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

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yose Wandy, Marcus Vogt, Rushit Kansara, Clemens Felsmann, Christoph Herrmann
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning HVAC control system body shop automotive industry Technology T
Acceso en línea:	https://doaj.org/article/3c63834dad5c4c6b80c6ecfe91d9178a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3c63834dad5c4c6b80c6ecfe91d9178a
record_format	dspace
spelling	oai:doaj.org-article:3c63834dad5c4c6b80c6ecfe91d9178a2021-11-11T16:01:23ZSensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing10.3390/en142172711996-1073https://doaj.org/article/3c63834dad5c4c6b80c6ecfe91d9178a2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7271https://doaj.org/toc/1996-1073The 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>, 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>. 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.Yose WandyMarcus VogtRushit KansaraClemens FelsmannChristoph HerrmannMDPI AGarticlemachine learningHVACcontrol systembody shopautomotive industryTechnologyTENEnergies, Vol 14, Iss 7271, p 7271 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning HVAC control system body shop automotive industry Technology T
spellingShingle	machine learning HVAC control system body shop automotive industry Technology T Yose Wandy Marcus Vogt Rushit Kansara Clemens Felsmann Christoph Herrmann Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing
description	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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>, 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>. 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.
format	article
author	Yose Wandy Marcus Vogt Rushit Kansara Clemens Felsmann Christoph Herrmann
author_facet	Yose Wandy Marcus Vogt Rushit Kansara Clemens Felsmann Christoph Herrmann
author_sort	Yose Wandy
title	Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing
title_short	Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing
title_full	Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing
title_fullStr	Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing
title_full_unstemmed	Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing
title_sort	sensor-based machine learning approach for indoor air quality monitoring in an automobile manufacturing
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3c63834dad5c4c6b80c6ecfe91d9178a
work_keys_str_mv	AT yosewandy sensorbasedmachinelearningapproachforindoorairqualitymonitoringinanautomobilemanufacturing AT marcusvogt sensorbasedmachinelearningapproachforindoorairqualitymonitoringinanautomobilemanufacturing AT rushitkansara sensorbasedmachinelearningapproachforindoorairqualitymonitoringinanautomobilemanufacturing AT clemensfelsmann sensorbasedmachinelearningapproachforindoorairqualitymonitoringinanautomobilemanufacturing AT christophherrmann sensorbasedmachinelearningapproachforindoorairqualitymonitoringinanautomobilemanufacturing
_version_	1718432407542038528

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

Ejemplares similares