Energy Forecasting Model for Ground Movement Operation in Green Airport

Autor: Adedayo Ajayi, Patrick Chi-Kwong Luk, Liyun Lao, Mohammad Farhan Khan
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Energies, Vol 16, Iss 13, p 5008 (2023)
Druh dokumentu: article
ISSN: 16135008
1996-1073
DOI: 10.3390/en16135008
Popis: 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.
Databáze: Directory of Open Access Journals
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