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실내 이산화탄소 농도 예측을 위한 기계학습 모델 검증Verification of Machine Learning Algorithm for CO2 Prediction in Building

Authors
김효준조영흠류성룡
Issue Date
Dec-2020
Publisher
한국건축친환경설비학회
Keywords
Carbon dioxide concentration; Prediction model; Machine learning; 이산화탄소 농도; 예측모델; 기계학습
Citation
한국건축친환경설비학회 논문집, v.14, no.6, pp.699 - 706
Journal Title
한국건축친환경설비학회 논문집
Volume
14
Number
6
Start Page
699
End Page
706
URI
https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/18531
ISSN
1976-6483
Abstract
The objective of this study is to develop prediction model of indoor carbon dioxide (CO2) concentration using machine learning algorithm. Indoor CO2 concentration is one of the indicators of indoor ventilation standard, and indoor air quality and ventilation performance can be checked through CO2 concentration. The machine learning model is a method of analyzing the relationship between measured input/output data and does not require a high level of theoretical knowledge about the output value to be predicted, making it easy to develop a prediction model. In this study, a CO2 prediction model was developed using an artificial neural network, a support vector machine, a random forest, and a K-nearest neighbor algorithm based on the existing HVAC system operation data. When comparing the performance of the developed CO2 prediction model, the ANN model showed high performance. As a result of analyzing the time series data using the developed model, the measured indoor CO2 concentration and the CO2 concentration of the prediction model were similar, but on average, a relative error of less than about 5% occurred.
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