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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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Bibliographic Details
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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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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