Lee, H. -S.Tekin, CemSchaar, M.Lee, J. -W.2018-04-122018-04-122017http://hdl.handle.net/11693/37631Date of Conference: 7-9 December 2016Conference Name: IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016In this paper, we study a unit commitment (UC) problem minimizing operating costs of the power system with renewable energy sources. We develop a contextual learning algorithm for UC (CLUC) which learns which UC schedule to choose based on the context information such as past load demand and weather condition. CLUC does not require any prior knowledge on the uncertainties such as the load demand and the renewable power outputs, and learns them over time using the context information. We characterize the performance of CLUC analytically, and prove its optimality in terms of the long-term average cost. Through the simulation results, we show the performance of CLUC and the effectiveness of utilizing the context information in the UC problem.EnglishLearningRenewable energyUncertaintyUnit commitmentLearning algorithmsNatural resourcesOperating costsSemanticsContext informationContextual learningRenewable energy sourceUnit commitment problemRenewable energy resourcesConference Paper10.1109/GlobalSIP.2016.7905966