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2836. Unit Commitment Predictor
Invited abstract in session WA-3: Data Science and Optimization, stream Data Science Meets Optimization.
Wednesday, 8:30-10:00Room: 1005 (building: 202)
Authors (first author is the speaker)
| 1. | Farzaneh Pourahmadi
|
| Technical University of Denmark |
Abstract
The system operators usually need to solve large-scale unit commitment problems within limited time frame for computation. In this talk, we will discuss how by learning and predicting the on/off commitment decisions of conventional units, there is a potential for system operators to speed up their computation significantly. Additionally, we propose a data-driven unit commitment model enabling the system operator to utilize available contextual information in the unit commitment model to enhance decision-making efficiency. We explore whether, and if so to what extent, our proposed data-driven model outperforms stochastic models.
Keywords
- Mathematical Programming
- Critical Decision Making
- Forecasting
Status: accepted
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