无需调参的气候不变预测区间，提升风光发电多时序预报可靠性
Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecasting
- 基于分组条件分割校准与集成树模型，自适应调整区间宽度
- 跨4个气候区、1-12小时时序，覆盖率达标且区间得分降低35%
- 单模型通用，适合分布式可再生能源系统部署
可靠的不确定性量化对将太阳能和风能接入现代电力系统至关重要，因为运营商需权衡风险而非仅依赖点预测。现有概率方法往往缺乏有限样本有效性，或需按站点重新校准，导致单一模型难以在分布广泛的发电资产间迁移。本文提出一种异方差、非对称、组条件的分割校准框架，基于Bootstrap多样性XGBoost集成，生成随局部难度自适应变化宽度的预测区间，同时保持分布无关的覆盖保证。一个固定配置的模型在跨越南北半球的四个气候迥异站点上评估，涵盖1至12小时预测时序，针对太阳辐照度和风速两种目标。该框架在两类目标上均实现接近名义覆盖率，相比竞争基线区间得分最高降低35%，且区间校准与锐利性为方法固有特性，不依赖站点特异性调参。
原文摘要 · Abstract (English)
Reliable uncertainty quantification is essential for integrating solar and wind generation into modern power systems, where operators must weigh risk rather than act on point forecasts alone. Existing probabilistic methods, however, often either lack finite-sample validity or require per-site recalibration, so a single model rarely transfers across the diverse climates of a dispersed generation fleet. This paper proposes a heteroscedastic, asymmetric, group-conditional split-conformal framework built on a bootstrap-diverse XGBoost ensemble, producing prediction intervals that adapt in width to local difficulty while retaining distribution-free coverage guarantees. A single fixed specification, with no per-site or per-horizon tuning, is evaluated across four climatologically distinct sites spanning both hemispheres, at horizons of 1 to 12 hours, for both solar irradiance and wind speed. The framework holds near-nominal coverage on both targets and reduces the Interval Score by up to 35% relative to competitive baselines, with the calibration and sharpness of its intervals shown to be properties of the method rather than of site-specific tuning.
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