# MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models

arXiv: https://arxiv.org/abs/2505.20930 · HTML: https://mlsanity.com/paper/2505.20930/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 3 of 3)

- [Accelerating System Adequacy Assessment using the Multilevel Monte Carlo Approach](https://mlsanity.com/paper/1910.13013/) (2019)
- [Minimising Unserved Energy Using Heterogeneous Storage Units](https://mlsanity.com/paper/1807.09044/) (2018)
- [Multilevel Monte Carlo with Surrogate Models for Resource Adequacy Assessment](https://mlsanity.com/paper/2203.03437/) (2022)

## Abstract (arXiv)

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.