Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors

Hsieh Chih Hsu; Chen Yu Pan

doi:10.1109/ICASI57738.2023.10179512

Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors

Hsieh Chih Hsu, Chen Yu Pan

Department of Architecture

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Indoor air quality reduces pollutants through different ventilation methods. Using different ventilation strategies is the focus of most scholars with limited resources. Therefore, we use outdoor environmental factors to data-driven control mechanical ventilation facilities.This proposed framework also optimizes the deep learning model (LSTM) through clustering analysis, and through cross-validation, the accuracy of the model is 97.45%. At the same time, this model can reduce energy consumption by 53%.

Original language	English
Title of host publication	2023 9th International Conference on Applied System Innovation, ICASI 2023
Editors	Shoou-Jinn Chang, Sheng-Joue Young, Artde Donald Kin-Tak Lam, Liang-Wen Ji, Stephen D. Prior
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	80-82
Number of pages	3
ISBN (Electronic)	9798350398380
DOIs	https://doi.org/10.1109/ICASI57738.2023.10179512
Publication status	Published - 2023
Event	9th International Conference on Applied System Innovation, ICASI 2023 - Chiba, Japan Duration: 2023 Apr 21 → 2023 Apr 25

Publication series

Name	2023 9th International Conference on Applied System Innovation, ICASI 2023

Conference

Conference	9th International Conference on Applied System Innovation, ICASI 2023
Country/Territory	Japan
City	Chiba
Period	23-04-21 → 23-04-25

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Hardware and Architecture
Information Systems
Information Systems and Management
Electrical and Electronic Engineering
Mechanical Engineering
Electronic, Optical and Magnetic Materials

Access to Document

10.1109/ICASI57738.2023.10179512

Cite this

Hsu, H. C., & Pan, C. Y. (2023). Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors. In S.-J. Chang, S.-J. Young, A. D. K.-T. Lam, L.-W. Ji, & S. D. Prior (Eds.), 2023 9th International Conference on Applied System Innovation, ICASI 2023 (pp. 80-82). (2023 9th International Conference on Applied System Innovation, ICASI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI57738.2023.10179512

Hsu, Hsieh Chih ; Pan, Chen Yu. / Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors. 2023 9th International Conference on Applied System Innovation, ICASI 2023. editor / Shoou-Jinn Chang ; Sheng-Joue Young ; Artde Donald Kin-Tak Lam ; Liang-Wen Ji ; Stephen D. Prior. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 80-82 (2023 9th International Conference on Applied System Innovation, ICASI 2023).

@inproceedings{d7bd9fa469b043009d59f5fdc6356cb6,

title = "Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors",

abstract = "Indoor air quality reduces pollutants through different ventilation methods. Using different ventilation strategies is the focus of most scholars with limited resources. Therefore, we use outdoor environmental factors to data-driven control mechanical ventilation facilities.This proposed framework also optimizes the deep learning model (LSTM) through clustering analysis, and through cross-validation, the accuracy of the model is 97.45%. At the same time, this model can reduce energy consumption by 53%.",

author = "Hsu, {Hsieh Chih} and Pan, {Chen Yu}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th International Conference on Applied System Innovation, ICASI 2023 ; Conference date: 21-04-2023 Through 25-04-2023",

year = "2023",

doi = "10.1109/ICASI57738.2023.10179512",

language = "English",

series = "2023 9th International Conference on Applied System Innovation, ICASI 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "80--82",

editor = "Shoou-Jinn Chang and Sheng-Joue Young and Lam, {Artde Donald Kin-Tak} and Liang-Wen Ji and Prior, {Stephen D.}",

booktitle = "2023 9th International Conference on Applied System Innovation, ICASI 2023",

address = "United States",

}

Hsu, HC & Pan, CY 2023, Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors. in S-J Chang, S-J Young, ADK-T Lam, L-W Ji & SD Prior (eds), 2023 9th International Conference on Applied System Innovation, ICASI 2023. 2023 9th International Conference on Applied System Innovation, ICASI 2023, Institute of Electrical and Electronics Engineers Inc., pp. 80-82, 9th International Conference on Applied System Innovation, ICASI 2023, Chiba, Japan, 23-04-21. https://doi.org/10.1109/ICASI57738.2023.10179512

Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors. / Hsu, Hsieh Chih; Pan, Chen Yu.
2023 9th International Conference on Applied System Innovation, ICASI 2023. ed. / Shoou-Jinn Chang; Sheng-Joue Young; Artde Donald Kin-Tak Lam; Liang-Wen Ji; Stephen D. Prior. Institute of Electrical and Electronics Engineers Inc., 2023. p. 80-82 (2023 9th International Conference on Applied System Innovation, ICASI 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors

AU - Hsu, Hsieh Chih

AU - Pan, Chen Yu

PY - 2023

Y1 - 2023

N2 - Indoor air quality reduces pollutants through different ventilation methods. Using different ventilation strategies is the focus of most scholars with limited resources. Therefore, we use outdoor environmental factors to data-driven control mechanical ventilation facilities.This proposed framework also optimizes the deep learning model (LSTM) through clustering analysis, and through cross-validation, the accuracy of the model is 97.45%. At the same time, this model can reduce energy consumption by 53%.

AB - Indoor air quality reduces pollutants through different ventilation methods. Using different ventilation strategies is the focus of most scholars with limited resources. Therefore, we use outdoor environmental factors to data-driven control mechanical ventilation facilities.This proposed framework also optimizes the deep learning model (LSTM) through clustering analysis, and through cross-validation, the accuracy of the model is 97.45%. At the same time, this model can reduce energy consumption by 53%.

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U2 - 10.1109/ICASI57738.2023.10179512

DO - 10.1109/ICASI57738.2023.10179512

M3 - Conference contribution

AN - SCOPUS:85167364030

T3 - 2023 9th International Conference on Applied System Innovation, ICASI 2023

SP - 80

EP - 82

BT - 2023 9th International Conference on Applied System Innovation, ICASI 2023

A2 - Chang, Shoou-Jinn

A2 - Young, Sheng-Joue

A2 - Lam, Artde Donald Kin-Tak

A2 - Ji, Liang-Wen

A2 - Prior, Stephen D.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th International Conference on Applied System Innovation, ICASI 2023

Y2 - 21 April 2023 through 25 April 2023

ER -

Hsu HC, Pan CY. Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors. In Chang SJ, Young SJ, Lam ADKT, Ji LW, Prior SD, editors, 2023 9th International Conference on Applied System Innovation, ICASI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 80-82. (2023 9th International Conference on Applied System Innovation, ICASI 2023). doi: 10.1109/ICASI57738.2023.10179512

Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors

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