用于多步太阳能和风能预测的气候不变共形预测区间
Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecasting
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中文总结 AI 辅助
研究针对多步太阳能和风能预测，提出基于自举多样XGBoost集成的气候不变共形预测区间框架，无需逐站点或逐时间跨度调整，在不同气候站点测试，相比基线降低区间得分35%，保持接近名义覆盖。
中文摘要 AI 辅助
可靠的不确定性量化对于将太阳能和风能发电整合到现代电力系统至关重要，在该系统中运营商必须权衡风险而不是仅依据点预测行动。现有概率方法往往缺乏有限样本有效性或需要逐站点重新校准，单一模型很少能在分散发电集群的不同气候条件下转移。本文提出了一个基于自举多样的XGBoost集成的异方差、不对称、组条件分裂共形框架，生成的预测区间宽度能适应局部难度，同时保持无分布覆盖保证。在跨越两个半球的四个气候不同的站点，针对1至12小时的时间跨度，对太阳辐照度和风速进行评估，单一固定规范无需逐站点或逐时间跨度调整。该框架在两个目标上都保持接近名义覆盖，相对于竞争基线，区间得分降低多达35%，其区间的校准和锐度是该方法的特性而非特定于站点的调整。
英文摘要
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.
发表机构
- Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, India（计算机科学与工程系，Dr. B.R. 阿姆贝卡尔国立技术学院贾兰德哈尔，印度）
机构由 AI 辅助整理，请以论文原文为准。
