用集成学习提升碳强度预测精度，更好适应不同地区差异。
EnsembleCI: Ensemble Learning for Carbon Intensity Forecasting
- 通过加权多个子模型预测，实现端到端自适应
- 平均误差降低19.58%，多数区域表现最优
- 可识别地区特征，适合电力碳排放管理场景
碳强度（CI）衡量每单位电力产生的平均碳排放，是评估环境影响的关键指标。准确的CI预测对减少碳足迹至关重要，但现有先进方法CarbonCast因无法处理区域差异且缺乏适应性而表现不足。为此，本文提出EnsembleCI，一种基于集成学习的自适应端到端预测方法。该方法融合多个子学习器的加权预测，具备更强灵活性与区域适应能力。在11个区域电网上的评估显示，EnsembleCI在几乎所有电网中均优于CarbonCast，平均绝对百分比误差（MAPE）最低，整体预测精度平均提升19.58%。尽管受区域固有差异影响，性能仍有波动，但其长期预测更稳健，并能识别区域特异性关键特征，体现良好可解释性与实际应用价值。这些结果表明EnsembleCI是更精准可靠的碳强度预测方案。代码与数据已公开于https://github.com/emmayly/EnsembleCI。
原文摘要 · Abstract (English)
Carbon intensity (CI) measures the average carbon emissions generated per unit of electricity, making it a crucial metric for quantifying and managing the environmental impact. Accurate CI predictions are vital for minimizing carbon footprints, yet the state-of-the-art method (CarbonCast) falls short due to its inability to address regional variability and lack of adaptability. To address these limitations, we introduce EnsembleCI, an adaptive, end-to-end ensemble learning-based approach for CI forecasting. EnsembleCI combines weighted predictions from multiple sublearners, offering enhanced flexibility and regional adaptability. In evaluations across 11 regional grids, EnsembleCI consistently surpasses CarbonCast, achieving the lowest mean absolute percentage error (MAPE) in almost all grids and improving prediction accuracy by an average of 19.58%. While performance still varies across grids due to inherent regional diversity, EnsembleCI reduces variability and exhibits greater robustness in long-term forecasting compared to CarbonCast and identifies region-specific key features, underscoring its interpretability and practical relevance. These findings position EnsembleCI as a more accurate and reliable solution for CI forecasting. EnsembleCI source code and data used in this paper are available at https://github.com/emmayly/EnsembleCI.
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