Machine learning method for energy consumption prediction of ships in port considering green ports
Machine learning method for energy consumption prediction of ships in port considering green ports
复制标题
考虑绿色港口的港口船舶能耗预测机器学习方法
DOI：
10.1016/j.jclepro.2020.121564
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发表时间：
2020-08
影响因子：
11.1
通讯作者：
Wang Wenyuan
中科院分区：
文献类型：
--
作者：
Peng Yun;Liu Huakun;Li Xiangda;Huang Jian;Wang Wenyuan
本文的两个主要贡献是：1)对港口船舶能源消耗进行了预测；2)通过提出的考虑绿色港口的港口船舶能源消耗预测模型，探讨了港口船舶能源消耗的降低策略。首先，收集了中国京唐港影响船舶能耗的15个特征并进行了分析。然后，建立了梯度增强回归（GBR）、随机森林回归（RF）、BP网络（BP）、线性回归（LR）和k -最近邻回归（KNN） 5种机器学习模型，并将船舶固有属性和港口外部特征组成的15个特征作为输入。然后，采用k-fold交叉验证来验证模型的有效性。最后，计算特征的重要度，选择最重要的特征。此外，通过实验研究了改变若干特征对船舶能耗的影响，并讨论了两种降低能耗的策略。结果表明，净吨位、载重吨位、实际重量和设施效率是预测船舶能耗的前4个特征。综上所述，当设施效率提高一倍时，船舶泊位能耗降低34.17%，港口能耗降低8.41%。所提出的方法和讨论的策略的发现可为绿色港口的建设提供参考。
Two main contributions of this paper are 1) the energy consumption of ships (ECS) in port is predicted, 2) reduction strategies for energy consumption of ships in port are discussed by the proposed prediction models considering green port. Firstly, 15 characteristics which have impact on the energy consumption of ships are collected by Jingtang Port in China and analysis is conducted. Then, five machine learning models including Gradient Boosting Regression (GBR), Random Forest Regression (RF), BP Network (BP), Liner Regression (LR) and K-Nearest Neighbor Regression (KNN) are developed and 15 features consisting of inherent property of ship and external features of ports are set as inputs. After then, k-folds cross validation is adopted to verify the effectiveness of models. Finally, the feature importance is calculated and the most important features are selected. Besides, experiments are conducted to find the effect of changing several features on energy consumption of ships, and two consumption reduction strategies are discussed. The results show that net tonnage, deadweight tonnage, actual weight and efficiency of facilities are the top 4 features for predicting the energy consumption of ships. In conclusion, when efficiency of facilities is doubled, the energy consumption of ships is reduced by 34.17% at berth and 8.41% in port. The finding of proposed methods and discussed strategies can give references to green port construction.
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影响因子：
9
作者：
R. Winkel;U. Weddige;D. Johnsen;V. Hoen;S. Papaefthimiou
通讯作者：
R. Winkel;U. Weddige;D. Johnsen;V. Hoen;S. Papaefthimiou
影响因子：
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作者：
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DOI：
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发表时间：
2018-10
期刊：
Energy Procedia
影响因子：
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作者：
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DOI：
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发表时间：
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期刊：
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影响因子：
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作者：
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DOI：
10.1109/cac.2018.8623540
发表时间：
2018-11
期刊：
2018 Chinese Automation Congress (CAC)
影响因子：
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作者：
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通讯作者：
Beiyan Jiang;Zhijin Cheng;Qianting Hao;Nan Ma