用XAI解释深度模型如何预测电价，揭示市场驱动因素。
Explaining deep neural network models for electricity price forecasting with XAI
- 结合SHAP、梯度法与热力图，解析五地电价模型特征贡献。
- 提出SSHAP值与SSHAP线，更好展现高维表格模型的复杂性。
- 适合电力市场分析、模型可解释性研究者参考。
电力市场高度复杂，存在大量交互和依赖关系，难以理解其内部运作及价格驱动因素。传统计量方法虽具可解释性，但预测能力不如深度神经网络（DNN）。本文采用DNN预测电价，并运用XAI方法分析影响价格动态的关键因素，以增进对不同电力市场运行机制的理解。通过结合SHAP、梯度法与热力图等可视化技术，对五个电力市场的特征行为与贡献进行分析。创新提出SSHAP值与SSHAP线，提升高维表格模型的可解释性表达。
原文摘要 · Abstract (English)
Electricity markets are highly complex, involving lots of interactions and complex dependencies that make it hard to understand the inner workings of the market and what is driving prices. Econometric methods have been developed for this, white-box models, however, they are not as powerful as deep neural network models (DNN). In this paper, we use a DNN to forecast the price and then use XAI methods to understand the factors driving the price dynamics in the market. The objective is to increase our understanding of how different electricity markets work. To do that, we apply explainable methods such as SHAP and Gradient, combined with visual techniques like heatmaps (saliency maps) to analyse the behaviour and contributions of various features across five electricity markets. We introduce the novel concepts of SSHAP values and SSHAP lines to enhance the complex representation of high-dimensional tabular models.
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