Predicting airborne pollutant concentrations and events in a commercial building using low-cost pollutant sensors and machine learning: A case study

Abstract

Prediction of indoor airborne pollutant concentrations can enable a smart indoor air quality control strategy that potentially reduces building energy use and improves occupant comfort. In service of this overarching goal, this work pursues four objectives: 1) Determine which low-cost airborne pollutant sensors are useful for prediction of indoor air quality variables of interest, investigating whether a few commercially available sensors held value for making such predictions. 2) Investigate which algorithms are most useful for making these predictions. 3) Develop an understanding of how far into the future we can conceivably predict indoor concentrations based on low-cost airborne pollutant signals. 4) Investigate methods for predicting elevated concentration events from historical data. Four different methods (Rolling Average, Random Forest, Gradient Boosting, and Long-Short Term Memory) for predicting eight indoor pollutant concentrations (carbon dioxide, nitrogen dioxide, ozone, PM 1, PM 2.5, PM 10, formaldehyde, total volatile organic compounds) are compared for their ability to predict future sensor signals in a single commercial building in California. Long-Short Term Memory was consistently the best method for predicting indoor pollutants, though the best combinations of input variables differed depending on pollutant of interest. To predict elevated concentration events, results show that indirect classification through a regression prediction that was then compared to a threshold performed marginally better than a direct classification prediction for all pollutants except PM1.