센서와 머신 러닝을 통한 교실 책상 면 조도 예측

Prediction of Daylight Illuminance on Classroom Desk Surface Using Machine Learning Model and Illuminance Sensor

본 연구의 목적은 외부 조도 센서를 통해 교실의 책상 면 조도를 예측하는데 있다. 이를 위해 기존 태양추적센서와 다르게 구름 양 등을 고려 가능한 자연채광센서를 개발하고, 센서값을 통해 책상면 조도를 예측하는식을 제작하였다. 그러나 이론식을 통해 측정은 정확도가 70%이고, 블라인드를 고려하지 못하는 단점이 있었다. 이를 보완하기 위해 머신 러닝을 진행하였다. 실측을 통한 머신러닝 데이터셋 구성은 시공간상 제약이 있어 시뮬레이션을 제작하였다. 시뮬레이션 예상값과 실측의 평균 편차는 5% 이내였다. 교실의 향, 블라인드유무, 절기, 시간을 변화시키며 시뮬레이션한 센서와 책상면 조도 데이터는 총 192세트 1728개이다. 이를 머신러닝한 결과 시뮬레이션 데이터와 95.4%, 실측값과95.2%의 정확도를 보였다. 또한 교실에서 디밍이 가능한 시간을 찾아 에너지 절감효과를 계산한 결과 동지13.0%, 춘추분 29.6%, 하지 40.7%의 에너지 절감이 가능한 것으로 나타났다.

The purpose of this study is to predict the desk illumination of the classroom using the results obtained through sensors that can measure the natural lighting environment, and to save energy by dimming the artificial lighting of the classroom. In addition, a theoretical equation for predicting desk illumination through sensor values was produced. However, the desk illumination measured through the theoretical formula had a disadvantage of 70% accuracy and could not consider the blinds in the classroom. Machine learning was conducted to solve these shortcomings. Since constructing a machine learning dataset through actual measurement has time and space limitations, a simulation was produced to construct a dataset. As a result of comparing the produced simulation and the measured value, there was an error within 5%, which was used as machine learning data. There are a total of 192 sets and 1,728 data of natural light sensor values and classroom desk illumination values measured by changing direction, blind use, season, and time through simulation. As a result of learning this through the XGBoost regression model, the simulation value showed an accuracy of 95.4%, and the actual measurement value showed an accuracy of 95.2%. In addition, as a result of predicting desk illumination by natural lighting in each season and calculating the lighting energy saving effect, it was found to have an energy saving effect of 13.0% in winter, 29.6% in spring and fall, and 40.7% in summer.