Conformal prediction 校准英国风电预测不确定性区间研究
The Cost of Coverage: Conformalising Native Forecast Uncertainty for GB Wind Power
这篇论文把 conformal prediction 用在风电预测区间上，发现 pooled 校准在高风速下会失效，改用分层校准能减少 40% 以上的越界量，做预测不确定性量化的可以看看。
研究在 GB 两个风电场、6 种预测器（从 ARIMA 到 tabular foundation model）上测试 conformal prediction 校准原生预测区间。校准以很小甚至没有 Winkler Score 惩罚修复了经验边际覆盖率。但 pooled 校准在高风速下仍存在覆盖不足的问题。按风况分层校准可将高风速下的 day-ahead 越界量较 pooled 校准降低超过 40%，同时整体区间更窄。
The Cost of Coverage: Conformalising Native Forecast Uncertainty for GB Wind Power
Uncertainty bands on wind forecasts help inform reserve sizing and commitments, but often fail to deliver their stated coverage. We apply conformal prediction to native bands to see what it adds and what it costs, comparing six forecasters, from ARIMA to a tabular foundation model, at two wind farms in Great Britain (GB) under operational conditions. We show that calibration repairs empirical marginal coverage with little or no Winkler Score penalty, but this apparent success conceals under-coverage in high winds. Calibrating by forecast wind regime turns marginal coverage into regime-level coverage, cutting day-ahead out-of-band volume in high winds by over 40% relative to pooled calibration while narrowing bands overall.