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Prediction of grid-connected photovoltaic performance using artificial neural networks and experimental dataset considering environmental variation

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Abstract

Photovoltaic (PV) investment requires a feasibility study of the PV system in terms of environmental parameters at the location, which is the implementation time and cost. In this study, a 1.4 PV system was installed in Sohar, Oman and the system recorded data, which was modelled using an artificial neural network (ANN). The contribution of this study is to use three proposed ANN models (MLP, SOFM, and SVM) to predict similar systems in twelve other locations throughout the country based on measured solar irradiance and ambient temperature in these locations. The experimental results of Sohar show feasible values of 6.82 A, 150–160 V, 800–1000 W, and 245.8 kWh, peak current, voltage, power, and energy, respectively. Also, the proposed models show an excellent prediction with less error and high accuracy. Furthermore, statistical and sensitivity analyses are presented with a comparison of results found by researchers in the literature for validation. The lowest RMSE was found for SOFM (0.2514) in the training phase compared with (0.2528) for MLP and (0.2167) for SVM. The same sequence but with a higher accuracy was found for SOFM (95.25%), while (92.55%) and (89.19%) for MLP and SVM, respectively. In conclusion, the sensitivity analysis shows that solar irradiance has more effect on the output compared with ambient temperature. Also, a prediction of PV output for Duqm was forecasted till 2050, where it is found insignificant deviation due to climate change compared with 2020.

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Abbreviations

PV:

Photovoltaic

GCPV:

Grid-connected photovoltaic

ANN:

Artificial neural networks

VOC :

Open circuit voltage

ISC :

Short circuit current

MLP:

Multilayer perceptron

SOFM:

Self-organizing feature map

SVM:

Support victor machine

COE:

Cost of energy

LCCA:

Life cycle cost analysis

SY:

Specific yield

CF:

Capacity factor

MAPE:

Mean absolute percentage error

RMSE:

Root mean square error

MAE:

Mean absolute error

MSE:

Mean square error

STC:

Standard test conditions

d i (p) :

Desired response for a given input

u ij :

Value in the eigenvectors matrix (s)

s jj :

Value in the diagonal coordinate of s (variance–covariance matrix)

N :

Number of years

N r :

The number of components replaced over the lifetime of the system

P in(t):

The instantaneous input power

P Inv :

The inverter power

P loss(t):

The instantaneous power losses

P PV :

The PV module power

P peak :

The PV peak power

P R :

Rated power

R :

Correlation

R 2 :

Coefficient of determination

t 1 :

The hour, day, month

t 2 :

The minute, hour, day

T :

Temperature

T c :

The cell temperature

T standard :

The temperature of (25 °C) at standard test conditions (STC)

G:

The incident solar irradiance (W/m2)

G standard :

Solar radiation (1000 W/m2) at standard test conditions

y i(p):

Predicted value of E(w) at iteration p

l i :

Value in the diagonal position of L (L is a diagonal matrix of the eigenvalues of s)

ρ :

Distance to the winner-neuron

α(t):

Learning rate

e i(n):

Error correction learning

δ i(n):

Local error

\(x_{i}\) :

Value of the ith observation

\(\overline{x}\) :

Mean value of all the observations

x :

First dataset {x1,…, xn}

y :

Other dataset {y1,…,yn} containing n value

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Acknowledgements

The research leading to these results has received Research Project Grant Funding from the Research Council of the Sultanate of Oman, Research Grant Agreement No. ORG SU EI 11 010.

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Authors and Affiliations

  1. Sohar University, PO Box 44, Sohar, PCI 311, Oman

    Hussein A. Kazem

  2. Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Hussein A. Kazem

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  1. Hussein A. Kazem
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Kazem, H.A. Prediction of grid-connected photovoltaic performance using artificial neural networks and experimental dataset considering environmental variation. Environ Dev Sustain 25, 2857–2884 (2023). https://doi.org/10.1007/s10668-022-02174-0

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