Logistic regression based multi-objective optimization of IAQ ventilation system considering healthy risk and ventilation energy

Sehee Pyo, Seungchul Lee, Minjeong Kim, Jeong Tai Kim, Changkyoo Yoo

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

Till date, conventional indoor air quality (IAQ) ventilation systems have controlled the IAQ using fixed ventilation rate strategy without consideration of ventilation energy consumption and outdoor air quality. In this paper, a multiobjective optimization (MOO) method was used to find optimal set-points of IAQ ventilation system which balance the IAQ improvement and ventilation energy saving, where logistic regression was suggested to classify the current IAQ data with the healthy risk level. The results show that the proposed ventilation system with varying setpoints can save the ventilation energy and also improve the IAQ level better than the existing ventilation system.

Original languageEnglish
Pages (from-to)583-589
Number of pages7
JournalEnergy Procedia
Volume62
DOIs
Publication statusPublished - 2014
Event6th International Conference on Sustainability in Energy and Buildings, SEB 2014 - Cardiff, Wales, United Kingdom
Duration: 25 Jun 201427 Jun 2014

Bibliographical note

Publisher Copyright:
© 2014 The Author.

Keywords

  • Energy saving
  • Indoor air quality (IAQ)
  • Logistic regression
  • Multiobjective optimization (MOO)
  • Ventilation control system

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