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http://dx.doi.org/10.17661/jkiiect.2021.14.5.413

A Study on Machine Learning Model for Predicting Uncollected Parameters in Indoor Environment Evaluation  

Jeong, Jin-Hyoung (Department of Biomedical IT, Catholic Kwandong University)
Jo, Jae-Hyun (Department of Bio-medical Engineering, Catholic Kwandong University)
Kim, Seung-Hun (Department of Bio-medical Engineering, Catholic Kwandong University)
Bang, So-Hyeon (Department of Biomedical IT, Catholic Kwandong University)
Lee, Sang-Sik (Department of Bio-medical Engineering, Catholic Kwandong University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.14, no.5, 2021 , pp. 413-420 More about this Journal
Abstract
This study is about a machine learning model for predicting insufficient parameters through other parameters when one of the collected parameters is insufficient. A regression model was created to predict time, temperature, humidity, CO2, and light quantity data through the machine learning regression analysis function in Matlab. In addition, the three models with the lowest RMSE values for each parameter were selected and verified. For verification, the predicted values were obtained by applying the test data to the prediction model derived from each parameter, and the correlation coefficient and error average between the measured values and the obtained predicted values were obtained and then compared.
Keywords
Indoor environment; Machine learning; Prediction of variables; ICT; IoT;
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1 Andrei ClaudiuCosma, RahulSimha "Machine learning method for real-time non-invasive prediction of individual thermal preference in transient conditions", Building and Environment, Volume 148, 15 January 2019, Pages 372-383   DOI
2 Boram Lee, "Evaluation of Overall Comfort in Hospitals in Summer using Various Comforts Related Factors", Department of Environmental Health Graduate School of Public Health Seoul National University
3 Kim Seong-Hoon, Han Gi-Tae, "A Study of Indoor Air Pollution Prediction based on LSTM Model using Fine Dust Sensor Data in Kitchen Environment", Korea Institute Of Communication Sciences, 2020.2, 1127-1128(2 pages)
4 Kim, Da-young, Kim, Him-chan, Kim, Hyung-keun, Kim, Tae-yeon, "Health Risk Assessment by a Numerical Estimation Model for Indoor Air Quality Evaluation of Residential Buildings", Journal of Korean Institute of Architectural Sustainable Environment and Building Systems 10(1), 2016.2, 7-14(8 pages)
5 Boram Lee*, Daeyeop Lee**, Hyunkyung Ban*, Sewon Lee*, KyooSang Kim***, Kiyoung Lee*†, "Evaluation of Annual Indoor Environment Quality in Hospitals using Various Comfort-related Factors", J Environ Health Sci. 2017; 43(3): 214-222   DOI
6 L.T.Wong, K.W.Mui, P.S.Hui,"A multivariate-logistic model for acceptance of indoor environmental quality (IEQ) in offices", Building and Environment, Volume 43, Issue 1, January 2008, Pages 1-6   DOI
7 PoryaGhasemia, MasoudKarbasib, Alireza Zamani Nouric, MahdiSarai Tabrizia, Hazi Mohammad Azamathullad, "Application of Gaussian process regression to forecast multi-step ahead SPEI drought index", Alexandria Engineering Journal, Volume 60, Issue 6, December 2021, Pages 5375-5392   DOI
8 Wikipedia, "Decision Tree Learning"
9 Wikipedia, "support vector machine"
10 Ah Hyoun Kim, Ji Hyun Kim, Hyun Joong Kim, "The guideline for choosing the right-size of tree for boosting algorithm", Journal of the Korean Data And Information Science Society 23(5), 2012.10, 949-959 (11 pages)   DOI
11 Marcus M. Noack1*, Gregory S. Doerk2, Ruipeng Li3, Jason K. Streit4, RichardA.Vaia4, KevinG.Yager2* & Masafumi Fukuto3*, "Autonomous materials discovery driven byGaussian process regression with inhomogeneous measurement noise and anisotropic kernels", Scientifc Reports, (2020) 10:17663   DOI
12 JohannaKallioa, JaakkoTervonena , PauliRasanena, RikuMakynenb , JaniKoivusaaria, JohannesPeltolaa, "Forecasting office indoor CO2 concentration using machine learning with a one-year dataset, Building and Environment, Volume 187, January 2021, 107409   DOI
13 Wikipedia, "Root Mean Square Deviation; RMSD"
14 Jung Jaeyoun, Jeong Insoo, Bao Wei "A Study on the Improvement of Indoor Environment in the Underground Parking Lot of Apartments in Jeonju City", The Journal of the Korean Rural Architecture Association, Volume 22, No.1, No. 76, pp.1-12
15 Deog-Gyeong Yoon*, Jai-Won Chung**, "Prediction Model of Indoor Radon Concentration using Indoor and Outdoor Temperature, Humidity and Indoor Radon Concentration", Journal of KIIT. Vol. 18, No. 12, pp. 31-37, Dec. 31, 2020.
16 Young Jae Choi, Eun Ji Choi, Hye Un Cho, Jin Woo Moon, "Development of an Indoor Particulate Matter (PM2.5) Prediction Model for Improving School Indoor Air Quality Environment", Korea Institute of Ecological Architecture and Environment, 2021.2, 35-40 (6 pages)
17 Francesco Salamone1*, Lorenzo Belussi1, Cristian Curro2, Ludovico Danza1, Matteo Ghellere1, Giulia Guazzi1, Bruno Lenzi2, Valentino Megale2 and Italo Meroni1, "Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users' Feedback, IoT and Machine Learning: A Case Study††", MDPI Journals Sensors Volume 18 Issue 5, 12 April 2018
18 JiyoungSeo, AnseopChoi, MinkiSung, "Recommendation of indoor luminous environment for occupants using big data analysis based on machine learning", Building and Environment, Volume 198, July 2021, 107835   DOI