A Hybrid LSTM-LightGBM Model for Precise Short-Term Wind Power Forecasting

Faruque, MO; Hossain, MA; Alam, SMM; Islam, MR; Islam, MR; Guo, Y

A Hybrid LSTM-LightGBM Model for Precise Short-Term Wind Power Forecasting

Faruque, MO Hossain, MA Alam, SMM Islam, MR Islam, MR Guo, Y

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2024, 00, pp. 1-2
Issue Date:: 2024-01-09

In Progress

	Filename	Description	Size
	1704662 am.pdf	Accepted version	356.73 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is being processed and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	Faruque, MO
dc.contributor.author	Hossain, MA
dc.contributor.author	Alam, SMM
dc.contributor.author	Islam, MR
dc.contributor.author	Islam, MR
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684
dc.date	2023-10-27
dc.date.accessioned	2024-05-13T01:46:04Z
dc.date.available	2024-05-13T01:46:04Z
dc.date.issued	2024-01-09
dc.identifier.citation	2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2024, 00, pp. 1-2
dc.identifier.isbn	9798350301571
dc.identifier.uri	http://hdl.handle.net/10453/178877
dc.description.abstract	This paper proposes a novel hybrid model that combines long short term memory LSTM with light gradient boosting LightGBM to achieve precise onshore wind power prediction in Australia The intermittent nature of renewable energy sources like wind power necessitates accurate forecasting to minimize operational costs and enhance power system reliability and security The hybrid model showcases promising results in forecasting 15 minute interval short term wind generation data Performance is evaluated using root mean squared errors RMSE and means absolute errors MAE and compares the proposed model with neural network NN gated recurrent unit GRU and standalone LSTM The comparative analysis demonstrates the superiority of the proposed hybrid model showing lower error indices with exceptional forecasting capability This hybrid LSTM LightGBM model holds great potential for optimizing renewable energy integration into power systems facilitating cost effective and reliable energy generation
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD)
dc.relation.ispartof	2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices
dc.relation.isbasedon	10.1109/asemd59061.2023.10368796
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	A Hybrid LSTM-LightGBM Model for Precise Short-Term Wind Power Forecasting
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Tianjin, China
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	in_progress	*
dc.date.updated	2024-05-13T01:46:02Z
pubs.finish-date	2023-10-29
pubs.publication-status	Published
pubs.start-date	2023-10-27
pubs.volume	00

Abstract:

This paper proposes a novel hybrid model that combines long short term memory LSTM with light gradient boosting LightGBM to achieve precise onshore wind power prediction in Australia The intermittent nature of renewable energy sources like wind power necessitates accurate forecasting to minimize operational costs and enhance power system reliability and security The hybrid model showcases promising results in forecasting 15 minute interval short term wind generation data Performance is evaluated using root mean squared errors RMSE and means absolute errors MAE and compares the proposed model with neural network NN gated recurrent unit GRU and standalone LSTM The comparative analysis demonstrates the superiority of the proposed hybrid model showing lower error indices with exceptional forecasting capability This hybrid LSTM LightGBM model holds great potential for optimizing renewable energy integration into power systems facilitating cost effective and reliable energy generation

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178877