LLM-Enhanced Feature Engineering for Multi-Factor Electricity Price Predictions
Haochen Xue, Chenghao Liu, Chong Zhang, Yuxuan Chen, Angxiao Zong, Zhaodong Wu, Yulong Li, Jiayi Liu, Kaiyu Liang, Zhixiang Lu, Ruobing Li, Jionglong Su
准确预测电价波动对于有效的风险管理和决策至关重要。 传统的预测模型往往无法捕捉电力市场的复杂非线性动态,特别是当涉及天气条件和市场波动等外部因素时。 这些限制阻碍了他们在高波动性市场(如新南威尔士州)电力市场提供可靠预测的能力。 为了应对这些挑战,我们引入了FAEP,这是一个功能增强型电力价格预测框架。 FAEP利用大型语言模型(LLM)与高级功能工程相结合,以提高预测准确性。 通过结合天气数据和价格波动跳跃等外部功能,并利用检索增强生成(RAG)进行有效特征提取,FAEP克服了传统方法的缺点。 FAEP 中的混合 XGBoost-LSTM 模型进一步完善了这些增强功能,从而产生了更强大的预测框架。 实验结果表明,与澳大利亚新南威尔士州电力市场的其他电价预测模型相比,FAEP实现了最先进的(SOTA)性能,展示了LLM增强功能工程和混合机器学习架构的效率。
Accurately forecasting electricity price volatility is crucial for effective risk management and decision-making. Traditional forecasting models often fall short in capturing the complex, non-linear dynamics of electricity markets, particularly when external factors like weather conditions and market volatility are involved. These limitations hinder their ability to provide reliable predictions in markets with high volatility, such as the New South Wales (NSW) electricity market. To address thes...