Abstracts Engineering

Predicting Carbon Dioxide Levels and Occupancy with Machine Learning and Environmental Data

by Giorgi; Khederchah, Christian; Li, Henrik Datunaishvili

Buildings consume the majority of the world’s energy usage through heating, ventilation and cooling. These elements are not regulated in an efficient and effective manner. Lights and heating are often left in action in empty spaces leading to waste. This project’s goal and purpose is to mitigate this wastefulness by implementing self powered environment sensors that can predict carbon dioxide levels and occupancy. These values can then be used to regulate spaces accordingly. The approach chosen to find a solution to this problem was to use machine learning. Machine learning was used to generate a prediction model. Different methods and models were used such as Gaussian Process Regression and Tree algorithm. The most effective model for this particular case turned out to be Gaussian Process Regression. The model was built by using accumulate, a model was made to calculate carbon dioxide values through humidity, temperature and pressure where an accuracy above 90% was achieved. The model to calculate occupancy levels had significantly lower accuracy. The reason that the carbon dioxide model was a success and the occupancy model was not, is due to the small size of the data set used while training the model. Carbon dioxide values had a bigger variance between data points, while the occupancy dataset contained mostly ones and zeros. This concludes to a longer training period to achieve high accuracy and precision for the occupancy model. The model for carbon dioxide converges with fewer data points as the result of the data having higher variance.