Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption

Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processin...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Cai, Zhiyuan [verfasserIn] Li, Lecheng [verfasserIn] Yu, Long [verfasserIn] Li, Congbo [verfasserIn] Sun, Miao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Fuel consumption prediction model Multi-source data processing Long short-term memory Black-box model Gray-box model

Übergeordnetes Werk:	Enthalten in: Ocean engineering - Amsterdam [u.a.] : Elsevier Science, 1970, 291
Übergeordnetes Werk:	volume:291

DOI / URN:	10.1016/j.oceaneng.2023.116434

Katalog-ID:	ELV066344018

Internformat


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520			\|a Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processing method for obtaining high-quality training data. A long short-term memory (LSTM) neural network, suitable for time-series data, was used to develop the fuel consumption black-box model. This was combined with the ship theory to establish a fuel consumption theoretical model, thereby generating the LSTM based gray-box model. We explored the impact of data diversity, quality, and quantity on black-box and gray-box models. Analysis of the navigation data of a passenger ship and meteorological data collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) and MeteoBlue indicated that the combination of variables obtained via the feature selection of the least absolute shrinkage and selection operator (LASSO) statistical method yielded the best overall prediction performance. Moreover, the gray-box model was relatively stable in terms of the changes in effective variables. An analysis of data quality revealed that the systematic processing of outliers, which improves the accuracy of both models by 6.19% compared with direct deletion. Furthermore, the gray-box models use less amounts of data than the black-box models to achieve higher accuracy.
650		4	\|a Fuel consumption prediction model
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650		4	\|a Gray-box model
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700	1		\|a Li, Congbo \|e verfasserin \|4 aut
700	1		\|a Sun, Miao \|e verfasserin \|4 aut
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allfields	10.1016/j.oceaneng.2023.116434 doi (DE-627)ELV066344018 (ELSEVIER)S0029-8018(23)02818-4 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Cai, Zhiyuan verfasserin (orcid)0000-0002-7096-6658 aut Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processing method for obtaining high-quality training data. A long short-term memory (LSTM) neural network, suitable for time-series data, was used to develop the fuel consumption black-box model. This was combined with the ship theory to establish a fuel consumption theoretical model, thereby generating the LSTM based gray-box model. We explored the impact of data diversity, quality, and quantity on black-box and gray-box models. Analysis of the navigation data of a passenger ship and meteorological data collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) and MeteoBlue indicated that the combination of variables obtained via the feature selection of the least absolute shrinkage and selection operator (LASSO) statistical method yielded the best overall prediction performance. Moreover, the gray-box model was relatively stable in terms of the changes in effective variables. An analysis of data quality revealed that the systematic processing of outliers, which improves the accuracy of both models by 6.19% compared with direct deletion. Furthermore, the gray-box models use less amounts of data than the black-box models to achieve higher accuracy. Fuel consumption prediction model Multi-source data processing Long short-term memory Black-box model Gray-box model Li, Lecheng verfasserin aut Yu, Long verfasserin (orcid)0000-0002-9177-7741 aut Li, Congbo verfasserin aut Sun, Miao verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 291 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:291 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 291
spelling	10.1016/j.oceaneng.2023.116434 doi (DE-627)ELV066344018 (ELSEVIER)S0029-8018(23)02818-4 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Cai, Zhiyuan verfasserin (orcid)0000-0002-7096-6658 aut Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processing method for obtaining high-quality training data. A long short-term memory (LSTM) neural network, suitable for time-series data, was used to develop the fuel consumption black-box model. This was combined with the ship theory to establish a fuel consumption theoretical model, thereby generating the LSTM based gray-box model. We explored the impact of data diversity, quality, and quantity on black-box and gray-box models. Analysis of the navigation data of a passenger ship and meteorological data collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) and MeteoBlue indicated that the combination of variables obtained via the feature selection of the least absolute shrinkage and selection operator (LASSO) statistical method yielded the best overall prediction performance. Moreover, the gray-box model was relatively stable in terms of the changes in effective variables. An analysis of data quality revealed that the systematic processing of outliers, which improves the accuracy of both models by 6.19% compared with direct deletion. Furthermore, the gray-box models use less amounts of data than the black-box models to achieve higher accuracy. Fuel consumption prediction model Multi-source data processing Long short-term memory Black-box model Gray-box model Li, Lecheng verfasserin aut Yu, Long verfasserin (orcid)0000-0002-9177-7741 aut Li, Congbo verfasserin aut Sun, Miao verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 291 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:291 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 291
