Implementation of novel Machine Learning Technique using several Meta with Naive Bayes Models to Analyse the Performance of Wave Energy Converters

Abstract

Optimization of the layouts of arrays of wave energy converters (WECs) is a challenging problem. The hydrodynamic analysis and performance estimation of such systems are performed using semi-analytical and numerical models such as the boundary element method. However, the analysis of an array of such converters becomes computationally expensive, and the computational time increases rapidly with the number of devices in the system. As such determination of optimal layouts of WECs in arrays becomes extremely difficult. This paper explores the Ada Boost with Naïve Bayes perform well as well it showing an efficient outcome. It has the greatest accuracy result of 85.75%. The Ada Boost with Naïve Bayes produces the greatest precision result of 0.86. The Ada Boost with Naïve Bayes and Stochastic Gradient Boost with Naïve Bayes produce the maximum recall of 0.86. The Ada Boost with Naïve Bayes has the greatest F-Measure result of 0.86. The Ada Boost with Naïve Bayes model has the highest MCC value of 0.65. The Ada Boost with Naïve Bayes model has the greatest kappa value of 0.66. The Ada Boost with Naïve Bayes model has an optimal results compare with other models.