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Identification of room temperature models using k-step PEM for Hammerstein systems

: Paschke, Fabian; Zaiczek, Tobias; Röbenack, Klaus

Postprint urn:nbn:de:0011-n-5659887 (469 KByte PDF)
MD5 Fingerprint: fafaa633688df563e7b87c3a098b1c65
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Erstellt am: 5.12.2019

Institute of Electrical and Electronics Engineers -IEEE-:
23rd International Conference on System Theory, Control and Computing, ICSTCC 2019 : 9-11 October 2019, Sinaia, Romania
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-0699-1
ISBN: 978-1-7281-0698-4
ISBN: 978-1-7281-0700-4
International Conference on System Theory, Control and Computing (ICSTCC) <23, 2019, Sinaia>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
03ET1372A; FMopt
Verbundvorhaben: EnOB: FMopt: Verfahren zur Ressourcenminimierung im technischen Gebäudebetrieb. Teilvorhaben: Modellentwicklung und Optimierung
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IIS, Institutsteil Entwurfsautomatisierung (EAS) ()
system identification; hammerstein; Model Predictive Control (MPC)

The contribution focuses on the identification of short term room temperature prediction models using k-step ahead prediction error minimization (PEM). In the first part of the paper we describe the general identification problem and summarize the k-step PEM-algorithm for discrete time Hammerstein systems. In the second part the k-step ahead approach is applied to measured data of a conference room and compared to the standard 1-step PEM approach. The results indicate, that k-step ahead approach can indeed lead to better prediction performance in this particular practical application.