The rapid growth of demand for electrical energy and the depletion of fossil fuels opened the door for renewable energy; with solar energy being one of the most popular sources, as it is considered pollution free, freely available and requires minimal maintenance. This paper investigates the feasibility of using machine learning (ML) based MPPT techniques, to harness maximum power on a PV system under PSC. In this study, certain contributions to the field of PV systems and ML based systems were made by introducing nine (9) ML based MPPT techniques, by presenting three (3) experiments under different weather conditions. Decision Tree (DT), Multivariate Linear Regression (MLR), Gaussian Process Regression (GPR), Weighted K-Nearest Neighbors (WK-NN), Linear Discriminant Analysis (LDA), Bagged Tree (BT), Naïve Bayes classifier (NBC), Support Vector Machine (SVM) and Recurrent Neural Network (RNN) performances are validated and proved using MATLAB SIMULINK simulation software. The experimental results demonstrated that WK-NN performs significantly better when compared with other proposed ML based algorithms.

中文翻译：

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不同天气条件下光伏系统机器学习MPPT算法综合评估

电能需求的快速增长和化石燃料的枯竭为可再生能源打开了大门；太阳能是最受欢迎的来源之一，因为它被认为是无污染的、免费提供的并且需要最少的维护。本文研究了使用基于机器学习 (ML) 的 MPPT 技术在 PSC 下利用光伏系统的最大功率的可行性。在这项研究中，通过介绍九 (9) 种基于 ML 的 MPPT 技术，通过在不同天气条件下展示三 (3) 项实验，对光伏系统和基于 ML 的系统领域做出了某些贡献。决策树 (DT)、多元线性回归 (MLR)、高斯过程回归 (GPR)、加权 K-最近邻 (WK-NN)、线性判别分析 (LDA)、袋装树 (BT)、朴素贝叶斯分类器 (NBC) , 支持向量机 (SVM) 和循环神经网络 (RNN) 性能使用 MATLAB SIMULINK 仿真软件进行验证和证明。实验结果表明，与其他提出的基于 ML 的算法相比，WK-NN 的性能明显更好。