Scenario Reduction for Stochastic Day-Ahead Scheduling: A Mixed Autoencoder Based Time-Series Clustering Approach
| Autor: | Wenyuan Tang, Junkai Liang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
021103 operations research General Computer Science Artificial neural network business.industry Computer science Stochastic process 020209 energy 0211 other engineering and technologies 02 engineering and technology Autoencoder Renewable energy Scheduling (computing) 0202 electrical engineering electronic engineering information engineering Time series Cluster analysis business Curse of dimensionality |
| Zdroj: | IEEE Transactions on Smart Grid. 12:2652-2662 |
| ISSN: | 1949-3061 1949-3053 |
| DOI: | 10.1109/tsg.2020.3047759 |
| Popis: | Scenario based stochastic scheduling has drawn a tremendous amount of interests worldwide in tackling the uncertainty of renewable energy and accounting for risks. It is important to generate representative time-series scenarios of renewable energy, while keeping the dimensionality of the scenario set tractable. This article presents a mixed autoencoder based clustering approach to select a reduced scenario set from high-dimensional time series. In contrast to other techniques targeting on minimizing different probability distances, the proposed architecture accounts for the pattern recognition within a large set of scenarios. The effectiveness of the model is verified in the case studies, where the data sets from the Bonneville Power Administration and Elia are used. The numerical results show that the model outperforms the state of the art, in terms of statistical metrics and through empirical analysis. |
| Databáze: | OpenAIRE |
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