Inspired by the fact that 50% of building energy usage in the United States is consumed by HVAC systems, the objective of this research project is to use a mobile application and a portable sensor (anemometer and tempreature sensor) built in one device to collect supply tempreature and volumetric air flow from this sensor and send it to the machine learning model to predict zone tempreature. The outcome of the model is compared with the outcome of the model that is fed with the same data attributes from the building HVAC database and then compared using advanced statistical analysis to check for uncallibration.

The mobile application is built on iOS and we are using Testo and Govee for obtaining supply tempreature/volumetric air flow and room tempreature respectively.

Areas: Mobile development, Cloud Computing, Embedded Sensor Systems, Computer Networks, Machine Learning