| 논문명 |
거주자 행태 기반 공동주택 CO2 농도 예측 기계학습 알고리즘 기초 연구 / Basic Research on Machine Learning Algorithm for Predicting CO2 Concentration in an Apartment based on Resident Behavior |
| 저자명 |
홍수민(Hong, Su-Min) ; 조경주(Cho, Kyung-Joo) ; 강태욱(Kang, Tae-Wook) |
| 수록사항 |
한국건축친환경설비학회 논문집, Vol.17 No.6 (2023-12) |
| 주제어 |
기계환기장치; 재실자 행위; 머신러닝 ; Mechanical ventilation system; Residents’behaviors; Machine learning |
| 요약2 |
This paper focuses on developing and validating a CO2 concentration prediction model to optimally control and operate mechanical ventilation systems in residential buildings. Various datasets and models were utilized throughout the research process. Initially, CO2 concentration was predicted based on a basic dataset comprising the day of the week, time, temperature, and indoor CO2 levels. Subsequently, a more comprehensive dataset, incorporating ventilation system operation counts and natural ventilation presence, was employed for predictions.
Machine learning and deep learning models such as ANN, DNN, LSTM, KNN, and LGBM were trained using these datasets, and their predictive performance was compared across different areas of the residence, namely bedrooms, living rooms, and corridors. The findings demonstrate that even using the basic dataset alone, it is possible to predict CO2 levels in residential buildings with a high degree of confidence. These results are expected to contribute to the development of future algorithms for operating next-generation mechanical ventilation systems that aim to minimize energy consumption while maintaining a clean indoor environment.
This paper is anticipated to increase interest and understanding in environmentally conscious indoor environmental control systems and offer essential research insights for the advancement of building systems and environmental technology in the future. |
