arXiv:2607. 21444v1 Announce Type: cross Abstract: Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme heat, heavy precipitation, and humidity increasingly raises equipment fault risk and undermines the resilience of urban energy and mobility services.
By Cande Lian (School of Management, Foshan University, Foshan, China), Wentao Zeng (School of Management, Foshan University, Foshan, China), Jiabin Wu (School of Management, Foshan University, Foshan, China), Yiming Bie (School of Transportation, Jilin University, Changchun, China), Wei Zhou (Department of Civil and Environmental Engineering, National University of Singapore)
arXiv:2607. 11349v1 Announce Type: cross Abstract: Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather.
By Wentao Zeng (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China, School of Mechanical and Electrical Engineering and Automation, Foshan University, Foshan, China), Zijian Huang (School of Artificial Intelligence, South China Normal University, Guangzhou, China), Yiming Bie (School of Transportation, Jilin University, Changchun, China), Jiabin Wu (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China), Jun Gong (Department of Civil Engineering, The University of Hong Kong, Hong Kong, China)
Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather. Existing models struggle to represent these heterogeneous inputs and rarely explain the causal drivers of consumption.
The paper proposes a proactive approach to road safety in Greater Sydney by using connected vehicle telemetry to predict risky driving events before crashes occur. It quantifies risky driving with g‑force thresholds and builds spatio‑temporal heatmaps to locate high‑risk zones. Eight predictive models were compared, with ARIMA achieving the lowest error and showing that simple time‑series methods can rival deep learning when data are limited, highlighting the value of IoT data for targeted safety interventions.
By Adriana-Simona Mih\u{a}i\c{t}\u{a}, Clarence Cheung, Artur Grigorev, Tuo Mao, David Lillo-Trynes
arXiv:2608. 02088v1 Announce Type: new Abstract: Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records.
By Fariba Dehghan, Sebastian Stein, Vahid Yazdanpanah, Stephanie Gauthier, Masood Nazari
arXiv:2606. 17572v1 Announce Type: new Abstract: Learned dynamics models often answer global physical questions, such as fault severity or impact stiffness, by pooling a per-step feature sequence into one readout vector.
By Yifan Wang
arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.
By Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar, Pascal Van Hentenryck
arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.
By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni
arXiv:2604. 26634v2 Announce Type: replace Abstract: Norway's electricity market is heavily dominated by hydropower, but the 2021-2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models calibrated on historical data.
By My Thi Diem Phan, Trung Tuyen Truong, Hoai Phuong Ha, Dat Thanh Nguyen
The paper introduces Multi-Task Anti-Causal learning (MTAC), a framework that estimates latent causes from observed effects by exploiting both task-invariant and task-specific structural dependencies. MTAC constructs a structural equation model that separates a shared backbone mechanism from task-specific deviations, then uses maximum a posteriori inference to reconstruct causes. Applied to urban event reconstruction—parking violations, abandoned properties, and unsanitary conditions—MTAC outperforms strong baselines on real data from Manhattan and Newark, achieving up to a 33.04% reduction in mean absolute error.
By Liangkai Zhou, Susu Xu, Shuqi Zhong, Shan Lin
The paper presents a modular, scalable framework for estimating the probability of failure (PoF) of power‑line assets using geospatial machine learning. It integrates diverse environmental predictors—topography, vegetation indices, lightning climatology, proximity features, and operational records—to model vegetation‑ and lightning‑related failure modes. The architecture is designed to be computationally efficient, easily extensible to new data sources, and suitable for large‑scale utility deployment, enabling asset‑level risk stratification for inspection and resilience planning.
By Artur Sokolovsky, Bhavik Merai, Moe Jafari, Muen Chen
arXiv:2606. 19199v1 Announce Type: cross Abstract: The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability.
By Giuseppe Gabriele, Fabio Pavirani, Seyed Soroush Karimi Madahi, Chris Develder