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Complex-Valued Wavelet Neural Network Prediction of the Daily Global Solar Irradiation of the Great Maghreb Region

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Progress in Clean Energy, Volume 1

Abstract

In this chapter, the prediction of the daily global solar irradiation of the great Maghreb region using the complex-valued wavelet neural network (CVWNN) is presented. Both multi-input single output (MISO) and multi-input multi-output (MIMO) strategies are considered. The meteorological data of the capitals of the great Maghreb, which are Tripoli (Libya), Tunis (Tunisia), Algiers (Algeria), Rabat (Morocco), El Aaiun (Western Sahara), and Nouakchott (Mauritania), are used like samples from each country. To test the applicability and the feasibility of the CWNN to predict the daily global irradiation for the great Maghreb case, several models are presented. Results obtained throughout this chapter show that the CWN technique is suitable for prediction of the daily solar irradiation of the great Maghreb region.

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Abbreviations

CVWNN:

Split complex-valued wavelet neural networks

n :

Number of inputs

m :

Number of neurons in the hidden layer

l :

Number of output

X n :

Input vector

t m :

Translations of the hidden neurons

d m :

Dilations of the hidden neurons

f 1C (.):

Complex-valued wavelet used for the hidden layer

f 2C (.):

Complex-valued activation function used for the output layer

\( j=\sqrt{-1} \) :

Imaginary unit

y l :

lth desired output

ŷ l :

lth predicted output

t C(d):

Complex-valued temporal index, d = 1, …, 365

T m :

Daily air temperature, °C

H m :

Relative humidity, %

G m :

Daily global solar irradiation, kJ/m2

T d :

Complex-valued daily air temperature

H d :

Complex-valued relative humidity

G d :

Complex-valued daily global solar irradiation

nRMSE:

Normalized root mean squared error, %

R 2 :

Coefficient of determination, %

MAE:

Mean absolute error, %

N :

Number of samples

MIMO:

Multi input multi output

MISO:

Multi input single output

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Authors and Affiliations

  1. Laboratory of Computer science, Modeling, Optimization and Electronic Systems (LIMOSE), Department of Physics, Faculty of sciences, University M’Hamed Bougara, Boumerdès, 35000, Algeria

    Lyes Saad Saoud, Fayçal Rahmoune, Victor Tourtchine & Kamel Baddari

Authors
  1. Lyes Saad Saoud
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  2. Fayçal Rahmoune
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  3. Victor Tourtchine
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  4. Kamel Baddari
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Corresponding author

Correspondence to Lyes Saad Saoud .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, University of Ontario, Oshawa, Ontario, Canada

    Ibrahim Dincer

  2. Department of Mechanical Engineering, Dokuz Eylul University, Izmir, Turkey

    C. Ozgur Colpan

  3. Department of Energy Systems Engineering, Suleyman Demirel University, Isparta, Turkey

    Onder Kizilkan

  4. Department of Mechanical Engineering, Dokuz Eylul University, Izmir, Turkey

    M. Akif Ezan

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Saad Saoud, L., Rahmoune, F., Tourtchine, V., Baddari, K. (2015). Complex-Valued Wavelet Neural Network Prediction of the Daily Global Solar Irradiation of the Great Maghreb Region. In: Dincer, I., Colpan, C., Kizilkan, O., Ezan, M. (eds) Progress in Clean Energy, Volume 1. Springer, Cham. https://doi.org/10.1007/978-3-319-16709-1_23

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  • DOI: https://doi.org/10.1007/978-3-319-16709-1_23

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16708-4

  • Online ISBN: 978-3-319-16709-1

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