Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market
arXiv:2605.22387v1 Announce Type: new Abstract: Accurate electricity price forecasting (EPF) is essential for market participants to support operational planning and risk management, yet remains challenging due to strong volatility, nonlinear dynamics, and frequent extreme price spikes. These challenges are particularly pronounced in the Australian National Electricity Market (NEM), where high renewable penetration further increases uncertainty. This paper investigates week-ahead electricity price forecasting and proposes a hybrid KAN+XGBoost framework that integrates Kolmogorov-Arnold Network
The increasing penetration of renewable energy in grids like Australia's NEM is driving greater volatility and uncertainty, making advanced forecasting methods essential for market stability and economic efficiency.
Accurate electricity price forecasting is critical for energy market participants to optimize operations, manage financial risks, and ensure grid reliability, especially with the transition to more distributed and renewable generation.
The adoption of hybrid AI models such as KAN+XGBoost represents a significant analytical advancement in managing the complexity and volatility of modern electricity markets.
- · Energy traders
- · Grid operators
- · AI/ML solution providers
- · Renewable energy producers
- · Traditional forecasting methodologies
- · Inefficient power generators
Improved financial stability and operational efficiency for market participants.
Accelerated integration of renewable energy sources due to enhanced grid management capabilities.
Potential for new algorithmic trading strategies and dynamic pricing models within energy markets.
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Read at arXiv cs.LG