Sensor-driven analysis of retrofit options for low energy buildings**

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Energy assessment and providing retrofit options of existing buildings is currently costly and time consuming. This project aims to use the internet of things and big data to provide a cheaper way to find optimal retrofit options for residential buildings. A web of cheap sensors based on internet of things technology will be tested by monitoring environmental conditions in a 1870s era residential building. The data obtained from the sensors is stored in a database that will be analysed by machine learning algorithms trained using the available sensor data. The goal is that, when sufficient data is obtained, predictions can be made about optimal retrofit options for the monitored building. To validate this newly developed pathway traditional physics-based models will also be used. These provide a well-understood analysis method for existing buildings, but take significant time and expertise to develop. If these models are not properly calibrated, the error in energy consumption predictions may be in the order of 100%. The machine learning methods proposed require large amounts of sensor data but less time and expertise to develop. Furthermore, they provide accurate predictions of building performance based on available historical data. When these methods are fully developed, simple sensor data from residential buildings can be used to provide tailored retrofit options as a fast and inexpensive alternative to conventional physics-based modelling methods. This will provide quick retrofit solutions for the existing building stock. Improving building efficiency will help Canada meet its emissions goals and prevent further catastrophic climate change impacts.