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Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems

  • Faults in Heating, Ventilation and Air Conditioning (HVAC) systems affect the energy efficiency of buildings. To date, there rarely exist methods to detect and diagnose faults during the operation of buildings that are both cost-effective and sufficient accurate. This study presents a method that uses artificial intelligence to automate the detection of faults in HVAC systems. The automated fault detection is based on a residual analysis of the predicted total heating power and the actual total heating power using an algorithm that aims to find an optimal decision rule for the determination of faults. The data for this study was provided by a detailed simulation of a residential case study house. A machine learning model and an ARX model predict the building operation. The model for fault detection is trained on a fault-free data set and then tested with a faulty operation. The algorithm for an optimal decision rule uses various statistical tests of residual properties such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that it is possible to predict faults for both known faults and unknown faults. The challenge is to find the optimal algorithm to determine the best decision rules. In the outlook of this study, further methods are presented that aim to solve this challenge.

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Metadaten
Author:Michael Parzinger, Lucia Hanfstaengl, Ferdinand Sigg, Uli Spindler, Ulrich Wellisch, Markus Wirnsberger
URL:https://doi.org/10.3390/su12176758
Parent Title (English):Sustainability
Document Type:Article (peer reviewed)
Language:English
Publication Year:2020
Tag:HVAC; fault detection; residual analysis
Volume:12
Issue:17
First Page:6758
Peer reviewed:Ja
Project Title:ENOB: In Situ Nachweis: 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-Annex71
Acronym:In Situ 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
5 Naturwissenschaften und Mathematik / 53 Physik