Rok Hribar

Journal articlesshow all

Advanced computing to support urban climate neutrality, Energy Sustain. Soc. (2025)

Conference papersshow all

Books & Chaptersshow all

A Framework for Applying Data-Driven AI/ML Models in Reliability, Recent Advances in Microelectronics Reliability, Springer (2024)
Reliability Improvements for In-Wheel Motor, Recent Advances in Microelectronics Reliability, Springer (2024)

Advanced computing to support urban climate neutrality

G. Papa, R. Hribar, G. Petelin, V. Vukašinović
DOI: 10.1186/s13705-025-00517-z

BibTex:
@article{PapeEtAl2025, title = {Advanced computing to support urban climate neutrality}, journal = {Energy, Sustainability and Society}, volume = {15}, pages = {}, year = {2025}, issn = {2192-0567}, doi = {https://doi.org/10.1186/s13705-025-00517-z}, url = {https://link.springer.com/article/10.1186/s13705-025-00517-z}, author = {Gregor Papa and Rok Hribar and Gašper Petelin and Vida Vukašinović} }

abstract:
Achieving climate neutrality in cities is a major challenge, especially in light of rapid urbanization and the urgent need to combat climate change. This paper explores the role of advanced computational methods in the transition of cities to climate neutrality, with a focus on energy supply and transportation systems. Central to this are recent advances in artificial intelligence, particularly machine learning, which offer enhanced capabilities for analyzing and processing large, heterogeneous urban data. By integrating these computational tools, cities can develop and optimize complex models that enable real-time, data-driven decisions. Such strategies offer the potential to significantly reduce greenhouse gas emissions, improve energy efficiency in key infrastructures and strengthen the sustainability and resilience of cities. In addition, these approaches support predictive modeling and dynamic management of urban systems, enabling cities to address the multi-faceted challenges of climate change in a scalable and proactive way. The methods, which go beyond traditional data processing, use state-of-the-art technologies such as deep learning and ensemble models to tackle the complexity of environmental parameters and resource management in urban systems. For example, recurrent neural networks have been trained to predict gas consumption in Ljubljana, enabling efficient allocation of energy resources up to 60 h in advance. Similarly, traffic flow predictions were made based on historical and weather-related data, providing insights for improved urban mobility. In the context of logistics and public transportation, computational optimization techniques have demonstrated their potential to reduce congestion, emissions and operating costs, underlining their central role in creating more sustainable and efficient urban environments. The integration of cutting-edge technologies, advanced data analytics and real-time decision-making processes represents a transformative pathway to developing sustainable, climate-resilient urban environments. These advanced computational methods enable cities to optimize resource management, improve energy efficiency and significantly reduce greenhouse gas emissions, thus actively contributing to global climate and environmental protection.