Open AccessMachine learning-based modeling and controller tuning of a heat pump
Author(s) -
Mohammad Khosravi,
Nicolas Schmid,
Annika Eichler,
Philipp Heer,
Roy S. Smith
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1343/1/012065
Subject(s) - controller (irrigation) , control theory (sociology) , computer science , nonlinear system , artificial neural network , control engineering , open loop controller , feed forward , engineering , control (management) , closed loop , artificial intelligence , physics , quantum mechanics , agronomy , biology
In this paper, we consider the problem of controller tuning for an operating unit in a building energy system. The illustrative example used here is a real heat pump located in the NEST building at Empa, Dubendorf, Zurich, with its outflow temperature controlled by a PI-controller. The plant is in use and accordingly, intervening in its normal operation is not allowed. Moreover, the model of plant is not available or it can be changed due to aging or possible modification. Accordingly, it is desired to utilize a tuning method which is model-free, operates online, does not intervene with the normal operation of the plant and use only the available historical measurement data. Additionally, it is required to guarantee the safety of the plant during the tuning procedure. In this regard, we formulate the controller tuning problem as a black-box optimization and adopt a safe Bayesian optimization approach for controller parameters tuning. In order to assess numerically the performances of the scheme, first we model the plant as a nonlinear ARX model in form of a feedforward neural network. Subsequently, we train the neural network using the available historical measurement data. Finally, the obtained model is used as an oracle in the controller tuning procedure in order to numerically verify the effectivity of the proposed approach.