用频域感知 Transformer 提升电网碳足迹预测精度与鲁棒性
FTimeXer: Frequency-aware Time-series Transformer with Exogenous variables for Robust Carbon Footprint Forecasting
- 引入 FFT 频率分支与门控时频融合，捕捉多尺度周期模式
- 通过随机外生变量掩码和一致性正则化，提升对缺失/错位数据的鲁棒性
- 适用于需高精度碳核算的电力系统与碳中和决策场景
准确及时地预测电网碳足迹对于产品碳足迹（PCF）核算和科学减排决策至关重要。然而，电网碳强度具有高度非平稳性，现有方法难以有效利用周期性和振荡模式，且在面对缺失数据或时间错位等不规则外生输入时表现不佳。为此，我们提出 FTimeXer，一种面向外生变量的频域感知时间序列 Transformer，配备稳健训练机制。FTimeXer 采用基于快速傅里叶变换（FFT）的频率分支与门控时频融合结构，有效捕捉多尺度周期性特征；同时结合随机外生变量掩码与一致性正则化，减少虚假相关性并增强模型稳定性。在三个真实世界数据集上的实验表明，其性能持续优于多个强基线模型。这些改进使电网碳因子预测更加可靠，为有效的 PCF 核算和低碳化决策提供支持。
原文摘要 · Abstract (English)
Accurate and up-to-date forecasting of the power grid's carbon footprint is crucial for effective product carbon footprint (PCF) accounting and informed decarbonization decisions. However, the carbon intensity of the grid exhibits high non-stationarity, and existing methods often struggle to effectively leverage periodic and oscillatory patterns. Furthermore, these methods tend to perform poorly when confronted with irregular exogenous inputs, such as missing data or misalignment. To tackle these challenges, we propose FTimeXer, a frequency-aware time-series Transformer designed with a robust training scheme that accommodates exogenous factors. FTimeXer features an Fast Fourier Transform (FFT)-driven frequency branch combined with gated time-frequency fusion, allowing it to capture multi-scale periodicity effectively. It also employs stochastic exogenous masking in conjunction with consistency regularization, which helps reduce spurious correlations and enhance stability. Experiments conducted on three real-world datasets show consistent improvements over strong baselines. As a result, these enhancements lead to more reliable forecasts of grid carbon factors, which are essential for effective PCF accounting and informed decision-making regarding decarbonization.
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