Machine learning method for energy consumption prediction of ships in port considering green ports

Abstract

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.