论文标题
优化的时间序列聚合：一定大小的全部？
Time series aggregation for optimization: One-size-fits-all?
论文作者
论文摘要
使用使用不同时间序列作为数据输入的优化模型的基本问题之一是模型准确性和计算障碍性之间的权衡。为了克服这些完整优化模型的计算棘手性，输入数据和模型大小的维度通常通过时间序列聚合（TSA）方法降低。但是，传统的TSA方法通常基于以下通常的信念，即最近似输入数据的群集还会导致汇总的模型，从而最能近似完整模型，而真正重要的指标 - 最重要的指标 - 最终的优化结果中产生的输出误差 - 无法很好地解决。在本文中，我们计划挑战这种信念，并表明具有理论基础的基于输出的TSA方法具有空前的计算效率和准确性潜力。
One of the fundamental problems of using optimization models that use different time series as data input, is the trade-off between model accuracy and computational tractability. To overcome the computational intractability of these full optimization models, the dimension of input data and model size is commonly reduced through time series aggregation (TSA) methods. However, traditional TSA methods often apply a one-size-fits-all approach based on the common belief that the clusters that best approximate the input data also lead to the aggregated model that best approximates the full model, while the metric that really matters - the resulting output error in optimization results - is not well addressed. In this paper, we plan to challenge this belief and show that output-error based TSA methods with theoretical underpinnings have unprecedented potential of computational efficiency and accuracy.
