Solar Irradiance: Types and Applications

152 Pages·2020·11.036 MB·English

by  Daryl M. Welsh

ADVANCES IN ASTRONOMY AND ASTROPHYSICS

S I
OLAR RRADIANCE

T A
YPES AND PPLICATIONS

No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or
by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no
expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No
liability is assumed for incidental or consequential damages in connection with or arising out of information
contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in
rendering legal, medical or any other professional services.
A A
DVANCES IN STRONOMY
A
AND STROPHYSICS

Additional books and e-books in this series can be found
on Nova’s website under the Series tab.
ADVANCES IN ASTRONOMY AND ASTROPHYSICS

S I
OLAR RRADIANCE

T A
YPES AND PPLICATIONS

DARYL M. WELSH
EDITOR
Copyright © 2020 by Nova Science Publishers, Inc.

All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form
or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without
the written permission of the Publisher.

We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content
from this publication. Simply navigate to this publication’s page on Nova’s website and locate the “Get Permission”
button below the title description. This button is linked directly to the title’s permission page on copyright.com.
Alternatively, you can visit copyright.com and search by title, ISBN, or ISSN.

For further questions about using the service on copyright.com, please contact:
Copyright Clearance Center
Phone: [phone] Fax: [phone] E-mail: [email protected].

NOTICE TO THE READER
The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty
of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or
consequential damages in connection with or arising out of information contained in this book. The Publisher shall not
be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of,
or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is
claimed for those parts to the extent applicable to compilations of such works.

Independent verification should be sought for any data, advice or recommendations contained in this book. In
addition, no responsibility is assumed by the Publisher for any injury and/or damage to persons or property arising
from any methods, products, instructions, ideas or otherwise contained in this publication.

This publication is designed to provide accurate and authoritative information with regard to the subject matter
covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other
professional services. If legal or any other expert assistance is required, the services of a competent person should be
sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE
AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS.

Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data

Names: Welsh, Daryl M., editor.
Title: Solar irradiance : types and applications / Daryl M. Welsh. Description: New York : Nova Science Publishers, [2020] | Series:
Advances in astronomy and astrophysics | Includes bibliographical references and index. | Summary: "Solar Irradiance: Types and
Applications first presents intelligent models for sizing, parameters forecasting and control of a photovoltaic system on the basis of a
modified fuzzy neural net. The modified fuzzy neural net provides automatic fulfillment and modification of all proposed intelligent
models. Following this, the authors discuss modeling direct normal irradiance at the Earth's surface. In addition to looking traditionally at
direct normal irradiance as a fuel for concentrating solar systems, its use in computing the sunshine number is also explored. The closing
study explores the potential of using simple empirical and artificial neural network models to estimate global solar radiation on a
horizontal surface. Algeria was used as a case study and four statistical parameters were chosen to assess the performances of each model
or approach"-- Provided by publisher.
Identifiers: LCCN 2020043982 (print) | LCCN 2020043983 (ebook) | ISBN 9781536187861 (paperback) | ISBN 9781536187991 (adobe
pdf) Subjects: LCSH: Solar energy. | Solar radiation--Computer simulation. | Solar radiation--Measurement.
Classification: LCC TJ810 .S663 2020 (print) | LCC TJ810 (ebook) | DDC 621.47--dc23
LC record available at https://lccn.loc.gov/2020043982
LC ebook record available at https://lccn.loc.gov/2020043983

Published by Nova Science Publishers, Inc. † New York
CONTENTS

Preface vii
Chapter 1 Photovoltaic Applications on the
Basis of Modified Fuzzy Neural Net 1
Ekaterina A. Engel
Chapter 2 Direct Normal Irradiance:
Measurement, Modeling and Applications 81
Marius Paulescu
Chapter 3 Potential Assessment of Global Solar Radiation
Resources Based on Empirical and ANN Models:
Algeria as a Case Study 119
T. E. Boukelia, M. S. Mecibah,
A. Ghellab and A. Bouraoui
Index 135

PREFACE

Solar Irradiance: Types and Applications first presents intelligent
models for sizing, parameters forecasting and control of a photovoltaic
system on the basis of a modified fuzzy neural net. The modified fuzzy
neural net provides automatic fulfillment and modification of all proposed
intelligent models.
Following this, the authors discuss modeling direct normal irradiance
at the Earth’s surface. In addition to looking traditionally at direct normal
irradiance as a fuel for concentrating solar systems, its use in computing
the sunshine number is also explored.
The closing study explores the potential of using simple empirical and
artificial neural network models to estimate global solar radiation on a
horizontal surface. Algeria was used as a case study and four statistical
parameters were chosen to assess the performances of each model or
approach.
Chapter 1 - The long-term contribution of solar energy is predicated on
overcoming remaining technical barriers, mainly through research and
development based on artificial intelligence. Data mining and machine
learning methods gained a great attention in photovoltaic applications. The
real-life photovoltaic systems have complex non-linear dynamic due to
random variation of the system parameters and fluctuation of the solar
irradiance. Thus, neural-network-based solutions have been proposed to
approximate this complex dynamic. But the neuronet needs to become
viii Daryl M. Welsh
more adaptive. Modification of the network into a recurrent neuronet with
fuzzy units provides intelligent behavior. This forms the motivation for the
development of intelligent models for sizing, parameters forecasting and
control of a photovoltaic system on the basis of a modified fuzzy neural
net. Compared to existing fuzzy neuronets, including Adaptive Network-
Based Fuzzy Inference System, the modified fuzzy neural net includes
recurrent neuronets and fuzzy units. The authors generated the optimum
architecture of the modified fuzzy neural net on the basis of development
modified swarm intelligence algorithms. The function approximation
capabilities of a neural net are exploited to approximate a membership
function. This chapter presents the intelligent models for sizing, parameters
forecasting and control of a photovoltaic system on the basis of a modified
fuzzy neural net. The modified fuzzy neural net provides automatic
fulfillment and modification of all proposed intelligent models. The first
proposed model on the basis of a modified fuzzy neural net provides a two
days ahead forecasting of the hourly power from a photovoltaic system
under random perturbations. In order to train the effective modified fuzzy
neural net the authors developed the modified multi-dimensional Particle
Swarm Optimization, in which the multi-dimensional Particle Swarm
Optimization is combined with the Levenberg-Marquardt algorithm. The
comparison simulation results show that proposed modified multi-
dimensional PSO algorithm outperforms multi-dimensional Particle Swarm
Optimization and Levenberg-Marquardt algorithm in training the effective
modified fuzzy neural net for a two days ahead forecasting of the hourly
PV array power. The second proposed model on the basis of a modified
fuzzy neural net is ambient temperature forecasting model under random
perturbations. This model is important for photovoltaic systems.
Simulation comparison results for a forecasting of ambient temperature
demonstrate the effectiveness of the modified fuzzy neural net trained by
proposed modified Ant Lion Optimizer as compared with the same one
trained by Ant Lion Optimizer or Levenberg-Marquardt algorithm. The
third proposed model on the basis of a modified fuzzy neural net is a
photovoltaic system control model. According the photovoltaic system
condition, the modified fuzzy neural net provides a maximum power point