LLM-Enhanced Short-Term Electricity Price Forecasting Method for Australian Electricity Market

Huang, Y; Zhu, Y; Lei, G; Wang, A; Zhu, J

LLM-Enhanced Short-Term Electricity Price Forecasting Method for Australian Electricity Market

Huang, Y Zhu, Y

Lei, G

Wang, A Zhu, J

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Applied Sciences Switzerland, 2026, 16, (1)
Issue Date:: 2026-01-01

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Field	Value	Language
dc.contributor.author	Huang, Y
dc.contributor.author	Zhu, Y https://orcid.org/0009-0007-4994-0908
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Wang, A
dc.contributor.author	Zhu, J
dc.date.accessioned	2026-01-23T05:45:10Z
dc.date.available	2026-01-23T05:45:10Z
dc.date.issued	2026-01-01
dc.identifier.citation	Applied Sciences Switzerland, 2026, 16, (1)
dc.identifier.issn	2076-3417
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/192293
dc.description.abstract	This study investigates a large language model driven (LLM) framework for intelligent preprocessing and short-term electricity price forecasting in the Australian National Electricity Market (NEM). By integrating unstructured news features, weather signals, and cyclical calendar variables, the model captures both physical and informational drivers of price volatility. A hybrid approach combining quantile regression with conformal calibration achieves statistically significant improvements in accuracy and uncertainty calibration. The framework demonstrates the potential of integrating LLMs into operational forecasting pipelines to support electricity market decision-making and risk management.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Applied Sciences Switzerland
dc.relation.isbasedon	10.3390/app16010200
dc.rights	info:eu-repo/semantics/openAccess
dc.title	LLM-Enhanced Short-Term Electricity Price Forecasting Method for Australian Electricity Market
dc.type	Journal Article
utslib.citation.volume	16
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
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/Engineering and IT Related HDR Students
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-01-23T05:45:08Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	1

Abstract:

This study investigates a large language model driven (LLM) framework for intelligent preprocessing and short-term electricity price forecasting in the Australian National Electricity Market (NEM). By integrating unstructured news features, weather signals, and cyclical calendar variables, the model captures both physical and informational drivers of price volatility. A hybrid approach combining quantile regression with conformal calibration achieves statistically significant improvements in accuracy and uncertainty calibration. The framework demonstrates the potential of integrating LLMs into operational forecasting pipelines to support electricity market decision-making and risk management.

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http://hdl.handle.net/10453/192293