面向短期建筑负荷预测的模型无关校正框架
原标题：A Model-Agnostic Correction Framework for Short-Term Building Load Forecasting
AI 摘要
论文提出 WeL-RAC，一种与模型无关的天气-负荷检索增强校正框架，在预测时根据近期负荷状态和对齐预测时域的天气预报检索相似历史案例，并用这些案例的实际未来负荷轨迹校正基础预测器的初始输出，无需重新训练或修改模型。在三个数据集、31 个建筑案例、8 种预测骨干和 1 至 18 小时的四个预测时域上评估，96 组数据集级对比中有 90 组优于对应基础预测器。案例研究还展示了检索到的历史证据如何提升或降低预测，从而体现框架的可解释性。
正文节选
[orcid=0000-0003-1380-6428] A Model-Agnostic Correction Framework for Short-Term Building Load Forecasting Abstract Short-term building load forecasting remains challenging because load patterns can change rapidly with weather and operating conditions. Data-driven forecasting models may become less reliable when the current operating regime is rare or poorly represented during training, even when similar situations exist elsewhere in the historical record. Recent advances in retrieval-augmented