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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.
Published Version
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