Online ship speed optimization based on BiLSTM encoder-decoder |
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| Institution: | 1. School of Artificial Intelligence and Automation, Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan, China;2. China Ship Development and Design Center, Wuhan, China;1. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China;2. School of Vehicle and Mobility, Tsinghua University, Bejing 100084, China;1. School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong 266520, PR China;2. School of Engineering, Qufu Normal University, Rizhao, Shandong 276826, PR China;3. School of Automation, Southeast University, Nanjing, Jiangsu 210096, PR China;1. ZNDY Ministerial Key Laboratory, School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China;2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Beijing Beijing, 10083, China;3. School of Mechatronical Engineering, Beijing Institute of Technology, No. 5, South Street, Beijing, Beijing, 100081, China;1. Key Laboratory of Intelligent Analysis and Decision on Complex Systems, School of Science, Chongqing University of Posts and Telecommunications, Chongqing, PR China;2. Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, PR China;3. Department of Complexity Science, Potsdam Institute for Climate Impact Research, Potsdam, Germany;4. Institute of Physics, Humboldt University of Berlin, Berlin, Germany;1. Dept. of Fire Protection Engineering, Pukyong National University, Busan, Korea;2. Dept. of Electrical Engineering, Dong-A University, Busan, Korea |
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| Abstract: | Ship speed optimization can reduce fuel consumption and protect the environment. The existing offline speed optimization model cannot ensure the optimal ship speed due to the changing weather and sea condition during the voyage. We propose an online speed optimization model which consists of an offline speed optimization model and a fuel consumption prediction model. The online speed optimization model is to plan the average speed for each segment of the entire voyage. The fuel consumption prediction model is used to continuously monitor the fuel consumption during the voyage. For fuel consumption prediction, we propose a bidirectional long short-term memory (BiLSTM) based encoder-decoder with temporal attention mechanism. By the comparative experiments with three baseline models, the accuracy of the proposed prediction model is verified. |
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