文章基本信息

标题：Hardware-in-the-loop test of learning-based controllers for grid-supportive building heating operation
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作者：Lilli Frison ; Sweetin Paul ; Torsten Koller 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：17107-17112
DOI：10.1016/j.ifacol.2020.12.1652
语种：English
出版社：Elsevier
摘要：AbstractWhile MPC is the state-of-the-art approach for building heating control with proven cost savings and improvement in energy flexibility, in practice, buildings are operated by simple rules-based controllers which are not able to accomplish an energy efficient and flexible operation. This paper explores the suitability of deep neural networks for approximating optimal economic MPC strategies for this task. In particular, we develop a convolutional neural network controller and test it in a closed-loop simulation against MPC and an improved predictive rule-based controller. The learned controller is easy to implement and fast to process on standard building control hardware. The feasibility, performance and robustness of the learned controller is validated in a realistic hardware-in-the-loop test setup for the demand-responsive operation of a heat pump combined with a storage tank.
关键词：KeywordsLearning-based ControlNeural Network ControlEconomic MPCEnergy StorageOperationPlanningBuilding AutomationSmart Grids