allfields_unstemmed	10.1016/j.oceaneng.2023.116434 doi (DE-627)ELV066344018 (ELSEVIER)S0029-8018(23)02818-4 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Cai, Zhiyuan verfasserin (orcid)0000-0002-7096-6658 aut Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processing method for obtaining high-quality training data. A long short-term memory (LSTM) neural network, suitable for time-series data, was used to develop the fuel consumption black-box model. This was combined with the ship theory to establish a fuel consumption theoretical model, thereby generating the LSTM based gray-box model. We explored the impact of data diversity, quality, and quantity on black-box and gray-box models. Analysis of the navigation data of a passenger ship and meteorological data collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) and MeteoBlue indicated that the combination of variables obtained via the feature selection of the least absolute shrinkage and selection operator (LASSO) statistical method yielded the best overall prediction performance. Moreover, the gray-box model was relatively stable in terms of the changes in effective variables. An analysis of data quality revealed that the systematic processing of outliers, which improves the accuracy of both models by 6.19% compared with direct deletion. Furthermore, the gray-box models use less amounts of data than the black-box models to achieve higher accuracy. Fuel consumption prediction model Multi-source data processing Long short-term memory Black-box model Gray-box model Li, Lecheng verfasserin aut Yu, Long verfasserin (orcid)0000-0002-9177-7741 aut Li, Congbo verfasserin aut Sun, Miao verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 291 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:291 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 291
allfieldsGer	10.1016/j.oceaneng.2023.116434 doi (DE-627)ELV066344018 (ELSEVIER)S0029-8018(23)02818-4 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Cai, Zhiyuan verfasserin (orcid)0000-0002-7096-6658 aut Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processing method for obtaining high-quality training data. A long short-term memory (LSTM) neural network, suitable for time-series data, was used to develop the fuel consumption black-box model. This was combined with the ship theory to establish a fuel consumption theoretical model, thereby generating the LSTM based gray-box model. We explored the impact of data diversity, quality, and quantity on black-box and gray-box models. Analysis of the navigation data of a passenger ship and meteorological data collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) and MeteoBlue indicated that the combination of variables obtained via the feature selection of the least absolute shrinkage and selection operator (LASSO) statistical method yielded the best overall prediction performance. Moreover, the gray-box model was relatively stable in terms of the changes in effective variables. An analysis of data quality revealed that the systematic processing of outliers, which improves the accuracy of both models by 6.19% compared with direct deletion. Furthermore, the gray-box models use less amounts of data than the black-box models to achieve higher accuracy. Fuel consumption prediction model Multi-source data processing Long short-term memory Black-box model Gray-box model Li, Lecheng verfasserin aut Yu, Long verfasserin (orcid)0000-0002-9177-7741 aut Li, Congbo verfasserin aut Sun, Miao verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 291 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:291 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 291
allfieldsSound	10.1016/j.oceaneng.2023.116434 doi (DE-627)ELV066344018 (ELSEVIER)S0029-8018(23)02818-4 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Cai, Zhiyuan verfasserin (orcid)0000-0002-7096-6658 aut Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processing method for obtaining high-quality training data. A long short-term memory (LSTM) neural network, suitable for time-series data, was used to develop the fuel consumption black-box model. This was combined with the ship theory to establish a fuel consumption theoretical model, thereby generating the LSTM based gray-box model. We explored the impact of data diversity, quality, and quantity on black-box and gray-box models. Analysis of the navigation data of a passenger ship and meteorological data collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) and MeteoBlue indicated that the combination of variables obtained via the feature selection of the least absolute shrinkage and selection operator (LASSO) statistical method yielded the best overall prediction performance. Moreover, the gray-box model was relatively stable in terms of the changes in effective variables. An analysis of data quality revealed that the systematic processing of outliers, which improves the accuracy of both models by 6.19% compared with direct deletion. Furthermore, the gray-box models use less amounts of data than the black-box models to achieve higher accuracy. Fuel consumption prediction model Multi-source data processing Long short-term memory Black-box model Gray-box model Li, Lecheng verfasserin aut Yu, Long verfasserin (orcid)0000-0002-9177-7741 aut Li, Congbo verfasserin aut Sun, Miao verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 291 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:291 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 291
language	English
source	Enthalten in Ocean engineering 291 volume:291
sourceStr	Enthalten in Ocean engineering 291 volume:291
format_phy_str_mv	Article
bklname	Meerestechnik
institution	findex.gbv.de
topic_facet	Fuel consumption prediction model Multi-source data processing Long short-term memory Black-box model Gray-box model
