标题： 基于外部数据增强的自适应概率负荷预测元表示 显示英文标题

标题： External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting

摘要： 准确的居民负荷预测对于提高电力系统可靠性至关重要，尤其是在可再生能源比例上升和需求侧灵活性增强的背景下。 然而，大多数统计和机器学习模型将外部因素（如天气、日历效应和定价）作为额外输入，忽略了它们的异质性，从而限制了有用外部信息的提取。 我们提出了一种范式转变：外部数据应作为元知识，以动态适应预测模型本身。 基于这一理念，我们设计了一个使用超网络的元表示框架，该框架根据外部条件调节基础深度学习（DL）模型的选定参数。 这提供了表达能力和适应性。 我们进一步集成了一个专家混合（MoE）机制，通过选择性专家激活来提高效率，同时通过过滤冗余的外部输入来提高鲁棒性。 所提出的模型被称为外部数据的元专家混合（M2oE2），在有限的额外开销下显著提高了准确性和鲁棒性，在多种负荷数据集中优于现有的最先进方法。 数据集和源代码可在 https://github.com/haorandd/M2oE2\_load\_forecast.git 公开获取。 显示英文摘要

摘要： Accurate residential load forecasting is critical for power system reliability with rising renewable integration and demand-side flexibility. However, most statistical and machine learning models treat external factors, such as weather, calendar effects, and pricing, as extra input, ignoring their heterogeneity, and thus limiting the extraction of useful external information. We propose a paradigm shift: external data should serve as meta-knowledge to dynamically adapt the forecasting model itself. Based on this idea, we design a meta-representation framework using hypernetworks that modulate selected parameters of a base Deep Learning (DL) model in response to external conditions. This provides both expressivity and adaptability. We further integrate a Mixture-of-Experts (MoE) mechanism to enhance efficiency through selective expert activation, while improving robustness by filtering redundant external inputs. The resulting model, dubbed as a Meta Mixture of Experts for External data (M2oE2), achieves substantial improvements in accuracy and robustness with limited additional overhead, outperforming existing state-of-the-art methods in diverse load datasets. The dataset and source code are publicly available at https://github.com/haorandd/M2oE2\_load\_forecast.git.