Abstract
Air pollution is increasing profusely in Indian cities as well as throughout the world, and it poses a major threat to climate as well as the health of all living things. Air pollution is the reason behind degraded indoor air quality (IAQ) in urban buildings. Carbon dioxide (CO2) is the main contributor to indoor pollution as humans themselves are one of the generating sources of this pollutant. The testing and monitoring of CO2 consume cost and time and require smart sensors. Thus, to solve these limitations, machine learning (ML) has been used to predict the concentration of CO2 inside an office room. This study is based on the data collected through real-time measurements of indoor CO2, number of occupants, area per person, outdoor temperature, outer wind speed, relative humidity, and air quality index used as input parameters. In this study, ten algorithms, namely, artificial neural network (ANN), support vector machine (SVM), decision tree (DT), Gaussian process regression (GPR), linear regression (LR), ensemble learning (EL), optimized GPR, optimized EL, optimized DT, and optimized SVM, were used to predict the concentration of CO2. It has been found that the optimized GPR model performs better than other selected models in terms of prediction accuracy. The result of this study indicated that the optimized GPR model can predict the concentration of CO2 with the highest prediction accuracy having R , RMSE, MAE, NS, and a20-index values of 0.98874, 4.20068 ppm, 3.35098 ppm, 0.9817, and 1, respectively. This study can be utilized by the designers, researchers, healthcare professionals, and smart city developers to analyse the indoor air quality for designing air ventilation systems and monitoring CO2 level inside the buildings.
Highlights
Human health, performance, satisfaction, and productivity inside built environments are affected primarily by indoor environment quality (IEQ)
The objective of this study is to address the research gaps identified from the selective literature review using the artificial neural network (ANN) and other machine learning (ML) methods to predict CO2 concentration inside the office building
Each subset would be chosen in order for the validation process, the remaining 4 subsets being utilized for training inside the training stage
Summary
Performance, satisfaction, and productivity inside built environments are affected primarily by indoor environment quality (IEQ). Skön et al [24] modelled CO2 concentration in apartment buildings using artificial neural networks They considered temperature and relative humidity as input parameters. Mohammadshirazi et al [26] tested four different ML methods, rolling average, random forest, gradient boosting, and long short-term memory for the prediction of indoor concentration levels of carbon dioxide, total volatile organic compound, formaldehyde, PM10, PM2.5, PM1, ozone, and nitrogen dioxide. The proposed work uses artificial neural networks (ANN) and other machine learning methods to forecast CO2 level inside an office building. The objective of this study is to address the research gaps identified from the selective literature review using the ANN and other ML methods to predict CO2 concentration inside the office building. The performance comparison of different machine learning models used for predicting the CO2 level inside the building has been presented
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