Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908599756783616 |
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| author | Moradi, Alireza Tanneau, Mathieu Zandehshahvar, Reza Van Hentenryck, Pascal |
| author_facet | Moradi, Alireza Tanneau, Mathieu Zandehshahvar, Reza Van Hentenryck, Pascal |
| contents | Accurate forecasting is critical for reliable power grid operations, particularly as the share of renewable generation, such as wind and solar, continues to grow. Given the inherent uncertainty and variability in renewable generation, probabilistic forecasts have become essential for informed operational decisions. However, such forecasts frequently suffer from calibration issues, potentially degrading decision-making performance. Building on recent advances in Conformal Predictions, this paper introduces a tailored calibration framework that constructs context-aware calibration sets using a novel weighting scheme. The proposed framework improves the quality of probabilistic forecasts at the site and fleet levels, as demonstrated by numerical experiments on large-scale datasets covering several systems in the United States. The results demonstrate that the proposed approach achieves higher forecast reliability and robustness for renewable energy applications compared to existing baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15780 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction Moradi, Alireza Tanneau, Mathieu Zandehshahvar, Reza Van Hentenryck, Pascal Applications Machine Learning Accurate forecasting is critical for reliable power grid operations, particularly as the share of renewable generation, such as wind and solar, continues to grow. Given the inherent uncertainty and variability in renewable generation, probabilistic forecasts have become essential for informed operational decisions. However, such forecasts frequently suffer from calibration issues, potentially degrading decision-making performance. Building on recent advances in Conformal Predictions, this paper introduces a tailored calibration framework that constructs context-aware calibration sets using a novel weighting scheme. The proposed framework improves the quality of probabilistic forecasts at the site and fleet levels, as demonstrated by numerical experiments on large-scale datasets covering several systems in the United States. The results demonstrate that the proposed approach achieves higher forecast reliability and robustness for renewable energy applications compared to existing baselines. |
| title | Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2510.15780 |