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All Journal JOIV : International Journal on Informatics Visualization International Journal of Renewable Energy Development
Tran, Nguyen Bao Minh
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Hybrid renewable energy system design for a green port using HOMER Pro: A techno-economic assessment Dinh, Gia Huy; Pham, Minh Tuan; Tran, Nguyen Bao Minh; Tran, Cong Minh; Nguyen, Tat Quyen; Le, Thanh Tien; Nguyen, Hoang Phuong
International Journal of Renewable Energy Development Vol 14, No 4 (2025): July 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61344

Abstract

Maritime plays an important role in the national economy since a large number of goods in the world are transported by sea, although maritime transport is found to generate the largest greenhouse gas emission among transportation means. For maritime activities, the port is considered the key chain in logistics, thus, the transformation of ports into sustainable energy centres has emerged as a major need in the worldwide initiative to decarbonize marine activities. This research provides a comprehensive techno-economic evaluation of a Hybrid Renewable Energy System (HRES) for Thi Nai Port, Vietnam, utilizing HOMER Pro software. The suggested system seeks to eradicate dependence on fossil fuels by including solar photovoltaics, wind turbines, a biogas generator, and sophisticated battery storage, therefore providing operational robustness. Simulation outcomes demonstrate that an ideal configuration, consisting of a 6,175-kW photovoltaic array, a 500-kW biogas generator, and a 2,357-kW converter, results in a net present cost of 44.6 million USD and a levelized cost of energy of 0.394 USD/kWh. Renewable sources constitute 100% of the installed and operational capacity, with yearly carbon dioxide emissions diminished to a modest 1,286 kg. The research verifies that hybrid renewable solutions may provide competitive economic returns, with a payback period of eight to ten years, while delivering substantial environmental advantages. The study portrays Thi Nai Port as a scalable paradigm for green port transformation, offering a repeatable framework for other mid-sized ports in Southeast Asia pursuing sustainable energy solutions.
An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation Vu, Van Vien; Le, Phuoc Tai; Do, Thi Mai Thom; Nguyen, Thi Thuy Hieu; Tran, Nguyen Bao Minh; Paramasivam, Prabhu; Le, Thi Thai; Le, Huu Cuong; Chau, Thanh Hieu
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2641

Abstract

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.
Co-Authors Chau, Thanh Hieu Dinh, Gia Huy Do, Thi Mai Thom Le, Huu Cuong Le, Phuoc Tai Le, Thanh Tien Le, Thi Thai Nguyen, Hoang Phuong Nguyen, Tat Quyen Nguyen, Thi Thuy Hieu Paramasivam, Prabhu Pham, Minh Tuan Tran, Cong Minh Vu, Van Vien