Data-Driven Prediction of Vessel Propulsion Power Using Support Vector Regression with Onboard Measurement and Ocean Data
The fluctuation of the oil price and the growing requirement to reduce greenhouse gas emissions have forced ship builders and shipping companies to improve the energy efficiency of the vessels. The accurate prediction of the required propulsion power at various operating condition is essential to ev...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/6/1588 |
| _version_ | 1818001499997863936 |
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| author | Donghyun Kim Sangbong Lee Jihwan Lee |
| author_facet | Donghyun Kim Sangbong Lee Jihwan Lee |
| author_sort | Donghyun Kim |
| collection | DOAJ |
| description | The fluctuation of the oil price and the growing requirement to reduce greenhouse gas emissions have forced ship builders and shipping companies to improve the energy efficiency of the vessels. The accurate prediction of the required propulsion power at various operating condition is essential to evaluate the energy-saving potential of a vessel. Currently, a new ship is expected to use the ISO15016 method in estimating added resistance induced by external environmental factors in power prediction. However, since ISO15016 usually assumes static water conditions, it may result in low accuracy when it is applied to various operating conditions. Moreover, it is time consuming to apply the ISO15016 method because it is computationally expensive and requires many input data. To overcome this limitation, we propose a data-driven approach to predict the propulsion power of a vessel. In this study, support vector regression (SVR) is used to learn from big data obtained from onboard measurement and the National Oceanic and Atmospheric Administration (NOAA) database. As a result, we show that our data-driven approach shows superior performance compared to the ISO15016 method if the big data of the solid line are secured. |
| first_indexed | 2024-04-14T03:35:03Z |
| format | Article |
| id | doaj.art-d6832d4160ac4200ad9c0dc2eae7a50d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T03:35:03Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d6832d4160ac4200ad9c0dc2eae7a50d2022-12-22T02:14:48ZengMDPI AGSensors1424-82202020-03-01206158810.3390/s20061588s20061588Data-Driven Prediction of Vessel Propulsion Power Using Support Vector Regression with Onboard Measurement and Ocean DataDonghyun Kim0Sangbong Lee1Jihwan Lee2Korea Marine Equipment Research Institute, Busan 49111, KoreaLab021, Busan 48508, KoreaDivision of Systems Management and Engineering, Pukyong National University, Busan 48513, KoreaThe fluctuation of the oil price and the growing requirement to reduce greenhouse gas emissions have forced ship builders and shipping companies to improve the energy efficiency of the vessels. The accurate prediction of the required propulsion power at various operating condition is essential to evaluate the energy-saving potential of a vessel. Currently, a new ship is expected to use the ISO15016 method in estimating added resistance induced by external environmental factors in power prediction. However, since ISO15016 usually assumes static water conditions, it may result in low accuracy when it is applied to various operating conditions. Moreover, it is time consuming to apply the ISO15016 method because it is computationally expensive and requires many input data. To overcome this limitation, we propose a data-driven approach to predict the propulsion power of a vessel. In this study, support vector regression (SVR) is used to learn from big data obtained from onboard measurement and the National Oceanic and Atmospheric Administration (NOAA) database. As a result, we show that our data-driven approach shows superior performance compared to the ISO15016 method if the big data of the solid line are secured.https://www.mdpi.com/1424-8220/20/6/1588vessel power predictiondata-driven predictionsupport vector regressioniso15016onboard measurement dataocean whether datapredictive analytics |
| spellingShingle | Donghyun Kim Sangbong Lee Jihwan Lee Data-Driven Prediction of Vessel Propulsion Power Using Support Vector Regression with Onboard Measurement and Ocean Data Sensors vessel power prediction data-driven prediction support vector regression iso15016 onboard measurement data ocean whether data predictive analytics |
| title | Data-Driven Prediction of Vessel Propulsion Power Using Support Vector Regression with Onboard Measurement and Ocean Data |
| title_full | Data-Driven Prediction of Vessel Propulsion Power Using Support Vector Regression with Onboard Measurement and Ocean Data |
| title_fullStr | Data-Driven Prediction of Vessel Propulsion Power Using Support Vector Regression with Onboard Measurement and Ocean Data |
| title_full_unstemmed | Data-Driven Prediction of Vessel Propulsion Power Using Support Vector Regression with Onboard Measurement and Ocean Data |
| title_short | Data-Driven Prediction of Vessel Propulsion Power Using Support Vector Regression with Onboard Measurement and Ocean Data |
| title_sort | data driven prediction of vessel propulsion power using support vector regression with onboard measurement and ocean data |
| topic | vessel power prediction data-driven prediction support vector regression iso15016 onboard measurement data ocean whether data predictive analytics |
| url | https://www.mdpi.com/1424-8220/20/6/1588 |
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