Artificial intelligence for photovoltaic systems

Ghannam, R. , Valente Klaine, P. and Imran, M. (2019) Artificial intelligence for photovoltaic systems. In: Precup, R.-E., Kamal, T. and Hassan, S. Z. (eds.) Solar Photovoltaic Power Plants: Advanced Control and Optimization Techniques. Series: Power systems. Springer: Singapore, pp. 121-142. ISBN 9789811361500 (doi: 10.1007/978-981-13-6151-7_6)

[img]
Preview
Text
169685.pdf - Accepted Version

657kB

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.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Ghannam, Professor Rami and Imran, Professor Muhammad and Valente Klaine, Mr Paulo
Authors: Ghannam, R., Valente Klaine, P., and Imran, M.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Publisher:Springer
ISBN:9789811361500
Published Online:08 February 2019
Copyright Holders:Copyright © 2019 Springer Nature Singapore Pte Ltd.
First Published:First published in Solar Photovoltaic Power Plants: Advanced Control and Optimization Techniques: 121-142
Publisher Policy:Reproduced in accordance with the publisher copyright policy

University Staff: Request a correction | Enlighten Editors: Update this record