Measuring building thermal performance - coheating tests

Improving the thermal performance of buildings is an area where some of the largest energy and carbon savings can be made. Building energy use and carbon emissions can be reduced by as much as 60-80% through better insulation, draught proofing (improved thermal performance) and heating efficiency and controls. But how do you go about working out the performance of your house and what measures are best to undertake to reach this level of performance improvement? Improving building fabric is expensive, how do you work out which measures will be most cost-effective? and how do you make sure that your house actually achieves the target performance? how do you measure and check it?

These are the questions I’m currently grappling with for my own house and the openenergymonitor lab, these are also the questions we’re trying to develop improved processes for answering with our work with Carbon Coop.

Here’s a graphic from the Centre for Alternative Technology’s Zero Carbon Report illustrating the kind of performance improvements that are possible:


The most common way to investigate domestic building performance is by using simple building energy models such as SAP:
http://openenergymonitor.blogspot.co.uk/2013/06/building-energy-modelling-carbon-coop.html

The accuracy of a model is always dependent on its input data and assumptions, work that has been done on comparing modelled energy consumption as calculated by SAP vs actual energy consumption show that there is often a discrepancy and in some cases the discrepancy is so large (even a 100% or more) as to undermine decisions made based on model outputs.

https://www.bsria.co.uk/download/asset/agm-2011-co-heating-tests-sarah-birchall.pdf
To rely therefore on modelled performance only can be misleading.

It is possible however to measure the total building thermal performance by measuring how much energy it takes to heat a building up to a given temperature above the outside temperature. This procedure is known as a coheating test and was pioneered by the Center for the Built Environment at Leeds Met University. (There's also a good info sheet on coheating tests here by Peter Warm www.peterwarm.co.uk/?dl_id=6 )

The standard co-heating test involves heating building when unoccupied to an elevated internal temperature of 25C over a period of 1-3 weeks with electric heating and monitoring the electrical heat input, internal and external temperatures. Keeping the building unoccupied reduces unknown variables and so increases the accuracy of the measurement but it alongside the elevated temperature makes widespread testing of this kind difficult.

The question that several people have been asking and I’m aware of several groups working on this (@Housahedron and Richard Jack, lboro) is; can a method be developed for undertaking an ongoing co-heating equivalent test that can be undertaken while the building is being used, that could even measure thermal performance over time as measures are undertaken. The analogy is a car MPG meter but for your house.

Over the last few weeks I’ve been working on an approach that I think is showing promising results, it involves applying a dynamic model to realtime monitored heating input and external temperature data to model indoor temperature. The modelled internal temperature is then compared to the actual indoor temperature.


The model parameters that give a good match tell you the heat loss factor of your building and also it’s thermal mass. The heat loss factor is your MPG equivalent for household fabric thermal performance.

The model type is a multi stage resistor-capacitor model, using the resistor-capacitor analogy for thermal modelling is a standard approach used in many thermal models, there's an interesting page on it here: http://lpsa.swarthmore.edu/Systems/Thermal/SysThermalModel.html


This is all open source and the code so far is up on github here: https://github.com/emoncms/openbem,
There's also a forum thread here with a bit more information on the model and tests http://openenergymonitor.org/emon/node/2783
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