Smart Connected Homes

The project tests the feasibility of pulling different datasets together in a central model that can be used to provide meaningful, actionable advice to owners, managers and occupiers of buildings.

The feasibility study focuses on the housing sector which is vulnerable to fuel poverty and has a need to be smarter with budgets driven by cuts, whilst improving upon tenant experiences, wellbeing and satisfaction. The project combines data from multiple sources such as context data from the building model (BIM), sensor data (IoT), maintenance data and occupant surveys.

The data collection points have been designed to capture data relating to four of the seven wellbeing areas which are air quality, lighting levels, comfort and mind – these are measurable through sensors and surveys with minimal intrusion to the tenants.

Machine learning techniques are being applied to infer knowledge about occupant living habits that can be used to improve recommendations given to tenants and building managers to optimise energy efficiency and wellbeing.

Project Partners: BIM Academy Enterprises, Northumbria University, Your Homes Newcastle, National Energy Foundation

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