从共享需求模式到局部不确定性：通过混合紧凑适配进行概率负荷预测
From Shared Demand Patterns to Local Uncertainty: Probabilistic Load Forecasting by Mixing Compact Adaptations
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中文总结 AI 辅助
针对用户级负荷预测的可扩展性挑战，提出共享模型结合紧凑适配组件的概率预测框架，在SMART-DS数据集上优于多种基线并保持低成本。
中文摘要 AI 辅助
概率负荷预测已被广泛研究用于电力系统运行和规划，但在用户和变压器层面的预测引入了独特的可扩展性挑战。在这些层面上，负荷不确定性受到用户行为、天气和混合负荷构成的强烈影响，使得单一共享模型难以捕捉异质模式。使用独立的概率模型可以提高局部精度，但在大规模下训练、存储、更新和验证的成本高昂。为解决这一挑战，我们开发了一个可扩展的用户感知预测框架，通过共享模型学习常见需求行为，同时仅适配一小部分紧凑参数。该设计不是为每个负荷使用独立模型或将每个负荷分配给专门模型，而是学习一个小型的低维适配组件库，并允许每个负荷根据其预测特征组合这些组件。这保留了跨用户的共享知识，同时为异质和混合负荷构成提供了足够的灵活性。在SMART-DS数据集的590个负荷曲线上的实验表明，与统计、神经网络、基于Transformer和预训练时间序列基线相比，在确定性精度和概率质量方面均有一致的改进，同时保持了较低的存储和推理成本。
英文摘要
Probabilistic load forecasting has been widely studied for power-system operation and planning, but customer- and transformer-level forecasting introduces a distinct scalability challenge. At these levels, load uncertainty is strongly affected by customer behavior, weather, and mixed load composition, making it difficult for a single shared model to capture heterogeneous patterns. Using separate probabilistic models can improve local accuracy, but becomes costly to train, store, update, and validate at scale. To address this challenge, we develop a scalable customer-aware forecasting framework that learns common demand behavior through a shared model while adapting only a compact subset of parameters. Rather than using an independent model for each load or assigning each load to a specialized model, the proposed design learns a small bank of low-dimensional adaptation components and allows each load to combine them according to its forecasting characteristics. This preserves shared knowledge across customers while providing sufficient flexibility for heterogeneous and mixed load compositions. Experiments on 590 load profiles from the SMART-DS dataset show consistent improvements in deterministic accuracy and probabilistic quality over statistical, neural-network, Transformer-based, and pretrained time-series baselines, while retaining low storage and inference costs.
