首页|Reports from Wuhan University of Technology Highlight Recent Findings in Machine Learning (Comprehensive Evaluation of Machine Learning Models for Predicting Ship Energy Consumption Based On Onboard Sensor Data)
Reports from Wuhan University of Technology Highlight Recent Findings in Machine Learning (Comprehensive Evaluation of Machine Learning Models for Predicting Ship Energy Consumption Based On Onboard Sensor Data)
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Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Wuhan, People's Republic of China, by NewsRx journalists, research stated, “Machine learning models for predicting ship energy consumption are built and their influencing factors are investigated. First, data collected from a real ship is preprocessed.” 102 Financial supporters for this research include National Key R & D Program of China, National Natural Science Foundation of China (NSFC), Weichai Power Co., Ltd. technology project, Green Intelligent Inland Ship Innovation Programme of China. The news reporters obtained a quote from the research from the Wuhan University of Technology, “Six machine learning methods are used to establish the prediction models of ship fuel consumption, and the performance of models is evaluated by Mean Absolute Error, Coefficient of Determination and training time. Then, by analysing the correlation and impor-tance of the features, it's studied whether the model established complies with the laws of physics. Finally, the factors affecting the prediction performance of machine learning models are analysed. The results show that Random Forest and Extreme Gradient Boosting are the most suitable algorithms for ship fuel consumption prediction. Data preprocessing, data normalisation, training sample size, model type, ship operating conditions, as well as the thermotechnical parameters of main engine have impact on the prediction performance.”
WuhanPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningWuhan University of Technology