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 CO2, 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 R2. 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.
Highlights
The main function of an Heating Ventilating and Air-Conditioning (HVAC) system is to satisfy and maintain the requirements with respect to air and building environment in general, for example, thermal comfort, air pollution control and the hygienic aspect, which is achieved by conditioning outdoor air to the desired levels in occupied buildings or for product processing and transporting the air in the room through an exhaust duct
The main scope of this paper is to study different model approaches using data measured in an industrial welding area of an automobile body shop
Fine dust or Particulate matter (PM) is the term used to describe particles in the air that do not sink to the ground immediately, but remain in the atmosphere for a certain period of time
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The main function of an HVAC system is to satisfy and maintain the requirements with respect to air and building environment in general, for example, thermal comfort, air pollution control and the hygienic aspect, which is achieved by conditioning outdoor air to the desired levels in occupied buildings or for product processing and transporting the air in the room through an exhaust duct. It controls and maintains temperature, humidity, air movement, air cleanliness and pressure differential within a defined space. The main scope of this paper is to study different model approaches using data measured in an industrial welding area of an automobile body shop. By using machine learning algorithms, we show that by using commonly available sensors in a production hall, it is possible to correlate fine dust data cost-effectively
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