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Optimizing photovoltaic output performance prediction: a deep learning approach with LSTM neural networks and Adam optimizer / Syasya Nadhirah Hamedon, Juliana Johari and Fazlina Ahmat Ruslan

This study introduces an innovative approach to optimizing photovoltaic (PV) output performance prediction through Deep Learning, specifically employing Long Short-Term Memory (LSTM) networks and the Adaptive Moment Estimation (Adam) optimizer. The research is carried out using MATLAB R2023a, and th...

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Main Authors: Hamedon, Syasya Nadhirah, Johari, Juliana, Ahmat Ruslan, Fazlina
Format: Article
Language:English
Published: UiTM Press 2024
Subjects:
Photoelectronic devices (General)
Online Access:https://ir.uitm.edu.my/id/eprint/105786/1/105786.pdf
https://ir.uitm.edu.my/id/eprint/105786/
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id my.uitm.ir.105786
record_format eprints
spelling my.uitm.ir.1057862024-11-07T02:45:27Z https://ir.uitm.edu.my/id/eprint/105786/ Optimizing photovoltaic output performance prediction: a deep learning approach with LSTM neural networks and Adam optimizer / Syasya Nadhirah Hamedon, Juliana Johari and Fazlina Ahmat Ruslan jeesr Hamedon, Syasya Nadhirah Johari, Juliana Ahmat Ruslan, Fazlina Photoelectronic devices (General) This study introduces an innovative approach to optimizing photovoltaic (PV) output performance prediction through Deep Learning, specifically employing Long Short-Term Memory (LSTM) networks and the Adaptive Moment Estimation (Adam) optimizer. The research is carried out using MATLAB R2023a, and the dataset is publicly accessible from the Solcast website. Performance evaluation utilizing key indicators such as Loss Metrics and Root Mean Squared Error (RMSE) highlights the potential of this Adam-optimized LSTM method to significantly enhance the accuracy of PV performance prediction. The analysis explores the impact of learning rates, evaluating fixed rates (0.0001, 0.001, 0.01) and a dynamic transition (0.01 to 0.001) over 10 epochs. Notably, a learning rate of 0.01 demonstrates substantial improvements, achieving lower errors and consistently low losses, indicating a highly accurate and well-fitted model. The unexpected adaptability observed during a dynamic learning rate transition further highlights the model's potential. The study presents a comprehensive analysis of LSTMs and Adam optimizer for PV output performance prediction and provides valuable insights for researchers seeking optimal learning rates to develop robust and effective PV performance prediction models. UiTM Press 2024-10 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/105786/1/105786.pdf Optimizing photovoltaic output performance prediction: a deep learning approach with LSTM neural networks and Adam optimizer / Syasya Nadhirah Hamedon, Juliana Johari and Fazlina Ahmat Ruslan. (2024) Journal of Electrical and Electronic Systems Research (JEESR) <https://ir.uitm.edu.my/view/publication/Journal_of_Electrical_and_Electronic_Systems_Research_=28JEESR=29/>, 25 (1): 10. pp. 89-98. ISSN 1985-5389, e-ISSN : 3030-640X
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Photoelectronic devices (General)
spellingShingle Photoelectronic devices (General)
Hamedon, Syasya Nadhirah
Johari, Juliana
Ahmat Ruslan, Fazlina
Optimizing photovoltaic output performance prediction: a deep learning approach with LSTM neural networks and Adam optimizer / Syasya Nadhirah Hamedon, Juliana Johari and Fazlina Ahmat Ruslan
description This study introduces an innovative approach to optimizing photovoltaic (PV) output performance prediction through Deep Learning, specifically employing Long Short-Term Memory (LSTM) networks and the Adaptive Moment Estimation (Adam) optimizer. The research is carried out using MATLAB R2023a, and the dataset is publicly accessible from the Solcast website. Performance evaluation utilizing key indicators such as Loss Metrics and Root Mean Squared Error (RMSE) highlights the potential of this Adam-optimized LSTM method to significantly enhance the accuracy of PV performance prediction. The analysis explores the impact of learning rates, evaluating fixed rates (0.0001, 0.001, 0.01) and a dynamic transition (0.01 to 0.001) over 10 epochs. Notably, a learning rate of 0.01 demonstrates substantial improvements, achieving lower errors and consistently low losses, indicating a highly accurate and well-fitted model. The unexpected adaptability observed during a dynamic learning rate transition further highlights the model's potential. The study presents a comprehensive analysis of LSTMs and Adam optimizer for PV output performance prediction and provides valuable insights for researchers seeking optimal learning rates to develop robust and effective PV performance prediction models.
format Article
author Hamedon, Syasya Nadhirah
Johari, Juliana
Ahmat Ruslan, Fazlina
author_facet Hamedon, Syasya Nadhirah
Johari, Juliana
Ahmat Ruslan, Fazlina
author_sort Hamedon, Syasya Nadhirah
title Optimizing photovoltaic output performance prediction: a deep learning approach with LSTM neural networks and Adam optimizer / Syasya Nadhirah Hamedon, Juliana Johari and Fazlina Ahmat Ruslan
title_short Optimizing photovoltaic output performance prediction: a deep learning approach with LSTM neural networks and Adam optimizer / Syasya Nadhirah Hamedon, Juliana Johari and Fazlina Ahmat Ruslan
title_full Optimizing photovoltaic output performance prediction: a deep learning approach with LSTM neural networks and Adam optimizer / Syasya Nadhirah Hamedon, Juliana Johari and Fazlina Ahmat Ruslan
title_fullStr Optimizing photovoltaic output performance prediction: a deep learning approach with LSTM neural networks and Adam optimizer / Syasya Nadhirah Hamedon, Juliana Johari and Fazlina Ahmat Ruslan
title_full_unstemmed Optimizing photovoltaic output performance prediction: a deep learning approach with LSTM neural networks and Adam optimizer / Syasya Nadhirah Hamedon, Juliana Johari and Fazlina Ahmat Ruslan
title_sort optimizing photovoltaic output performance prediction: a deep learning approach with lstm neural networks and adam optimizer / syasya nadhirah hamedon, juliana johari and fazlina ahmat ruslan
publisher UiTM Press
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/105786/1/105786.pdf
https://ir.uitm.edu.my/id/eprint/105786/
_version_ 1817847103124144128
score 13.222552

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