Work

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

Year: 2025

Type: article

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.

Source: Applied Sciences

DOI: 10.3390/app16010200

Authors: Yutian Huang, Yachao Zhu, Gang Lei, Allen Wang, Jianguo Zhu

Institutions: University of Technology Sydney, The University of Sydney

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $2,896