Highway Typical Scenario Operation and Maintenance Energy Demand Forecasting

Jie Wang, Yuqiang Li, Junfeng Mai, Minmin Yuan () and Zhiqiang Liu
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Jie Wang: School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
Yuqiang Li: School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
Junfeng Mai: School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
Minmin Yuan: Research Institute of Highway Ministry of Transport, Beijing 100088, China
Zhiqiang Liu: Research Institute of Highway Ministry of Transport, Beijing 100088, China

Sustainability, 2025, vol. 17, issue 5, 1-17

Abstract: Highways play a critical role in global energy transitions and climate change mitigation, making the accurate forecasting of operational energy demand essential for improving energy efficiency and promoting green energy applications. This study develops a multi-scenario energy demand forecasting model focused on five key operational contexts: service areas, tunnels, toll stations, management centers, and roadside facilities. The model integrates user characteristics, behavioral patterns, and meteorological data, employing agent-based modeling (ABM) and the fuzzy C-means (FCM) clustering algorithm to simulate and analyze energy demand. Results indicate that during major holidays, total daily electricity consumption and peak demand increase by 143.2% and 43.8%, respectively, compared to baseline conditions. Conversely, during snowfall events, total electricity consumption and peak demand decrease by 8.8% and 11.7%, respectively. These findings provide valuable data support and a scientific basis for sustainable energy management in highway operations, contributing to the broader application of green energy solutions.

Keywords: green energy; demand forecasting; agent-based modeling; clustering algorithm; highway (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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