman, M. A. (2023). Urban microclimate and building energy models: A review of the latest progress in coupling strategies. Renewable and Sustainable Energy Reviews, 184, Article 113577. https://doi.org/10.1016/j.rser.2023.113577
- Sofia Municipality. (n.d.). Geographical characteristics. Portal of Sofia Municipality.
- Stewart, I. D., & Oke, T. R. (2012). Local Climate Zones for urban temperature studies. Bulletin of the American Meteorological Society, 93(12), 1879-1900. https://doi.org/10.1175/BAMS-D-11-00019.1
- Vasilev, I. (2026). Exemplary Model of AI-Supported Adaptive Optimization Energy Flow Control in SmartCity Microgrids: A Simulation-Based Scenarios. International Journal of Current Science Research and Review, 9(4), pp. 1824-1833. DOI: https://doi.org/10.47191/ijcsrr/V9-i4-14
- Vasilev, I. (2026). A Hybrid “ARIMA–ML Regression” Model for Enhanced Predictive Analysis in Cyber-Physical Systems: Conceptual framework and Simulation Evaluation. International Journal of Current Science Research and Review, 9(5), pp. 2232-2243. DOI: https://doi.org/10.47191/ijcsrr/V9-i5-02
- Vasilev, I. (2026). A Stochastic Framework for Fully Distributed Control Systems and CPS: From Local State Transitions to Global Uncertainty Propagation. International Journal of Current Science Research and Review, 9(6), pp. 2943-2957. DOI: https://doi.org/10.47191/ijcsrr/V9-i6-01
- Yang, J., Wang, Z.-H., & Kaloush, K. E. (2015). Environmental impacts of reflective materials: Is high albedo a “silver bullet” for mitigating urban heat island? Renewable and Sustainable Energy Reviews, 47, 830-843. https://doi.org/10.1016/j.rser.2015.03.092
- Zhou, D., Zhao, S., Liu, S., Zhang, L., & Zhu, C. (2014). Surface urban heat island in China’s 32 major cities: Spatial patterns and drivers. Remote Sensing of Environment, 152, 51-61. https://doi.org/10.1016/j.rse.2014.05.017

