LLM-Enhanced Short-Term Electricity Price Forecasting Method for Australian Electricity Market
Source abstract
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: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Load and Power Forecasting · Electric Power System Optimization · Integrated Energy Systems Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
School of Electrical and Computer Engineering, The University of Sydney, Camperdown, NSW 2006, Australia
References (13)
Show source-provided references
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