Please use this identifier to cite or link to this item:
|Title:||Data-driven ship berthing forecasting for cold ironing in maritime transportation||Authors:||Bakar, Nur Najihah Abu
Vasquez, Juan C.
Guerrero, Josep M.
|Issue Date:||2022||Publisher:||Elsevier Ltd||Abstract:||Cold ironing (CI) is an electrification alternative in the maritime sector used to reduce shipborne emissions by switching from fuel to electricity when a ship docks at a port. During the ship's berthing mode of operation, accurately estimating the berthing duration could assist the port operator to manage the berth allocation and energy scheduling optimally. However, the involvement of multiple input parameters with a large dataset requires a suitable handling method. Thus, this paper proposed a data-driven approach for ship berthing forecasting of cold ironing with various models such as artificial neural networks, multiple linear regression, random forest, decision tree, and extreme gradient boosting. Meanwhile, RMSE and MAE are two main indicators applied to assess forecasting accuracy. The simulation-based result shows that the artificial neural network outperforms all other models with the lowest error performance of RMSE (3.1343) and MAE (0.2548), suggesting its capability to handle nonlinearities in complex forecasting problems of port activity. The high accuracy of forecasting output in this study, which is berthing duration contributes to close estimation of two info: 1) CI power consumption and 2) departure time of the ship. This information is vital to the port operator to be used in the energy management system (EMS) as well as in the berth allocation problem (BAP). © 2022 The Author(s)||URI:||https://doi.org/10.1016/j.apenergy.2022.119947
|Appears in Collections:||Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu|
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
Show full item record
Files in This Item:
|5.85 MB||Adobe PDF||View/Open Request a copy|
WEB OF SCIENCETM
checked on Dec 4, 2023
checked on Dec 4, 2023
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.