用主动学习提升电力系统可靠性评估效率
MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models
- 结合主动学习的代理模型减少标注次数
- 在有限计算预算下显著降低方差
- 适合资源充裕性评估场景
多级蒙特卡洛（MLMC）是一种灵活高效的方差缩减技术，可加速复杂电力系统的可靠性评估。近期，数据驱动的代理模型因其高相关性和训练后极低的执行时间，被引入作为MLMC框架中的低层级模型。然而，在资源充裕性评估中，预标注数据集通常不可用。对于大规模系统，代理模型带来的效率增益常被标注训练数据所需的时间所抵消。因此，本文提出一种考虑训练时间的速度度量来评估MLMC效率。鉴于总计算时间预算有限，本文提出基于投票委员会的主动学习方法以减少所需的标注调用次数。案例研究显示，在给定计算预算下，主动学习与MLMC结合可显著降低方差。
原文摘要 · Abstract (English)
Multilevel Monte Carlo (MLMC) is a flexible and effective variance reduction technique for accelerating reliability assessments of complex power system. Recently, data-driven surrogate models have been proposed as lower-level models in the MLMC framework due to their high correlation and negligible execution time once trained. However, in resource adequacy assessments, pre-labeled datasets are typically unavailable. For large-scale systems, the efficiency gains from surrogate models are often offset by the substantial time required for labeling training data. Therefore, this paper introduces a speed metric that accounts for training time in evaluating MLMC efficiency. Considering the total time budget is limited, a vote-by-committee active learning approach is proposed to reduce the required labeling calls. A case study demonstrates that, within a given computational budget, active learning in combination with MLMC can result in a substantial reduction variance.
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