MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909712004415488 |
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| author | Zhang, Ruiqi Tindemans, Simon H. |
| author_facet | Zhang, Ruiqi Tindemans, Simon H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20930 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Zhang, Ruiqi Tindemans, Simon H. Machine Learning 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. |
| title | MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.20930 |