Citation: Ozdemir, T.; Taher, F.;
Ayinde, B.O.; Zurada, J.M.; Tuzun
Ozmen, O. Comparison of
Feedforward Perceptron Network
with LSTM for Solar Cell Radiation
Prediction. Appl. Sci. 2022, 12, 4463.
https://doi.org/10.3390/
app12094463
Academic Editor: Luis
Hernández-Callejo
Received: 9 January 2022
Accepted: 21 March 2022
Published: 28 April 2022
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Article
Comparison of Feedforward Perceptron Network with LSTM
for Solar Cell Radiation Prediction
Tugba Ozdemir
1,2,
*, Fatma Taher
3
, Babajide O. Ayinde
2
, Jacek M. Zurada
2,4
and Ozge Tuzun Ozmen
1,5
1
Department of Physics, Faculty of Arts and Sciences, Duzce University, Konuralp Yerleskesi,
Duzce 81620, Turkey; ozge.ozmen@bakircay.edu.tr
2
Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, USA;
babajide.ayinde@echonous.com (B.O.A.); jacek.zurada@louisville.edu (J.M.Z.)
3
Department of Computing & Applied Technology & Assistant Dean for Research and Out Reach in the
College of Technological Innovation, Zayed University, Dubai 19282, United Arab Emirates;
fatma.taher@zu.ac.ae
4
Information Technology Institute, University of Social Science, 90-113 Łódz, Poland
5
Department of Fundamental Sciences,
˙
Izmir Bakırçay University, Izmir 35665, Turkey
* Correspondence: tugbaozdemir238@gmail.com
Abstract:
Intermittency of electrical power in developing countries, as well as some European
countries such as Turkey, can be eluded by taking advantage of solar energy. Correct prediction of
solar radiation constitutes a very important step to take advantage of PV solar panels. We propose an
experimental study to predict the amount of solar radiation using a classical artificial neural network
(ANN) and deep learning methods. PV panel and solar radiation data were collected at Duzce
University in Turkey. Moreover, we included meteorological data collected from the Meteorological
Ministry of Turkey in Duzce. Data were collected on a daily basis with a 5-min interval. Data
were cleaned and preprocessed to train long-short-term memory (LSTM) and ANN models to
predict the solar radiation amount of one day ahead. Models were evaluated using coefficient of
determination (R
2
),
mean square error (MSE), root mean squared error (RMSE), mean absolute error
(MAE), and mean biased error (MBE). LSTM outperformed ANN with R
2
, MSE, RMSE, MAE, and
MBE of 0.93, 0.008, 0.089, 0.17, and 0.09, respectively. Moreover, we compared our results with
two similar
studies in the literature. The proposed study paves the way for utilizing renewable
energy by leveraging the usage of PV panels.
Keywords:
renewable energy; solar energy; artificial neural network; deep learning; LSTM; radiation
prediction
1. Introduction
1.1. Background
In recent years, the role of energy in the life standard of human beings has been
vitally important [
1
–
3
]. As the human population increases, energy demands increase
exponentially [
2
–
5
]. Researchers demonstrate that the energy demand is anticipated to be
approximately 1.5–3 times by 2050 [
2
,
6
,
7
]. Given that fact, we can anticipate that fossil fuels
such as petroleum, natural gas, and coal, which are the traditional energy sources, will
be depleted very soon. One more reason to switch to renewable energy is how harmful
the fossil fuels are to the environment [
4
,
8
]. It should be emphasized that consumption
of energy from fossil fuels is increasing CO
2
(carbon dioxide) and greenhouse gas (GHG)
emissions all over the world [
6
,
9
]. Increasing GHGs cause a rising atmospheric temperature
of the Earth’s surface [
7
–
13
]. With this concern, renewable energy has come into question
for the last century [2–5,7–13].
Alternatively, solar energy, which is among renewable energy sources, is abundant and
environmentally friendly, and photovoltaic (PV) technology has provided development
Appl. Sci. 2022, 12, 4463. https://doi.org/10.3390/app12094463 https://www.mdpi.com/journal/applsci
