Machine Learning and Deep Learning for Photovoltaic Applications

Abstract

Artificial intelligence (AI) techniques including machine learning and deep learning algorithms have shown their capability in solving complex problems in different sectors such as, natural language processing, pattern recognition, forecasting, robotics, and other applications. Researchers working in the field of solar energy application both solar thermal and photovoltaic, are more interested to apply these techniques, and recently they are highly motivated especially by the DL algorithms usefulness. This chapter aims to show some applications of AI techniques, such as the k-nearest neighbours, neural networks, deep neural networks, fuzzy logic and long-short term memory networks, in photovoltaic (PV) systems. Various topics are covered such as current-voltage curves estimation, PV power forecasting, maximum power point tracking, and fault classification for PV systems. The examples presented are developed under the Matlab environment and Python programming language and show good prospects.