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Heating, Ventilation, and Air Conditioning (HVAC) Noise Detection in Open-Plan Offices Using Recursive Partitioning

Auteur(s):


Médium: article de revue
Langue(s): en 
Publié dans: Buildings, , n. 12, v. 8
Page(s): 169
DOI: 10.3390/buildings8120169
Abstrait:

Open-plan offices have lower construction costs, allowing for significant savings in space and, according to designers, facilitate communication between workers, thus, improving collaboration, as well as the exchange of ideas. For these reasons, this type of office has become widespread, while highlighting numerous limitations and various problems. These include the control of anthropic and electromechanical noise. In this study, measurements of the noise emitted by a heating, ventilation, and air conditioning (HVAC) system were carried out in an open-plan office. The average spectral levels in a 1/3 octave band were compared through correlation analysis, to identify any redundant data. A model was then adapted to evaluate the importance of the variables, in order to classify the characteristics, by importance. To reduce the number of predictor variables, a selection analysis of the characteristics was carried out. A subset of predictors was extracted to be used to produce an accurate prediction model. Finally, a model based on recursive partitioning, to detect the operating conditions of an HVAC system, was developed and applied, so as to provide insights into the development and application of this technique, in these contexts. The high accuracy of the model (Accuracy = 0.9981) suggests the adoption of this tool for several applications.

Copyright: © 2018 by the authors; licensee MDPI, Basel, Switzerland.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10324847
  • Publié(e) le:
    22.07.2019
  • Modifié(e) le:
    02.06.2021