Effective End-to-End Learning Framework for Economic Dispatch

• URL: http://arxiv.org/abs/2002.12755v1
• Date: Sat, 22 Feb 2020 08:04:27 GMT
• Title: Effective End-to-End Learning Framework for Economic Dispatch
• Authors: Chenbei Lu, Kui Wang, Chenye Wu
• Abstract summary: We adopt the notion of end-to-end machine learning and propose a task-specific learning criteria to conduct economic dispatch. We provide both theoretical analysis and empirical insights to highlight the effectiveness and efficiency of the proposed learning framework.
• Score: 3.034038412630808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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