Energy Forecasting Model for Ground Movement Operation in Green Airport

Adedayo Ajayi, Patrick Chi-Kwong Luk (), Liyun Lao and Mohammad Farhan Khan
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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

Energies, 2023, vol. 16, issue 13, 1-19

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.

Keywords: green airport; multivariate; energy prediction; prophet algorithm; renewable energy; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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