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A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction

Solar power prediction is crucial for integrating renewable energy into the grid, but current methods often struggle with accuracy due to the limitations of machine learning algorithms. This study aims to enhance prediction accuracy by comparing the performance of Long Short-Term Memory (LSTM)...

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Main Authors: M. Helmy, Muhammad Fareezy Fahmy, Yusoff, Siti Hajar, Mansor, Hasmah, Gunawan, Teddy Surya, Chowdhury, Israth Jahan, Mohd Sapihie, Siti Nadiah
Format: Proceeding Paper
Language:English
Published: IEEE 2024
Subjects:
TK1001 Production of electric energy. Powerplants
TK3001 Distribution or transmission of electric power. The electric power circuit
Online Access:http://irep.iium.edu.my/115179/13/115179_%20A%20comparative%20analysis%20of%20LSTM.pdf
http://irep.iium.edu.my/115179/
https://ieeexplore.ieee.org/document/10675536
https://doi.org/10.1109/ICSIMA62563.2024.10675536
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spelling my.iium.irep.1151792024-10-22T04:28:04Z http://irep.iium.edu.my/115179/ A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction M. Helmy, Muhammad Fareezy Fahmy Yusoff, Siti Hajar Mansor, Hasmah Gunawan, Teddy Surya Chowdhury, Israth Jahan Mohd Sapihie, Siti Nadiah TK1001 Production of electric energy. Powerplants TK3001 Distribution or transmission of electric power. The electric power circuit Solar power prediction is crucial for integrating renewable energy into the grid, but current methods often struggle with accuracy due to the limitations of machine learning algorithms. This study aims to enhance prediction accuracy by comparing the performance of Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) models using datasets from Hebei, China. The main objective is identifying the most effective algorithm for precise solar power forecasting. The methodology involves training both models on historical solar power data and evaluating their performance against the Graph Spatial-Temporal Attention Neural Network (GSTANN) benchmark. The SVM model was selected for its superior metrics, achieving an MAE (Mean Absolute Error) of 0.5587, RMSE of 0.9741, and a training time of 0.0157 seconds. Results show that SVM outperforms GSTANN in 45 and 60- minute intervals, with MAE, MAPE, and RMSE improvements of up to 68.62%, 42.65%, and 69.44%, respectively. These findings suggest that SVM offers a more reliable solution for solar power prediction, providing valuable insights for further model enhancements. IEEE 2024-09-18 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/115179/13/115179_%20A%20comparative%20analysis%20of%20LSTM.pdf M. Helmy, Muhammad Fareezy Fahmy and Yusoff, Siti Hajar and Mansor, Hasmah and Gunawan, Teddy Surya and Chowdhury, Israth Jahan and Mohd Sapihie, Siti Nadiah (2024) A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction. In: IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications, 30-31 July 2024, Bandung, Indonesia. https://ieeexplore.ieee.org/document/10675536 https://doi.org/10.1109/ICSIMA62563.2024.10675536
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TK1001 Production of electric energy. Powerplants
TK3001 Distribution or transmission of electric power. The electric power circuit
spellingShingle TK1001 Production of electric energy. Powerplants
TK3001 Distribution or transmission of electric power. The electric power circuit
M. Helmy, Muhammad Fareezy Fahmy
Yusoff, Siti Hajar
Mansor, Hasmah
Gunawan, Teddy Surya
Chowdhury, Israth Jahan
Mohd Sapihie, Siti Nadiah
A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction
description Solar power prediction is crucial for integrating renewable energy into the grid, but current methods often struggle with accuracy due to the limitations of machine learning algorithms. This study aims to enhance prediction accuracy by comparing the performance of Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) models using datasets from Hebei, China. The main objective is identifying the most effective algorithm for precise solar power forecasting. The methodology involves training both models on historical solar power data and evaluating their performance against the Graph Spatial-Temporal Attention Neural Network (GSTANN) benchmark. The SVM model was selected for its superior metrics, achieving an MAE (Mean Absolute Error) of 0.5587, RMSE of 0.9741, and a training time of 0.0157 seconds. Results show that SVM outperforms GSTANN in 45 and 60- minute intervals, with MAE, MAPE, and RMSE improvements of up to 68.62%, 42.65%, and 69.44%, respectively. These findings suggest that SVM offers a more reliable solution for solar power prediction, providing valuable insights for further model enhancements.
format Proceeding Paper
author M. Helmy, Muhammad Fareezy Fahmy
Yusoff, Siti Hajar
Mansor, Hasmah
Gunawan, Teddy Surya
Chowdhury, Israth Jahan
Mohd Sapihie, Siti Nadiah
author_facet M. Helmy, Muhammad Fareezy Fahmy
Yusoff, Siti Hajar
Mansor, Hasmah
Gunawan, Teddy Surya
Chowdhury, Israth Jahan
Mohd Sapihie, Siti Nadiah
author_sort M. Helmy, Muhammad Fareezy Fahmy
title A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction
title_short A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction
title_full A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction
title_fullStr A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction
title_full_unstemmed A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction
title_sort comparative analysis of lstm, svm, and gstann models for enhancing solar power prediction
publisher IEEE
publishDate 2024
url http://irep.iium.edu.my/115179/13/115179_%20A%20comparative%20analysis%20of%20LSTM.pdf
http://irep.iium.edu.my/115179/
https://ieeexplore.ieee.org/document/10675536
https://doi.org/10.1109/ICSIMA62563.2024.10675536
_version_ 1814042756956291072
score 13.211869

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