融合KAN与XGBoost，提升澳洲电力市场周度电价预测精度
Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market
- 用KAN捕捉全局非线性特征，XGBoost处理局部波动，协同建模
- 相比XGBoost降低12%平均绝对误差，比基准模型降低超50%
- 适合需应对高波动、高可再生能源占比市场的决策者
准确的电力价格预测（EPF）对市场参与者制定运营计划和管理风险至关重要，但受强波动性、非线性动态及频繁极端价格飙升影响，仍具挑战性。这一问题在可再生能源渗透率高的澳大利亚国家电力市场（NEM）尤为突出。本文研究周度电价预测，提出一种混合KAN+XGBoost框架，结合柯尔莫戈罗夫-阿诺德网络（KAN）的全局非线性表征能力与树基学习器XGBoost的局部鲁棒性，以同时捕捉长期依赖关系与短期价格波动。基于真实NEM数据，采用扩展窗口评估策略进行实验。结果表明，该模型优于多种基准方法（包括SARIMAX、LSTM、独立KAN与XGBoost），相比XGBoost将平均绝对误差（MAE）降低约12%，相比朴素基准模型降低超过50%。结果表明，混合学习策略为高度动态电力市场中的电价预测提供了有效且稳健的解决方案。
原文摘要 · Abstract (English)
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 Networks (KAN) with tree-based learning. The proposed approach combines the global nonlinear representation capability of KAN with the local robustness of XGBoost to capture both long-term dependencies and short-term price fluctuations. Experiments are conducted on real-world NEM data using an expanding window evaluation strategy. The results demonstrate that the proposed model outperforms benchmark methods, including SARIMAX, Long Short-Term Memory (LSTM), standalone KAN, and XGBoost, reducing MAE by approximately 12% compared to XGBoost and by over 50% compared to a naive baseline. The results suggest that hybrid learning strategies provide an effective and robust solution for electricity price forecasting in highly dynamic electricity markets.
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