Citation: Lee, J.; Kim, Y. Comparative
Estimation of Electrical
Characteristics of a Photovoltaic
Module Using Regression and
Artificial Neural Network Models.
Electronics 2022, 11, 4228. https://
doi.org/10.3390/electronics11244228
Academic Editors: Luis
Hernández-Callejo, Sergio
Nesmachnow and Sara Gallardo
Saavedra
Received: 21 November 2022
Accepted: 16 December 2022
Published: 19 December 2022
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Article
Comparative Estimation of Electrical Characteristics of a
Photovoltaic Module Using Regression and Artificial Neural
Network Models
Jonghwan Lee and Yongwoo Kim *
Department of System Semiconductor Engineering, Sangmyung University, Cheonan 31066, Republic of Korea
* Correspondence: yongwoo.kim@smu.ac.kr
Abstract:
Accurate modeling of photovoltaic (PV) modules under outdoor conditions is essential to
facilitate the optimal design and assessment of PV systems. As an alternative model to the translation
equations based on regression methods, various data-driven models have been adopted to estimate
the current–voltage (I–V) characteristics of a photovoltaic module under varying operation conditions.
In this paper, artificial neural network (ANN) models are compared with the regression models for
five parameters of a single diode solar cell. In the configuration of the proposed PV models, the five
parameters are predicted by regression and neural network models, and these parameters are put
into an explicit expression such as the Lambert W function. The multivariate regression parameters
are determined by using the least square method (LSM). The ANN model is constructed by using a
four-layer, feed-forward neural network, in which the inputs are temperature and solar irradiance,
and the outputs are the five parameters. By training an experimental dataset, the ANN model is
built and utilized to predict the five parameters by reading the temperature and solar irradiance.
The performance of the regression and ANN models is evaluated by using root mean squared error
(RMSE) and mean absolute percentage error (MAPE). A comparative study of the regression and
ANN models shows that the performance of the ANN models is better than the regression models.
Keywords: regression; artificial neural network; I–V characteristics; photovoltaic module
1. Introduction
The output power of photovoltaic (PV) systems is strongly affected under arbitrary op-
erating conditions such as temperature and solar irradiance of PV modules [
1
,
2
]. However,
highly predictive and efficient models across different temperatures and irradiances have
not been established [
3
–
6
]. In addition, their nonlinear characteristics make highly predic-
tive modeling even more difficult [
7
–
13
]. The single-diode model (SDM) with five parame-
ters is widely utilized to reproduce the current–voltage (I–V)
characteristics [5–8]
. Owing
to the inherent implicit expression for the electrical equivalent circuit of the SDM, analytical
and explicit I–V models have been proposed to calculate the I–V relationship [
1
,
14
–
16
].
The explicit I–V model based on the Lambert W function is simple and efficient, while the
implicit model requires more computational time [
14
–
16
]. Although optimization methods
have been proposed to obtain the five parameters at the standard test condition (STC), sig-
nificant extraction efforts are required to consider the dependence of unknown parameters
on temperature and solar irradiance [
3
–
8
,
17
–
21
]. For arbitrary operating conditions, the
performance of the parameter translation model is greatly limited by the chosen translation
equation and correction factors [
13
,
17
–
21
]. In order to construct a complete PV model for
climatic conditions, the translational formula should be further modified [
17
,
19
] and new
parameters may need to be taken into account [
18
,
20
,
21
]. Moreover, the accuracy of the
translational formula varies significantly at low irradiance levels [
3
–
5
]. However, artificial
neural network (ANN) models provide parameter identification, I–V prediction with higher
accuracy directly from the measured data [
22
,
23
], and fault detection and diagnosis for
Electronics 2022, 11, 4228. https://doi.org/10.3390/electronics11244228 https://www.mdpi.com/journal/electronics
