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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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Main Authors: Jumaat, Siti Amely, Mohamed, Abdou Mani, Mohamad Nor, Ahmad Fateh
Format: Conference or Workshop Item
Language:English
Published: 2024
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/11686/1/P16647_5bd762f237a3f4d9ad575bcb4d02fb1e%205.pdf
http://eprints.uthm.edu.my/11686/
https://10.1109/ICPEA60617.2024.10499004
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spelling my.uthm.eprints.116862024-11-17T01:36:10Z http://eprints.uthm.edu.my/11686/ Prediction the photovoltaic system performance via Artificial Neural Network (ANN) Technique Jumaat, Siti Amely Mohamed, Abdou Mani Mohamad Nor, Ahmad Fateh T Technology (General) 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/11686/1/P16647_5bd762f237a3f4d9ad575bcb4d02fb1e%205.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. https://10.1109/ICPEA60617.2024.10499004
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
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/11686/1/P16647_5bd762f237a3f4d9ad575bcb4d02fb1e%205.pdf
http://eprints.uthm.edu.my/11686/
https://10.1109/ICPEA60617.2024.10499004
_version_ 1816133307314733056
score 13.222552

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