Classification Techniques for Renewable Energy: Identifying Renewable Energy Sources and Features

Abstract

In this chapter, the authors take a look at the many ways in which renewable energy sources can be classified according to their unique characteristics. To categorize renewable energy sources according to their unique characteristics, researchers use tools including GIS, statistical methodologies, and machine learning algorithms. Automatically classifying renewable energy sources using past data and pertinent parameters is a strong suit of machine learning techniques like NN, DT, and SVM. Clustering and PCA are two examples of the statistical methods used to classify and group similar forms of renewable energy by identifying patterns and similarities among them. Among many components that make up renewable energy sources are solar, wind, hydropower, geothermal, and biomass power. The renewable energy sector can speed up the shift to a low-carbon energy future, maximize resource use, and make better decisions with the help of machine learning algorithms, statistical approaches, and geographic information systems.