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Identifying faults in the building system based on model prediction and residuum analysis

  • The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.

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Metadaten
Author:Michael Parzinger, Ulrich Wellisch, Lucia Hanfstaengl, Ferdinand Sigg, Markus Wirnsberger, Uli Spindler
URL:https://doi.org/10.1051/e3sconf/202017222001
Parent Title (English):E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)
Document Type:Conference Proceeding
Language:English
Publication Year:2020
Tag:HVAC; fault detection; model prediction; residuum analysis
Volume:172
First Page:22001
Project Title:ENOB: InsituNachweis: Entwicklung einer Methodik zur Bestimmung der Energieeffizienz von Gebäuden auf Basis von optimierten in situ Messungen. Teilvorhaben: Entwicklung eines kostengünstigen Basis-Monitoring-Ansatzes mit Beteiligung am IEA-EBC-Annex 71
Acronym:Insitu Nachweisverfahren
Funding Agency:Bundesministerium für Wirtschaft und Energie
Grant Number:03ET1509C
faculties / departments:Fakultät für Angewandte Natur- und Geisteswissenschaften
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 51 Mathematik