Comparative analysis of Direct and Indirect Model Reference Adaptive Control by Extended Kalman Filter
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Smart Wires and Modular FACTS Controllers for Smart Grid Applications: A Review
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Integrated Renewable Energy Management System for Reduced Hydrogen Consumption using Fuel Cell
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Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
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Artificial Intelligence based Business Process Automation for Enhanced Knowledge Management
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Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
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Design of Effective Smart Communication System for Impaired People
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Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
Volume-3 | Issue-3
Prediction of Energy Consumption by Ships at the port using Deep Learning
Volume-3 | Issue-2
A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
Volume-3 | Issue-4
Power Transfer Capability Recognition in Deregulated System under Line Outage Condition Using Power World Simulator
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Transformer Oil Diagnostic Tests Analysis using Statistical Correlation Technique
Volume-4 | Issue-3
Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model
Volume-3 | Issue-4
Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
Volume-3 | Issue-3
Performance Analysis of Multiple Pico Hydro Power Generation
Volume-2 | Issue-2
Energy Efficient Data Mining Approach for Estimating the Diabetes
Volume-3 | Issue-2
Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
Volume-3 | Issue-3
Prediction of Energy Consumption by Ships at the port using Deep Learning
Volume-3 | Issue-2
A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
Volume-3 | Issue-4
Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
Volume-3 | Issue-2
Volume - 3 | Issue - 2 | june 2021
Published
31 July, 2021
The harbours using green ports have become a common mode of enabling the use of environment friendly energy consumption. In this paper, two major contributions are made: reduction of energy consumption in the ports by using ships; prediction of energy consumption with respect to a green port. The characteristics that will play a crucial role in energy consumption of ships are considered and a detailed analysis has been performed to predict the energy consumed by the ships. Deep learning methodologies such as, K-Nearest Regression (KNR), Linear Regression (LR), BP Network (BP), Random Forest Regression (RF) and Gradient Boosting Regression (GBR) are used to determine the different characteristics of the ships that are used while the external features of the ports are given as input. To determine the efficiency of the proposed work, k-fold cross validation is also incorporated. Based on feature importance, the crucial features of the algorithm are selected. The influence of different changing aspects on the ship's energy usage is identified, and reduction methods are implemented appropriately. According to the observed data, the most essential factors that may be utilised to estimate energy consumption of the ship are efficiency of facilities, actual weight, deadweight tonnage, and net tonnage. As the efficiency increases, there is also a significant reduction and the power consumption of the ship at the rate of 8% and 32% in port and berth respectively.
KeywordsEnergy consumption strategies machine learning deep learning green ports regression
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