Energy consumption prediction method based on LSSVM-PSO model for autonomous underwater gliders. (15th June 2021)

• Record Type: Journal Article
• Authors: Song, Yang; Xie, Xudong; Wang, Yanhui; Yang, Shaoqiong; Ma, Wei; Wang, Peng
• Is Part Of: Ocean engineering. Volume 230(2021)
• Publication Date: 2021-06-15
• Subjects: Autonomous underwater gliders -- Energy consumption prediction -- Least squares support vector machines -- Particle swarm optimization algorithm; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2021.108982
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: 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. Highlights: An intelligent energy consumption prediction model of underwater gliders is established. The optimal LSSVM-PSO model is obtained by comparing the performance of the LSSVM models based on different kernel functions. Compared with the mathematical energy consumption model, the LSSVM-PSO model can significantly improve prediction accuracy. A novel energy consumption prediction method combining the LSSVM-PSO model and Math-ECPM is proposed.