Prediction the photovoltaic system performance via artificial neural network (ANN) technique
Population growth and industrialization are driving up global energy consumption, which is expected to soar in the near future. However, the predominant use of fossil fuels exacerbates environmental pollution and greenhouse gas emissions, which are primary contributors to global warming. To add...
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2024
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| Online Access: | http://eprints.uthm.edu.my/11222/1/P16738_3693ab6d03f1c091a0ab12073a3ed873%206.pdf http://eprints.uthm.edu.my/11222/ http:\\10.1109/ICPEA60617.2024.10499004 |
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my.uthm.eprints.112222024-07-14T02:42:39Z http://eprints.uthm.edu.my/11222/ Prediction the photovoltaic system performance via artificial neural network (ANN) technique Jumaat, Siti Amely Mohamed, Abdou Mani Mohamad Nor, Ahmad Fateh TK Electrical engineering. Electronics Nuclear engineering Population growth and industrialization are driving up global energy consumption, which is expected to soar in the near future. However, the predominant use of fossil fuels exacerbates environmental pollution and greenhouse gas emissions, which are primary contributors to global warming. To address this, this study proposes an artificial neural network (ANN) model designed to forecast the power output of both monocrystalline and polycrystalline photovoltaic (PV) panels. The aim is to assess the performance and efficiency of these two PV panel types. Data spanning from 2018 to 2020 was gathered, with meteorological parameters serving as input for the ANN model. Polycrystalline panels exhibit higher voltage output, whereas monocrystalline panels typically yield greater current. The model's mean square error (MSE) for training, testing, and validation equated, indicating robust learning during training without overestimation. Both models demonstrate an excellent fit to the data, evident from the correlation coefficient (R) reaching 1. The predicted values closely align with actual trends for both panel types, with insignificant disparities in estimated voltage, current, and power. Overall, the polycrystalline panel outperforms the monocrystalline panel, boasting efficiencies of 0.999% and 0.997%, respectively 2024-03-04 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/11222/1/P16738_3693ab6d03f1c091a0ab12073a3ed873%206.pdf Jumaat, Siti Amely and Mohamed, Abdou Mani and Mohamad Nor, Ahmad Fateh (2024) Prediction the photovoltaic system performance via artificial neural network (ANN) technique. In: 2024 IEEE 4TH INTERNATIONAL CONFERENCE IN POWER ENGINEERING APPLICATIONS (ICPEA), 2024. http:\\10.1109/ICPEA60617.2024.10499004 |
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TK Electrical engineering. Electronics Nuclear engineering |
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TK Electrical engineering. Electronics Nuclear engineering Jumaat, Siti Amely Mohamed, Abdou Mani Mohamad Nor, Ahmad Fateh Prediction the photovoltaic system performance via artificial neural network (ANN) technique |
| description |
Population growth and industrialization are
driving up global energy consumption, which is expected to soar
in the near future. However, the predominant use of fossil fuels
exacerbates environmental pollution and greenhouse gas
emissions, which are primary contributors to global warming.
To address this, this study proposes an artificial neural network
(ANN) model designed to forecast the power output of both
monocrystalline and polycrystalline photovoltaic (PV) panels.
The aim is to assess the performance and efficiency of these two
PV panel types. Data spanning from 2018 to 2020 was gathered,
with meteorological parameters serving as input for the ANN
model. Polycrystalline panels exhibit higher voltage output,
whereas monocrystalline panels typically yield greater current.
The model's mean square error (MSE) for training, testing, and
validation equated, indicating robust learning during training
without overestimation. Both models demonstrate an excellent
fit to the data, evident from the correlation coefficient (R)
reaching 1. The predicted values closely align with actual trends
for both panel types, with insignificant disparities in estimated
voltage, current, and power. Overall, the polycrystalline panel
outperforms the monocrystalline panel, boasting efficiencies of
0.999% and 0.997%, respectively |
| format |
Conference or Workshop Item |
| author |
Jumaat, Siti Amely Mohamed, Abdou Mani Mohamad Nor, Ahmad Fateh |
| author_facet |
Jumaat, Siti Amely Mohamed, Abdou Mani Mohamad Nor, Ahmad Fateh |
| author_sort |
Jumaat, Siti Amely |
| title |
Prediction the photovoltaic system performance via artificial neural network (ANN) technique |
| title_short |
Prediction the photovoltaic system performance via artificial neural network (ANN) technique |
| title_full |
Prediction the photovoltaic system performance via artificial neural network (ANN) technique |
| title_fullStr |
Prediction the photovoltaic system performance via artificial neural network (ANN) technique |
| title_full_unstemmed |
Prediction the photovoltaic system performance via artificial neural network (ANN) technique |
| title_sort |
prediction the photovoltaic system performance via artificial neural network (ann) technique |
| publishDate |
2024 |
| url |
http://eprints.uthm.edu.my/11222/1/P16738_3693ab6d03f1c091a0ab12073a3ed873%206.pdf http://eprints.uthm.edu.my/11222/ http:\\10.1109/ICPEA60617.2024.10499004 |
| _version_ |
1805890679797186560 |
| score |
13.211869 |