解决电力价格预测中不确定性估计过自信的问题
Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting
- 提出校准感知的目标函数，提升概率预测可靠性
- 实证显示现有模型忽略校准会导致预测失真
- 适合关注能源市场风险管理的研究者
随着可再生能源并网比例上升，电力市场波动加剧，概率性电价预测对风险管控至关重要。然而，当前基于合理评分规则的方法常过度追求预测精度，忽视校准性，导致不确定性估计过于自信且统计不可靠。本文揭示了理论评分与实际校准之间的关键差距，表明若忽视可靠性，模型将退化为确定性预测的代理。研究强调未来工作需转向校准感知的目标函数与模型架构，以保障能源市场预测的概率分布完整性。
原文摘要 · Abstract (English)
As renewable energy integration increases market volatility, probabilistic electricity price forecasting has become essential for effective risk management. However, current-proper-scoring rules often prioritize forecast sharpness at the expense of calibration, leading to overconfident and statistically unreliable uncertainty estimates. This work highlights the critical gap between theoretical scoring and practical calibration, demonstrating that models can become mere proxies for deterministic forecasts when reliability is neglected. We conclude that future research must shift toward calibration-aware objectives and architectures to ensure the distributional integrity of energy market forecasts.
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