Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing

Yose Wandy, Marcus Vogt, Rushit Kansara, Clemens Felsmann and Christoph Herrmann
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Yose Wandy: Chair of Building Energy Systems and Heat Supply, Institute of Energy Technology, Technische Universität Dresden, Helmholtzstr. 14, 01069 Dresden, Germany
Marcus Vogt: Chair of Sustainable Manufacturing and Life Cycle Engineering, Institute of Machine Tools and Production Technology, Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany
Rushit Kansara: Chair of Sustainable Manufacturing and Life Cycle Engineering, Institute of Machine Tools and Production Technology, Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany
Clemens Felsmann: Chair of Building Energy Systems and Heat Supply, Institute of Energy Technology, Technische Universität Dresden, Helmholtzstr. 14, 01069 Dresden, Germany
Christoph Herrmann: Chair of Sustainable Manufacturing and Life Cycle Engineering, Institute of Machine Tools and Production Technology, Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany

Energies, 2021, vol. 14, issue 21, 1-16

Abstract: The alternative control concept using emission from the machine has the potential to reduce energy consumption in HVAC systems. This paper reports on a study of alternative inputs for a control system of HVAC using machine learning algorithms, based on data that are gathered in a welding area of an automotive factory. A data set of CO 2 , fine dust, temperatures and air velocity was logged using continuous and gravimetric measurements during two typical production weeks. The HVAC system was reduced gradually each day to trigger fluctuations of emission. The data were used to train and test various machine learning models using different statistical indices, consequently to choose a best fit model. Different models were tested and the Long Short-Term Memory model showed the best result, with 0.821 discrepancy on R 2 . The gravimetric samples proved that the reduction of air exchange rate does not correlate to escalation of fine dust linearly, which means one cannot rely on just gravimetric samples for HVAC system optimization. Furthermore, by using machine learning algorithms, this study shows that by using commonly available low cost sensors in a production hall, it is possible to correlate fine dust data cost effectively and reduce electricity consumption of the HVAC.

Keywords: machine learning; HVAC; control system; body shop; automotive industry (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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