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Energy consumption prediction method based on LSSVM-PSO model for autonomous underwater gliders

机译：基于LSSVM-PSO模型的能耗预测方法自主水下滑翔机

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摘要

Currently, most autonomous underwater gliders (AUGs) operate on primary lithium batteries. As the state of charge of a primary lithium battery and the influence of marine environment on the glider are difficult to measure, it is hard to forecast the energy consumption of a glider accurately, which has caused the failure of many glider missions. For the purpose of safely deploying the AUG mission and effectively optimizing the motion parameters to increase the endurance, it is very important to make an accurate energy consumption prediction model of the AUG. In this paper, a novel model based on the least squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithm, namely the LSSVM-PSO model, is proposed to forecast the energy consumption of the AUG. Considering that the kernel function and the LSSVM related parameters have a great influence on the performance of the prediction model, several LSSVM models based on different kernel functions for energy consumption prediction are established, and the parameters are optimized by the PSO algorithm. The performance of LSSVM- PSO models with different kernel functions are compared based on the sea trial data. The results indicate that the LSSVM-PSO model with a radial basis kernel function has a higher accuracy than other models for energy consumption prediction. Moreover, the performance of the LSSVM-PSO model trained by different sample sizes and that of the conventional mathematical energy consumption prediction model are compared. The results demonstrate that the LSSVM-PSO model is superior with a large enough training sample size.
机译：目前，大多数自主水下滑翔机（AUGS）在原发性锂电池上运行。作为主要锂电池的充电状态和海洋环境对滑翔机的影响很难测量，很难预测滑翔机的能耗，这使得许多滑翔机任务失败。为了安全地部署八月任务并有效地优化运动参数以增加耐力，使8月的精确能耗预测模型非常重要。本文采用基于最小二乘支持向量机（LSSVM）和粒子群优化（PSO）算法的新型模型，即LSSVM-PSO模型，以预测8月的能源消耗。考虑到内核功能和LSSVM相关参数对预测模型的性能产生很大影响，建立了基于不同内核函数的几个LSSVM模型进行了用于能量消耗预测，并且参数由PSO算法进行优化。基于海上试验数据比较了具有不同内核功能的LSSVM-PSO模型的性能。结果表明，具有径向基础内核功能的LSSVM-PSO模型具有比其他能耗预测模型更高的精度。此外，比较了由不同样本大小训练的LSSVM-PSO模型的性能以及传统的数学能量消耗预测模型的性能。结果表明，LSSVM-PSO模型具有足够大的训练样本大小。

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  • 来源
    《Oceanographic Literature Review》 |2021年第6期|1379-1379|共1页
  • 作者

    Y. Song; X. Xie; Y. Wang;

  • 作者单位

    School of Mechanical Engineering Tianjin University Tianjin China;

    School of Mechanical Engineering Tianjin University Tianjin China;

    School of Mechanical Engineering Tianjin University Tianjin China;

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  • 正文语种 eng
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