Effective End-to-End Learning Framework for Economic Dispatch

Autor:	Lu, Chenbei, Wang, Kui, Wu, Chenye
Rok vydání:	2020
Předmět:	Mathematics - Optimization and Control Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.12755 Zobrazit plný text záznamu View this record from Arxiv