실내 이산화탄소 농도 예측을 위한 기계학습 모델 검증

초록

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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