About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
ew 24(1):

Editorial

Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting

Download102 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/ew.4653,
        author={Haotian Chen and Xixia Huang},
        title={Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={11},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2024},
        month={4},
        keywords={composite electric power system, ship, energy management, model predictive control, power prediction, neural network, variational modal decomposition},
        doi={10.4108/ew.4653}
    }
    
  • Haotian Chen
    Xixia Huang
    Year: 2024
    Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting
    EW
    EAI
    DOI: 10.4108/ew.4653
Haotian Chen1,*, Xixia Huang1
  • 1: Shanghai Maritime University
*Contact email: haotianch@outlook.com

Abstract

A proposed solution is presented to address the issue of rising energy loss resulting from inaccurate power prediction in the predictive energy management strategy for composite electric power electric ship. The solution involves the development of a power prediction model that integrates Archimedes' algorithm, optimized variational modal decomposition, and BiLSTM. Within the framework of Model Predictive Control, this predictive model is utilized for power forecasting, transforming the global optimization problem into one of optimizing the power output distribution among power sources within the predictive time domain, then the optimization objective is to minimize the energy loss of the composite electric power system, and a dynamic programming algorithm is employed to solve the optimization problem within the forecast time domain. The simulation findings demonstrate a significant enhancement in the forecast accuracy of the power prediction model introduced in this study, with a 52.61% improvement compared to the AOA-BiLSTM power prediction model. Concurrently, the energy management strategy utilizing the prediction model proposed in this research shows a 1.02% reduction in energy loss compared to the prediction model control strategy based on AOA-BiLSTM, and a 15.8% reduction in energy loss compared to the ruler-based strategy.

Keywords
composite electric power system, ship, energy management, model predictive control, power prediction, neural network, variational modal decomposition
Received
2023-12-20
Accepted
2024-03-26
Published
2024-04-03
Publisher
EAI
http://dx.doi.org/10.4108/ew.4653

Copyright © 2024 H. Chen et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL