2 - Artificial intelligence techniques: Machine learning and deep learning algorithms

Abstract

In this chapter, an introduction to artificial intelligence techniques, including machine learning, ensemble learning, and deep learning, is presented. Several basic and comprehensive examples of different machine learning algorithms (such as support vector machine, K-nearest neighbors, linear regression, logistic regression, decision tree, random forest, K-means, fuzzy c-means, principal component analysis, hierarchical clustering, and neural networks), ensemble learning algorithms (like Bagging, Extra tree, AdaBoost, histogram-based gradient boosting and gradient boost, voting, and stacking), and deep learning algorithms (such as conventional neural networks, long short-term memory, and other hybrid configuration) are presented. The examples presented have been developed using Python programming language and offer a clear idea to readers on how to apply and use these algorithms for addressing photovoltaic problems. This chapter is not intended to provide the theoretical development of these algorithms, but it is focused on the application of these algorithms to solve certain photovoltaic systems problem.