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Electricity demand forecasting in green ports: Modelling and future research directions

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  • Carrillo-Galvez, Adrian
  • Carmo, Felipe do
  • Soares, Tiago
  • Mourão, Zenaida
  • Ponomarev, Ilia
  • Araújo, João
  • Bandeira, Eduardo

Abstract

Recently, there has been growing attention on the decarbonisation of maritime transport, particularly regarding the landside operations at ports. This has spurred the development and implementation of strategies and policies aimed at enhancing the environmental performance of port activities. Among these strategies, the electrification of port infrastructure is emerging as a potential industry standard for the future. However, there remains a significant gap in understanding the patterns of electricity consumption in ports and how to forecast them accurately. To address this gap, this paper provides a review of the current literature on electricity demand in ports, examining practical applications, methodologies employed, and their key limitations. The findings indicate that, despite its importance in supporting the electrification process, electricity demand forecasting in ports has not received substantial attention in either industry or academic research, and there are no clearly established policies to support port authorities in obtaining the necessary data. Finally, the paper outlines potential directions for future research and how port authorities or local government agencies can contribute to these efforts.

Suggested Citation

  • Carrillo-Galvez, Adrian & Carmo, Felipe do & Soares, Tiago & Mourão, Zenaida & Ponomarev, Ilia & Araújo, João & Bandeira, Eduardo, 2025. "Electricity demand forecasting in green ports: Modelling and future research directions," Transport Policy, Elsevier, vol. 171(C), pages 1012-1024.
  • Handle: RePEc:eee:trapol:v:171:y:2025:i:c:p:1012-1024
    DOI: 10.1016/j.tranpol.2025.07.013
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    1. Marcel Antal & Vlad Mihailescu & Tudor Cioara & Ionut Anghel, 2022. "Blockchain-Based Distributed Federated Learning in Smart Grid," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    2. Lam, Jasmine Siu Lee & Li, Kevin X., 2019. "Green port marketing for sustainable growth and development," Transport Policy, Elsevier, vol. 84(C), pages 73-81.
    3. Iris, Çağatay & Lam, Jasmine Siu Lee, 2021. "Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty," Omega, Elsevier, vol. 103(C).
    4. Harry Geerlings & Robert Heij & Ron van Duin, 2018. "Opportunities for peak shaving the energy demand of ship-to-shore quay cranes at container terminals," Journal of Shipping and Trade, Springer, vol. 3(1), pages 1-20, December.
    5. J. H. R. van Duin & H. Geerlings & L. A. Tavasszy & D. L. Bank, 2019. "Factors causing peak energy consumption of reefers at container terminals," Journal of Shipping and Trade, Springer, vol. 4(1), pages 1-17, December.
    6. Alper Seyhan, 2025. "A Novel Study for Machine-Learning-Based Ship Energy Demand Forecasting in Container Port," Sustainability, MDPI, vol. 17(12), pages 1-14, June.
    7. Mariusz Brzeziński & Dariusz Pyza & Joanna Archutowska & Michał Budzik, 2024. "Method of Estimating Energy Consumption for Intermodal Terminal Loading System Design," Energies, MDPI, vol. 17(24), pages 1-35, December.
    8. Guang Chen & Xiaofeng Ma & Lin Wei, 2024. "Multifeature-Based Variational Mode Decomposition–Temporal Convolutional Network–Long Short-Term Memory for Short-Term Forecasting of the Load of Port Power Systems," Sustainability, MDPI, vol. 16(13), pages 1-20, June.
    9. Zhang, Yue & Liang, Chengji & Shi, Jian & Lim, Gino & Wu, Yiwei, 2022. "Optimal Port Microgrid Scheduling Incorporating Onshore Power Supply and Berth Allocation Under Uncertainty," Applied Energy, Elsevier, vol. 313(C).
    10. Aiming Mo & Yan Zhang & Yiyong Xiong & Fan Ma & Lin Sun, 2024. "Energy–Logistics Cooperative Optimization for a Port-Integrated Energy System," Mathematics, MDPI, vol. 12(12), pages 1-24, June.
    11. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
    12. Zhen, Lu & Lin, Shumin & Zhou, Chenhao, 2022. "Green port oriented resilience improvement for traffic-power coupled networks," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    13. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    14. Martínez-Moya, Julián & Vazquez-Paja, Barbara & Gimenez Maldonado, Jose Andrés, 2019. "Energy efficiency and CO2 emissions of port container terminal equipment: Evidence from the Port of Valencia," Energy Policy, Elsevier, vol. 131(C), pages 312-319.
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