Sensor Data Analysis in Smart Buildings

Data analysis and Machine Learning are destined to evolve the current technology infrastructure by solving technology and economy demands present mainly in developed cities like New York. This research proposes a machine learning (ML) based solution to alleviate one of the main issues that big buildings such as CUNY campuses have, that is the waste of energy resources. The analysis of data coming from the readings of different deployed sensors such as CO2, humidity and temperature can be used to estimate occupancy in a specific room and building in general. The outcome of this research established a relationship between the CO2, temperature and humidity values of the room and occupancy by applying ML methods such as p-analysis, naive, logistic regression models, feature selection and others. The result, together with the implementation of an automatic infrastructure for light and HVAC systems, can be used to save money and resources