基于动态相似性，实现多主体负荷的在线概率预测。
Multi-task Online Learning for Probabilistic Load Forecasting
- 利用多任务学习捕捉多个区域负荷的动态相似性。
- 在多种负荷场景下显著提升预测准确率与不确定性建模能力。
- 适合需要实时、高精度负荷预测的电力系统管理场景。
负荷预测对电力系统的高效、可靠和经济运行至关重要。通过学习多个实体（如区域、建筑）之间的相似性，可提升预测性能。现有基于多任务学习的方法虽能利用多实体的历史负荷数据及关系进行预测，但难以有效评估负荷需求中的内在不确定性，也难以适应消费模式的动态变化。本文提出一种用于在线与概率负荷预测的多任务学习方法，通过挖掘多个实体间的动态相似性，提供高精度的概率预测。实验使用包含多个实体负荷数据、具有多样化与动态消费模式的数据集验证方法性能。结果表明，该方法在多种负荷消费场景下显著优于现有技术，有效提升了多任务学习在负荷预测中的应用效果。
原文摘要 · Abstract (English)
Load forecasting is essential for the efficient, reliable, and cost-effective management of power systems. Load forecasting performance can be improved by learning the similarities among multiple entities (e.g., regions, buildings). Techniques based on multi-task learning obtain predictions by leveraging consumption patterns from the historical load demand of multiple entities and their relationships. However, existing techniques cannot effectively assess inherent uncertainties in load demand or account for dynamic changes in consumption patterns. This paper proposes a multi-task learning technique for online and probabilistic load forecasting. This technique provides accurate probabilistic predictions for the loads of multiple entities by leveraging their dynamic similarities. The method's performance is evaluated using datasets that register the load demand of multiple entities and contain diverse and dynamic consumption patterns. The experimental results show that the proposed method can significantly enhance the effectiveness of current multi-task learning approaches across a wide variety of load consumption scenarios.
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