Data-Driven Propulsion Load Profile Prediction for All-Electric Ships
- Resource Type
- Conference
- Authors
- Chen, Wenjie; Tai, Kang; Lau, Michael; Abdelhakim, Ahmed; Chan, Ricky R.; Adnanes, Alf Koare; Tjahjowidodo, Tegoeh
- Source
- 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022 International Conference on. :1-9 Nov, 2022
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Torque
Wind speed
Boats
Machine learning
Predictive models
Propulsion
Solids
Load prediction
Hybrid shipboard power system
All-electric ship (AES)
- Language
To achieve high-efficiency power and energy man-agement in a modern hybrid shipboard power system, an accurate shipload profile has become the pre-condition for opti-mization of the power and energy control. With the introduction of the all-electric ship concept, propulsion load is determined by the electrical power demand instead of the propeller mechanical torque. In this study, a data-driven load profile prediction model of the ship propulsion power is developed with machine learning methods. A typical ferry boat operation data is utilized as the case study to verify the prediction accuracy. The prediction model is approached via three different regression models and the predicted results are studied and compared. The marine power load forecasting can improve power system efficiency and reduce the energy cost through a better understanding of the power system demand pattern.