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Energy Forecasting Model for Ground Movement Operation in Green Airport

Author

Listed:
  • Adedayo Ajayi

    (School of Water, Energy, Environment, Cranfield University, Cranfield MK43 0AL, UK)

  • Patrick Chi-Kwong Luk

    (School of Water, Energy, Environment, Cranfield University, Cranfield MK43 0AL, UK)

  • Liyun Lao

    (School of Water, Energy, Environment, Cranfield University, Cranfield MK43 0AL, UK)

  • Mohammad Farhan Khan

    (Sir David Bell Building, Digby Stuart College, University of Roehampton, London SW15 5PH, UK)

Abstract

The aviation industry has driven economic growth and facilitated cultural exchange over the past century. However, concerns have arisen regarding its contribution to greenhouse gas emissions and potential impact on climate change. In response to this challenge, stakeholders have proposed the use of electric ground support vehicles, powered by renewable energy sources, at airports. This solution aims to not only reduce emissions, but to also lower energy costs. Nonetheless, the successful implementation of such a system relies on accurate energy demand forecasting, which is influenced by flight data and fluctuations in renewable energy availability. This paper presents a novel data-driven, machine-learning-based energy prediction model that compared the performance of the Facebook Prophet and vector autoregressive integrated moving average algorithms to develop time series models to forecast the ground movement operation net energy demand in the airport, using historical flight data and an onsite airport-based PV power system (ASPV). The results demonstrate the superiority of the Facebook Prophet model over the vector autoregressive integrated moving average (VARIMA), highlighting its utility for airport operators and planners in managing energy consumption and preparing for future electrified ground movement operations at the airport.

Suggested Citation

  • Adedayo Ajayi & Patrick Chi-Kwong Luk & Liyun Lao & Mohammad Farhan Khan, 2023. "Energy Forecasting Model for Ground Movement Operation in Green Airport," Energies, MDPI, vol. 16(13), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5008-:d:1181711
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    References listed on IDEAS

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    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    2. Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
    3. Johannes Buhl & Christa Liedtke & Sebastian Schuster & Katrin Bienge, 2020. "Predicting the Material Footprint in Germany between 2015 and 2020 via Seasonally Decomposed Autoregressive and Exponential Smoothing Algorithms," Resources, MDPI, vol. 9(11), pages 1-17, October.
    4. Xuejun Shen & Limin Ou & Xiaojun Chen & Xin Zhang & Xuerui Tan, 2013. "The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-6, April.
    5. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.
    6. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    7. Forouzanfar, Mehdi & Doustmohammadi, Ali & Menhaj, M. Bagher & Hasanzadeh, Samira, 2010. "Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran," Applied Energy, Elsevier, vol. 87(1), pages 268-274, January.
    8. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    9. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    10. Mustika Sari & Wan Mazlina Wan Mohamed & Siti Ayu Jalil, 2022. "The Optimization Using Electric Ground Support Equipment in Aviation Industry," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 401-406.
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