In-situ sensor calibration in an operational air-handling unit coupling autoencoder and Bayesian inference

•This paper figures out limitations of the existing in-situ sensor calibration in an operational air-handling unit (AHU).•An advanced in-situ sensor calibration method is proposed by coupling autoencoder and Bayesian inference.•A three-step strategy to construct autoencoder input variables is sugges...

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Vydáno v:Energy and buildings Ročník 221; s. 110026
Hlavní autor: Yoon, Sungmin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Lausanne Elsevier B.V 15.08.2020
Elsevier BV
Témata:
Air-handling unit
AutoEncoder
Automation
Bayesian analysis
Bayesian inference
Calibration
Cooling coils
Coupling
Energy
Energy consumption
Energy industry
Energy usage
Fault detection
Fault detection and diagnosis (FDD)
Fault diagnosis
Handling
Mathematical models
Neural networks
Sensors
Statistical inference
Systematic errors
Virtual in-situ calibration (VIC)
Sensors
Fault detection and diagnosis (FDD)
Bayesian inference
AutoEncoder
Virtual in-situ calibration (VIC)
Air-handling unit
ISSN:0378-7788, 1872-6178
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Shrnutí:•This paper figures out limitations of the existing in-situ sensor calibration in an operational air-handling unit (AHU).•An advanced in-situ sensor calibration method is proposed by coupling autoencoder and Bayesian inference.•A three-step strategy to construct autoencoder input variables is suggested with a new distance function to reach good calibration performance in various faulty conditions.•The faulty AHU operation is returned by the in-situ calibration to the normal operation, which results in the system energy savings. Sensor errors have a considerable influence on the system operation and energy usage in an air handling unit. Sensor fault detection and diagnosis (SFDD) has been widely studied to handle the impacts of sensor errors on an air-handling unit (AHU). Beyond the SFDD, in-situ calibration can correct the faulty sensor (especially for systematic errors) automatically in field, thereby reducing the energy waste. In this study, we propose an advanced in-situ sensor calibration named virtual in-situ calibration in an operational AHU. The suggested method is intended to overcome the challenges of previous in-situ calibration methods by coupling the Bayesian inference and autoencoder. In a given sensor calibration domain of an AHU, based on the unsupervised learning neural network feature trained to duplicate their input variables, the autoencoder-coupled calibration can produce system and sensor models effectively without additional sensors and assumptions, which are the main limitations of the earlier methods. It improves the calibration performance and applicability in the AHU. In addition, a three-step strategy to construct autoencoder input variables and a new distance function to achieve successful calibration under various faulty conditions is proposed. In a case study, where the error in the cooling coil supply temperature (+2 °C) caused a total energy increase of 38%, the present method is shown to eliminate the sensor error and the energy waste completely. These results show the capabilities and potentials of the suggested method in the self-repair, diagnostics, and automation of a building energy sector.
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ISSN:0378-7788
1872-6178
DOI:10.1016/j.enbuild.2020.110026