Smart Connected Buildings is our study researching the feasibility of a building performance measurement tool which is currently lacking in the building industry, a tool which can make better use of the fact that many more buildings are being designed, refurbished and modelled using BIM.  Our team comprises BIM Academy, National Energy Foundation, Northumbria University and Your Homes Newcastle.


A BIM approach generates far more data than ever before, but there is a lack of means which make good use of the available data, such as giving insight into how a building is performing against how it was designed.

Since our last blog, our research and extensive industry interviews have identified a need to understand building performance to help reduce what social housing tenants spend on energy.  The project aim is to develop a platform which will link a range of live sensor data to models to provide actionable advice to tenants, landlords and industry to highlight the cause of performance gaps and reduce them.  Extensive testing of various sensor platforms has also taken place with the first deployment now being carried out.



During the modelling of new or existing buildings, data such as the thermal and other performance properties of fabric and insulation can be captured.  This data is combined with predicted weather conditions, the number of tenants and solar heat gain to produce an estimated fuel requirement for the building’s heating, hot water and lighting.  This is known as a SAP calculation -commonly used to determine how well a building will perform as part of the Building Regulation approval process.

An energy performance gap is the difference between the estimated energy usage from SAP calculations and the actual energy usage.  The cause of energy performance gaps can be a combination of an under performing building, poor heating control and inaccurate calculation assumptions.  Our platform aims to disrupt how performance gaps are tackled by addressing these causes.



This data will be combined with building information to provide context.  For example, by analysing how hard the boiler is working to bring a building up to heat and how quickly heat is lost, we aim to be able to highlight issues relating to the performance of the building fabric.  This will be fed back to the landlord so improvements can be made, and back into the industry as lessons learnt.



The sensors will also provide data to assist tenants in making efficient use of the building.  This will be achieved by applying machine learning techniques to analyse big data sets and provide meaningful advice to help tenants improve heating control, reduce energy bills and improve well being.

Comparisons will be made against assumptions used in SAP calculations with actual values, including weather conditions, occupancy data and building use, with discrepancies being fed back into the industry.

We aim to bridge a gap in the lack of feedback around the accuracy of the data.  This will have a real ability to drive change.

Over the coming months, we plan to have sensors deployed in 11 social landlord flats in the Newcastle upon Tyne region in the UK, these will be ready for when tenants move in early in 2018 so data collection and analysis can start.  We also intend to have a web application developed to assist in easy visualisation and interpretation of the data.  Check back in a couple of months for the next installment of our blog on this exciting initiative.