A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions

KiJeon Nam, SungKu Heo, Qian Li, Jorge Loy-Benitez, MinJeong Kim, DuckShin Park and ChangKyoo Yoo

Applied Energy, 2020, vol. 266, issue C, No S0306261920304050

Abstract: Passengers are directly or indirectly exposed to fine dust in indoor air of underground subway stations, which greatly affects the comfort and health of the passengers. However, conventional ventilation systems are manually operated, resulting in high energy consumption, and it is hard to consider the dynamic characteristics of indoor air quality due to the complex relationships between the environment of the subway station and climate change-driven outdoor air quality. Therefore, an energy-efficient ventilation optimization system based on deep learning and artificial intelligence (AI)-iterative dynamic programming was developed in this study for proactive environmental and economic maintenance of the underground ventilation system for a subway’s indoor air quality. The deep learning model predicted the next 24 h of the subway’s environmental status, and the AI-iterative dynamic programming searched a piecewise operational policy of ventilation flow rate for the same operational duration. Energy efficiency was improved by 8.68% while maintaining healthy indoor air quality for the passengers. The proactive optimal ventilation system for the platform of a target subway station presented a decrease of 96 tons of CO2 per year to help address climate change and operating expenditure savings of up to $4217 dollars per year.

Keywords: Artificial intelligence; Deep learning; Dynamic programming; Outdoor air quality; Proactive ventilation system; Energy efficiency (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920304050
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:266:y:2020:i:c:s0306261920304050

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.114893

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().