Case Studies

BIM Academy - Smart Connected Buildings


From BIM Journal. Click Here to Read Issue 5

From BIM Journal here.

By way of update to an ongoing project that considers data collection and analysis in terms of social housing, here again is Graham Kelly (BIM Academy) to outline their latest initiatives when it comes to assessing predicted vs actual performance.


Interest in Building Information Modelling (BIM) has seen a considerable rise in consistent data sharing practices. Design and construction processes have been significantly streamlined however, owners and occupants still do not know if their building is performing as ordered. Therefore, an opportunity remains to improve the utilisation of information once a building is in use.

Buildings generate huge volumes of data when in use. This data can be collected in several ways - information relating to the environment can be collected by sensors monitoring temperature, humidity, daylight and energy consumption. Occupancy information, such as the number of tenants, age groups and satisfaction can also be collected by various means such as PROBE occupant surveys. This data is commonly stored across multiple disconnected systems in numerous formats. Because of this, the ability to make conclusions is limited, certainly around energy performance gaps.

A performance gap is a disparity between the predicted energy use and the actual energy use of the building when in operation. Performance gaps occur for many reasons, which can see design details either being unbuildable, loosely defined, or missing. As such, improved feedback from the in-use phases can certainly help to close such gaps so that lessons can be applied earlier in the design and construction phases.

Performance gaps have great impact in sectors such as social housing. Poorly designed buildings with vulnerable tenants can severely impact fuel poverty and occupant wellbeing. In 2015 89.7% of fuel poor occupants were in properties deemed as not being energy efficient (GOV, 2017), directly increasing the likelihood of fuel poverty.


To test the feasibility of developing a platform which can give insight into the causes of performance gaps, over the past six months the team had refined use cases and conducted extensive testing on a range of sensors. In addition to this, the team started deploying sensors within a number of properties in the social housing sector. The data collected will be combined with building information obtained from a building model, to provide context.

Extensive industry interviews have identified a need to understand building performance to help reduce what social housing tenants spend on energy. Therefore the main use case to prove the concept will be to analysing how hard the boiler is working to bring a building up to heat and how quickly that heat is lost.


The project team aims to highlight issues relating to the performance of the building fabric, which will be fed back to the landlord so improvements can be made, and in addition to the industry as lessons learned. The team also aim to provide advice to tenants, which will assist them in making efficient use of the building. This will be achieved by applying powerful machine learning techniques to analyse the data collected and provide meaningful advice to help tenants improve heating control, reduce energy bills and improve overall well being.

Over the next six months, the team will develop a scalable web application which will assist in the visualisation and interpretation of the data. The development will be conducted in close collaboration with Your Homes Newcastle with the aim developing the concept further with a scalable and cost-effective way to gain significant and valuable insight into both new and existing buildings in use.

It is hoped that ongoing initiatives like this will continue to provide real world feedback, such that performance gaps of many kinds can continually narrow.

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