HVAC systems are the largest energy consumers in a building and a clean HVAC system can get about 11% in energy saving. Moreover, particulate pollution represents one of the main causes of cancer death and several health damages. This paper presents an innovative and not invasive procedure for the automatic indoor air quality assessment that depends on HVAC cleaning conditions. It is based on a mathematical algorithm that processes a few on-site physical measurements that are acquired by dedicated sensors at suitable locations with a specif-ic time table. The output of the algorithm is a set of indexes that provide a snapshot of the sys-tem with separated zoom on filters and ducts. The proposed methodology contributes to opti-mize both HVAC maintenance procedures and air quality preservation. Robustness, portability and low implementation costs allow to plan maintenance intervention, limiting it only when standard HVAC working conditions need to be restored.

Non invasive indoor air quality control through HVAC systems cleaning state

M C Basile;V Bruni;D De Canditiis;D Vitulano
2016

Abstract

HVAC systems are the largest energy consumers in a building and a clean HVAC system can get about 11% in energy saving. Moreover, particulate pollution represents one of the main causes of cancer death and several health damages. This paper presents an innovative and not invasive procedure for the automatic indoor air quality assessment that depends on HVAC cleaning conditions. It is based on a mathematical algorithm that processes a few on-site physical measurements that are acquired by dedicated sensors at suitable locations with a specif-ic time table. The output of the algorithm is a set of indexes that provide a snapshot of the sys-tem with separated zoom on filters and ducts. The proposed methodology contributes to opti-mize both HVAC maintenance procedures and air quality preservation. Robustness, portability and low implementation costs allow to plan maintenance intervention, limiting it only when standard HVAC working conditions need to be restored.
2016
Istituto Applicazioni del Calcolo ''Mauro Picone''
HVAC
data regularization and prediction
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/331637
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact