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.

中文翻译：

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随机提前调度的场景减少：基于混合自动编码器的时间序列聚类方法

基于场景的随机调度在解决可再生能源的不确定性和风险评估方面引起了全世界的极大兴趣。重要的是要生成可再生能源的有代表性的时间序列方案，同时保持方案的维度易于处理。本文提出了一种基于混合自动编码器的聚类方法，可以从高维时间序列中选择一个简化的场景集。与以最小化不同概率距离为目标的其他技术相比，所提出的体系结构在大量场景中考虑了模式识别。案例研究验证了该模型的有效性，该案例研究使用了Bonneville电力局和Elia的数据集。数值结果表明，该模型优于现有技术，