论文标题
经济派遣的有效端到端学习框架
Effective End-to-End Learning Framework for Economic Dispatch
论文作者
论文摘要
提高经济调度有效性的传统观念是尽可能准确地设计负载预测方法。但是，由于系统成本和负载预测错误之间的时间和空间相关性，这种方法可能是有问题的。这促使我们采用端到端机器学习的概念，并提出特定于任务的学习标准以进行经济派遣。具体而言，为了最大化数据利用，我们为学习过程设计了有效的优化内核。我们提供理论分析和经验见解，以突出提出的学习框架的有效性和效率。
Conventional wisdom to improve the effectiveness of economic dispatch is to design the load forecasting method as accurately as possible. However, this approach can be problematic due to the temporal and spatial correlations between system cost and load prediction errors. This motivates us to adopt the notion of end-to-end machine learning and to propose a task-specific learning criteria to conduct economic dispatch. Specifically, to maximize the data utilization, we design an efficient optimization kernel for the learning process. We provide both theoretical analysis and empirical insights to highlight the effectiveness and efficiency of the proposed learning framework.
