A Machine-Learning Approach to Predict Main Energy Consumption under Realistic Operational Conditions
Publication: Research › Journal article – Annual report year: 2012
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A Machine-Learning Approach to Predict Main Energy Consumption under Realistic Operational Conditions. / Petersen, Joan P; Winther, Ole; Jacobsen, Daniel J.
In: Ship Technology Research, Vol. 59, No. 1, 2012, p. 64-72.Publication: Research › Journal article – Annual report year: 2012
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TY - JOUR
T1 - A Machine-Learning Approach to Predict Main Energy Consumption under Realistic Operational Conditions
A1 - Petersen,Joan P
A1 - Winther,Ole
A1 - Jacobsen,Daniel J
AU - Petersen,Joan P
AU - Winther,Ole
AU - Jacobsen,Daniel J
PY - 2012
Y1 - 2012
N2 - The paper presents a novel and publicly available set of high-quality sensory data collected from a ferry over a period of two months and overviews exixting machine-learning methods for the prediction of main propulsion efficiency. Neural networks are applied on both real-time and predictive settings. Performance results for the real-time models are shown. The presented models were successfully developed in a trim optimisation application onboard a product tanker.
AB - The paper presents a novel and publicly available set of high-quality sensory data collected from a ferry over a period of two months and overviews exixting machine-learning methods for the prediction of main propulsion efficiency. Neural networks are applied on both real-time and predictive settings. Performance results for the real-time models are shown. The presented models were successfully developed in a trim optimisation application onboard a product tanker.
KW - Neural net
KW - Power prognosis
KW - Trim
JO - Ship Technology Research
JF - Ship Technology Research
SN - 0937-7255
IS - 1
VL - 59
SP - 64
EP - 72
ER -