# Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier

arXiv: https://arxiv.org/abs/2310.08601 · HTML: https://mlsanity.com/paper/2310.08601/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 7 Oct 2023.

## Builds on (most cited first, 18 of 18)

- [Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning](https://mlsanity.com/paper/1908.08729/) (2019)
- [Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods](https://mlsanity.com/paper/1909.10461/) (2019)
- [Regularization via Mass Transportation](https://mlsanity.com/paper/1710.10016/) (2017)
- [Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems](https://mlsanity.com/paper/1902.01697/) (2019)
- [Online Mixed-Integer Optimization in Milliseconds](https://mlsanity.com/paper/1907.02206/) (2019)
- [Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets](https://mlsanity.com/paper/1802.09639/) (2018)
- [Learning Warm-Start Points for AC Optimal Power Flow](https://mlsanity.com/paper/1905.08860/) (2019)
- [Learning for DC-OPF: Classifying active sets using neural nets](https://mlsanity.com/paper/1902.05607/) (2019)
- [Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch](https://mlsanity.com/paper/2112.13469/) (2021)
- [Leveraging power grid topology in machine learning assisted optimal power flow](https://mlsanity.com/paper/2110.00306/) (2021)
- [Solutions of DC OPF are Never AC Feasible](https://mlsanity.com/paper/1912.00319/) (2019)
- [Data-Driven Screening of Network Constraints for Unit Commitment](https://mlsanity.com/paper/1907.04694/) (2019)
- [Implied Constraint Satisfaction in Power System Optimization: The Impacts of Load Variations](https://mlsanity.com/paper/1904.01757/) (2019)
- [Is learning for the unit commitment problem a low-hanging fruit?](https://mlsanity.com/paper/2106.11687/) (2021)
- [Unit Commitment using Nearest Neighbor as a Short-Term Proxy](https://mlsanity.com/paper/1611.10215/) (2016)
- [Convex Hulls for the Unit Commitment Polytope](https://mlsanity.com/paper/1701.08943/) (2017)
- [Global Solution Strategies for the Network-Constrained Unit Commitment Problem with AC Transmission Constraints](https://mlsanity.com/paper/1801.07218/) (2018)
- [An MISOCP-Based Decomposition Approach for the Unit Commitment Problem with AC Power Flows](https://mlsanity.com/paper/2201.01496/) (2022)

## Abstract (arXiv)

The system operators usually need to solve large-scale unit commitment problems within limited time frame for computation. This paper provides a pragmatic solution, showing how by learning and predicting the on/off commitment decisions of conventional units, there is a potential for system operators to warm start their solver and speed up their computation significantly. For the prediction, we train linear and kernelized support vector machine classifiers, providing an out-of-sample performance guarantee if properly regularized, converting to distributionally robust classifiers. For the unit commitment problem, we solve a mixed-integer second-order cone problem. Our results based on the IEEE 6- and 118-bus test systems show that the kernelized SVM with proper regularization outperforms other classifiers, reducing the computational time by a factor of 1.7. In addition, if there is a tight computational limit, while the unit commitment problem without warm start is far away from the optimal solution, its warmly-started version can be solved to (near) optimality within the time limit.