Housing Stock Energy Management
As a housing stock manager, you have energy targets to achieve for the housing stock you manage and you have to make retrofit decisions with a limited budget and timescale. Applied InfraRed Thermography (IRT) will help you take some of the stress out of this decision-making.
In a previous blog we explored how an IRT camera can see energy, but quantifying it for energy loss, that is the Holy Grail for thermographers and something that can be done. In fact IRT surveys received a patent for just that back in May 2013.
find out how an IRT Surveys stock survey can help below.
Step 1: exploring pixels captured
Today’s Thermal cameras are digital and radiometric. That means every pixel records a value when you take an image. Every pixel is a temperature. A typical camera then has about 76,800 thermometers. Because everything is digital those temperatures are stored as metadata behind the visual bit of the image. So what? Well, this data can be accessed and interpreted. If you record how far away you were from the object you photographed then, with basic trigonometry, we can tell you the size of a pixel. Once you know the size, it’s easy to add them up and say 'You have 2.15 metres of red pixels on your wall'. That’s not quantified, I hear you scream. Hold ‘yer horses, I say. Step two involves capturing other data beyond the cameras abilities.
Step 2: adding building data
To truly quantify the images you need to know what the building is made of. We ask questions such as:
What is the U-value of the wall supposed to be?
How do you heat the property?
How much does that heat cost?
Where is the property
How efficient is the boiler?
What’s the temperature inside and outside?
Step 3: feeding templates
Over the past 6 years we have developed our own elastic templates. Stretching or shrinking them to fit clients buildings. We need to augment known variables from energy software such as SBEM or SAP, into the algorithms to arrive at a meaningful quantification. Weather data from the last 25 years is built into the models.
Boiler efficiency ratings from SEDBUK are added. Age and geographically relevant building regulations are taking into account. Eg. A home built in 1883 will most likely be solid, have high ceilings, large doors and a chimney. It’s likely more drafty than a more modern home.
The tricky bit for us as software developers was creating dynamic software that meant we didn’t have to physically enter a home to ascertain the internal temperature.
Our software re-calibrates itself to allow for differing internal temperatures constantly. When we survey 1000’s of homes we can’t ask all of them to be the same temperature inside, rather the software has the ability to moves its expectations. Missing insulation makes a difference of about 1-2 degrees to the surface of a wall. The difference between 18 and 19 is 1. The difference between 21 and 22 is also 1 – so we focus on the differential.
Housing stock energy management: the right data matters
Quantifying the number of pixels at a known size and known temperature becomes a lot more simple thereafter. Is it accurate? Yes. Is it perfect? No.
Input the wrong heat source – say gas instead of electric - and the results are wild.
Input the right data, and the results are quite startling.
In a recent survey of 60,000 homes for 83 housing associations we were never more than 3 SAP points away from full on investigative results. In the vast majority of cases we were bang on.
Being able to quantify energy for an entire housing stock is a rapid process that will help you
- make informed decisions,
- spend your budget wisely and
- help you apply for funding and grants for your investments.
Is housing stock energy management giving you headaches? Is your data ready to be fully exploited to ease your decision-making process? Please do contact us to discuss your energy management challenges by clicking on the button below.