Large Language Model for Extreme Electricity Price Forecasting in the Australia Electricity Market

This work addresses the challenge of accurately forecasting electricity prices within the volatile Australian market, especially during extreme conditions. It leverages advanced generative pre-trained Large Language Models (LLMs) to analyze the content of electricity market notices with the goal of identifying the drivers behind extreme price fluctuations. Additionally, this approach employs LLMs for an in-depth time-series analysis of electricity prices, providing Australian electricity company traders with insights to refine their trading strategies. To enhance forecasting accuracy this study adopts the QLoRA method for fine-tuning open access LLMs, enabling the analysis of market notices to generate a time series event dataset. A CNN-LSTM network architecture is designed to process both electricity price data and market notice information, thereby improving forecast precision in periods of extreme price volatility. The proposed decision support framework undergoes simulation and evaluation using data from the Australian electricity market, demonstrating its potential to significantly benefit traders in navigating the complexities of the energy sector.