A Data-Driven Identification Procedure for HVAC Processes with Laboratory and Real-World Validation

Abstract

Linear system identification is a well-known methodology for building mathematical models of dynamic systems from observed input–output data. It also represents an essential tool for model-based control design, adaptive control and other advanced control techniques. Use of linear identification is, however, often limited to academic environment and to research facilities equipped with scientific computing platforms and highly qualified staff. Common industrial or building control system technology rarely uses these advanced design techniques. The main obstacle is typically lack of experience with their practical implementation. In this article, a procedure is proposed, implemented, and tested, that brings the benefits of linear identification into broader control system practice. The open-source DCU control system platform with its advanced control framework is used for implementation of the proposed linear identification procedure. The procedure is experimentally tested in the laboratory setting using a unique model of HVAC system as well as in real-world environment in an experimental two storey family house. Testing this novel feature of the control system has proved satisfactory results, while some of them are presented in graphical and numerical form.