期刊
ENERGY AND BUILDINGS
卷 38, 期 2, 页码 142-147出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2005.06.002
关键词
load shifting; thermal energy storage (TES); optimal control; learning control; reinforcement learning
This paper is the first part of a two-part investigation of a novel approach to optimally control commercial building passive and active thermal storage inventory. The proposed building control approach is based on simulated reinforcement learning, which is a hybrid control scheme that combines features of model-based optimal control and model-free learning control. An experimental study was carried out to analyze the performance of a hybrid controller installed in a full-scale laboratory facility. The first part presents an overview of the project with an emphasis on the theoretical foundation. The motivation of the research will be introduced first, followed by a review of past work. A brief introduction of the theory is provided including classic reinforcement learning and its variation, so-called simulated reinforcement learning, which constitutes the basic architecture of the hybrid learning controller. A detailed discussion of the experimental results will be presented in the companion paper. (c) 2005 Elsevier B.V. All rights reserved.
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