Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency
This research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. It details new methodologies, including strategic energy management and a machine learning (ML) tool for predicting energy surplus and deficits. The hybrid energy modul...
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2025-05-01
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| author | Helena M. Ramos João S. T. Coelho Eyup Bekci Toni X. Adrover Oscar E. Coronado-Hernández Modesto Perez-Sanchez Kemal Koca Aonghus McNabola R. Espina-Valdés |
| author_facet | Helena M. Ramos João S. T. Coelho Eyup Bekci Toni X. Adrover Oscar E. Coronado-Hernández Modesto Perez-Sanchez Kemal Koca Aonghus McNabola R. Espina-Valdés |
| author_sort | Helena M. Ramos |
| collection | DOAJ |
| description | This research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. It details new methodologies, including strategic energy management and a machine learning (ML) tool for predicting energy surplus and deficits. The hybrid energy module solution for the Port of Avilés was further developed to evaluate the performance of new tools such as the Energy Management Tool (EMTv1), HYbrid for Renewable Energy Solutions (HY4RES), and a commercial model (Hybrid Optimization of Multiple Energy Resources—HOMER) in optimizing renewable energy and storage management. Seven scenarios were analyzed, integrating different energy sources and storage solutions. Using EMTv1, Scenario 1 showed high surplus energy, while Scenario 2 demonstrated grid independence with Pump-as-Turbine (PAT) storage. The HY4RES model was used to analyze Scenario 3, which achieved a positive grid balance, exporting more than imported, and Scenario 4 revealed limitations of the PAT system due to the low power installed. Scenario 5 introduced a 15 kWh battery, efficiently storing and discharging energy, reducing grid reliance, and fully covering energy needs. Using HOMER modeling, Scenario 6 required 546 kWh of grid energy but sold 2385 kWh back. Scenario 7 produced 3450 kWh/year, covering demand, resulting in 1834 kWh of surplus energy and a small capacity shortage (1.41 kWh/year). AI-based ML analysis was applied to five scenarios (the ones with access to numerical results), accurately predicting energy balances and optimizing grid interactions. A neural network time series (NNTS) model trained on average year data achieved high accuracy (R<sup>2</sup>: 0.9253–0.9695). The ANN model proved effective in making rapid energy balance predictions, reducing the need for complex simulations. A second case analyzed an increase of 80% in demand, confirming the model’s reliability, with Scenario 3 having the highest MSE (0.0166 kWh), Scenario 2 the lowest R<sup>2</sup> (0.9289), and Scenario 5 the highest R<sup>2</sup> (0.9693) during the validation process. This study highlights AI-driven forecasting as a valuable tool for ports to optimize energy management, minimize grid dependency, and enhance their efficiency. |
| format | Article |
| id | doaj-art-44ca3d455c7542d5a888a3e486ba3a42 |
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| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-44ca3d455c7542d5a888a3e486ba3a422025-08-20T01:49:11ZengMDPI AGApplied Sciences2076-34172025-05-01159521110.3390/app15095211Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ EfficiencyHelena M. Ramos0João S. T. Coelho1Eyup Bekci2Toni X. Adrover3Oscar E. Coronado-Hernández4Modesto Perez-Sanchez5Kemal Koca6Aonghus McNabola7R. Espina-Valdés8Civil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior Técnico, Department of Civil Engineering, Architecture and Environment, University of Lisbon, 1049-001 Lisbon, PortugalCivil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior Técnico, Department of Civil Engineering, Architecture and Environment, University of Lisbon, 1049-001 Lisbon, PortugalCivil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior Técnico, Department of Civil Engineering, Architecture and Environment, University of Lisbon, 1049-001 Lisbon, PortugalCivil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior Técnico, Department of Civil Engineering, Architecture and Environment, University of Lisbon, 1049-001 Lisbon, PortugalInstituto de Hidráulica y Saneamiento Ambiental, Universidad de Cartagena, Cartagena 130001, ColombiaHydraulic Engineering and Environmental Department, Universitat Politècnica de València, 46022 Valencia, SpainDepartment of Mechanical Engineering, Abdullah Gul University, 38080 Kayseri, TurkeySchool of Engineering, RMIT University, 124 La Trobe St., Melbourne, VIC 3000, AustraliaDepartment of Energy, University of Oviedo, CUIDA, 33007 Oviedo, SpainThis research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. It details new methodologies, including strategic energy management and a machine learning (ML) tool for predicting energy surplus and deficits. The hybrid energy module solution for the Port of Avilés was further developed to evaluate the performance of new tools such as the Energy Management Tool (EMTv1), HYbrid for Renewable Energy Solutions (HY4RES), and a commercial model (Hybrid Optimization of Multiple Energy Resources—HOMER) in optimizing renewable energy and storage management. Seven scenarios were analyzed, integrating different energy sources and storage solutions. Using EMTv1, Scenario 1 showed high surplus energy, while Scenario 2 demonstrated grid independence with Pump-as-Turbine (PAT) storage. The HY4RES model was used to analyze Scenario 3, which achieved a positive grid balance, exporting more than imported, and Scenario 4 revealed limitations of the PAT system due to the low power installed. Scenario 5 introduced a 15 kWh battery, efficiently storing and discharging energy, reducing grid reliance, and fully covering energy needs. Using HOMER modeling, Scenario 6 required 546 kWh of grid energy but sold 2385 kWh back. Scenario 7 produced 3450 kWh/year, covering demand, resulting in 1834 kWh of surplus energy and a small capacity shortage (1.41 kWh/year). AI-based ML analysis was applied to five scenarios (the ones with access to numerical results), accurately predicting energy balances and optimizing grid interactions. A neural network time series (NNTS) model trained on average year data achieved high accuracy (R<sup>2</sup>: 0.9253–0.9695). The ANN model proved effective in making rapid energy balance predictions, reducing the need for complex simulations. A second case analyzed an increase of 80% in demand, confirming the model’s reliability, with Scenario 3 having the highest MSE (0.0166 kWh), Scenario 2 the lowest R<sup>2</sup> (0.9289), and Scenario 5 the highest R<sup>2</sup> (0.9693) during the validation process. This study highlights AI-driven forecasting as a valuable tool for ports to optimize energy management, minimize grid dependency, and enhance their efficiency.https://www.mdpi.com/2076-3417/15/9/5211portsHY4REShybrid energy systemsport efficiencycarbon neutralitymicrogrid optimization |
| spellingShingle | Helena M. Ramos João S. T. Coelho Eyup Bekci Toni X. Adrover Oscar E. Coronado-Hernández Modesto Perez-Sanchez Kemal Koca Aonghus McNabola R. Espina-Valdés Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency Applied Sciences ports HY4RES hybrid energy systems port efficiency carbon neutrality microgrid optimization |
| title | Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency |
| title_full | Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency |
| title_fullStr | Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency |
| title_full_unstemmed | Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency |
| title_short | Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency |
| title_sort | optimization and machine learning in modeling approaches to hybrid energy balance to improve ports efficiency |
| topic | ports HY4RES hybrid energy systems port efficiency carbon neutrality microgrid optimization |
| url | https://www.mdpi.com/2076-3417/15/9/5211 |
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