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Artificial intelligence for photovoltaic systems

  • Rami Ghannam
  • , Paulo Valente Klaine
  • , Muhammad Imran
  • University of Glasgow

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

13 Scopus citations

Abstract

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods.

Original languageEnglish
Title of host publicationPower Systems
PublisherSpringer Verlag
Pages121-142
Number of pages22
DOIs
StatePublished - 2019
Externally publishedYes

Publication series

NamePower Systems
ISSN (Print)1612-1287
ISSN (Electronic)1860-4676

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

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