基于LSTM网络的耙吸挖泥船能效分析评估模型  

Rare Absorbing Mud-digging Boat Energy Efficiency Analysis and Evaluation Model Based on LSTM Network

作　　者：张宁 朱晖宇 张宇凡 杜重洋 Zhang Ning;Zhu Huiyu;Zhang Yufan;Du Chongyang(The Eighth Military Representative Office of the Naval Armaments Division in Shanghai,Shanghai,China;Chinese Institute of Shipping and Marine Engineering Design,Shanghai,China;Shanghai Zhongchuan NERC-SDT Co.,Ltd.,Shanghai,China)

机构地区：[1]海装驻上海地区第八军事代表室,上海 [2]中国船舶及海洋工程设计研究院,上海 [3]上海中船船舶设计技术国家工程研究中心有限公司,上海

出　　处：《科学技术创新》2023年第18期47-50,共4页Scientific and Technological Innovation

摘　　要：疏浚船的能耗排放和能耗成本极高,为准确预测疏浚船在挖泥工况下的能耗,减少能量损耗,降低营运成本,本文以耙吸挖泥船为研究对象,提出了一种基于长短期记忆(LSTM)网络的疏浚船能效分析预测方法,利用传感器采集的实船数据,将疏浚作业能效进行参数化表达,建立能效分析预测与评估模型,为疏浚作业能效提供评估理论。仿真结果表明,该方法可以准确地预测出疏浚船能耗的变化趋势。The energy consumption emissions and energy consumption costs of dredging ships are extrem-ely high.In order to accurately predict the energy consumption of dredging ships under the condition of mud,reduce energy loss,and reduce operating costs.The energy efficiency analysis prediction method based on the long-term memory(LSTM)network is used to use the real ship data collected by the sensor to perform the energy efficiency of the dredging operations,establish an energy efficiency analysis prediction and evaluation model,and provide an evaluation theory for the energy efficiency of dredging operations.The simulation results show that this method can accurately predict the trend of energy consumption of dredging ships.

关 键 词：疏浚船 LSTM神经网络 能效分析预测 

分 类 号：U674.31[交通运输工程—船舶及航道工程]