dewey-raw	690
isfreeaccess_bool	false
container_title	Ocean engineering
authorswithroles_txt_mv	Cai, Zhiyuan @@aut@@ Li, Lecheng @@aut@@ Yu, Long @@aut@@ Li, Congbo @@aut@@ Sun, Miao @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	30658977X
dewey-sort	3690
id	ELV066344018
language_de	englisch
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author	Cai, Zhiyuan
spellingShingle	Cai, Zhiyuan ddc 690 bkl 50.92 misc Fuel consumption prediction model misc Multi-source data processing misc Long short-term memory misc Black-box model misc Gray-box model Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption
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topic_title	690 VZ 50.92 bkl Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption Fuel consumption prediction model Multi-source data processing Long short-term memory Black-box model Gray-box model
topic	ddc 690 bkl 50.92 misc Fuel consumption prediction model misc Multi-source data processing misc Long short-term memory misc Black-box model misc Gray-box model
topic_unstemmed	ddc 690 bkl 50.92 misc Fuel consumption prediction model misc Multi-source data processing misc Long short-term memory misc Black-box model misc Gray-box model
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title	Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption
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title_full	Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption
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title_sort	diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption
title_auth	Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption
abstract	Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processing method for obtaining high-quality training data. A long short-term memory (LSTM) neural network, suitable for time-series data, was used to develop the fuel consumption black-box model. This was combined with the ship theory to establish a fuel consumption theoretical model, thereby generating the LSTM based gray-box model. We explored the impact of data diversity, quality, and quantity on black-box and gray-box models. Analysis of the navigation data of a passenger ship and meteorological data collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) and MeteoBlue indicated that the combination of variables obtained via the feature selection of the least absolute shrinkage and selection operator (LASSO) statistical method yielded the best overall prediction performance. Moreover, the gray-box model was relatively stable in terms of the changes in effective variables. An analysis of data quality revealed that the systematic processing of outliers, which improves the accuracy of both models by 6.19% compared with direct deletion. Furthermore, the gray-box models use less amounts of data than the black-box models to achieve higher accuracy.
abstractGer	Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processing method for obtaining high-quality training data. A long short-term memory (LSTM) neural network, suitable for time-series data, was used to develop the fuel consumption black-box model. This was combined with the ship theory to establish a fuel consumption theoretical model, thereby generating the LSTM based gray-box model. We explored the impact of data diversity, quality, and quantity on black-box and gray-box models. Analysis of the navigation data of a passenger ship and meteorological data collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) and MeteoBlue indicated that the combination of variables obtained via the feature selection of the least absolute shrinkage and selection operator (LASSO) statistical method yielded the best overall prediction performance. Moreover, the gray-box model was relatively stable in terms of the changes in effective variables. An analysis of data quality revealed that the systematic processing of outliers, which improves the accuracy of both models by 6.19% compared with direct deletion. Furthermore, the gray-box models use less amounts of data than the black-box models to achieve higher accuracy.
abstract_unstemmed	Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processing method for obtaining high-quality training data. A long short-term memory (LSTM) neural network, suitable for time-series data, was used to develop the fuel consumption black-box model. This was combined with the ship theory to establish a fuel consumption theoretical model, thereby generating the LSTM based gray-box model. We explored the impact of data diversity, quality, and quantity on black-box and gray-box models. Analysis of the navigation data of a passenger ship and meteorological data collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) and MeteoBlue indicated that the combination of variables obtained via the feature selection of the least absolute shrinkage and selection operator (LASSO) statistical method yielded the best overall prediction performance. Moreover, the gray-box model was relatively stable in terms of the changes in effective variables. An analysis of data quality revealed that the systematic processing of outliers, which improves the accuracy of both models by 6.19% compared with direct deletion. Furthermore, the gray-box models use less amounts of data than the black-box models to achieve higher accuracy.
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title_short	Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption
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author2	Li, Lecheng Yu, Long Li, Congbo Sun, Miao
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doi_str	10.1016/j.oceaneng.2023.116434
up_date	2024-07-06T17:25:07.500Z
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Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption

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