With heating, ventilation, and air conditioning (HVAC) systems eating up about 50 per cent of energy used in homes and buildings, cars are now being deployed in the United States with thermal-imaging rooftop rigs that create heat maps to detect building envelope inefficiencies.
The technology has been developed by Essess, a spin-out from MIT, to provide a more scalable and cost-effective approach to auditing buildings. On-site audits for residential and small commercial units can be difficult to implement on a large scale due to the cost and time required to manually conduct an audit on each unit, but each imaging rig can capture thermal images for tens of thousands of units in a single night.
The rig includes two long-wave infrared (LWIR) radiometric cameras and near-infrared cameras. In order to get a robust, vertical image of the scene, data streams from the LWIR are stitched together using proprietary algorithms. A LiDAR system – a remote sensing technology that measures distance by illuminating a target with a laser and analysing the reflected light – captures the images in 3D and separates building facades from the physical environment.
The vehicles are also armed with an on-board rig management system with a suite of driver and navigator tools to maintain and troubleshoot the imaging rig in real time, as well as optimise route planning.
“In our calculations, we assume the average temperature and pressure inside of the home and are able to measure the exact temperature and pressure in the outside environment. The technology can, very accurately, show blatant hot spots throughout the building envelope,” explained Navi Singh, head of solutions delivery at Essess.
The data captured using the thermal imaging system is then combined with utility data, weather data, property records and demographic information to deliver a highly personalised energy efficiency analysis for each building envelope. Combining multiple sources of information enables an analytical engine to identify the optimal structural improvements.
Thermal cameras have previously been very expensive – costing as much as $40,000 – with lower resolution than ordinary smartphone cameras. But a technology breakthrough by Field Intelligence Lab has led to low-cost cameras – about $1,000 – which produce high-resolution thermal images.
“We’ve made thermal imaging very automated on a very large scale,” says Essess co-founder Sanjay Sarma, who co-invented the technology.
The inherent scalability of the thermal imaging technology offers the potential for electric and gas utilities to scan their entire service territories in a matter of days or weeks to derive intelligence around their customers. That information has previously been far too costly to obtain.
By way of example, the map below displays over 17,000 buildings in Cambridge, Massachussetts which were scanned in a matter of hours.
The different colors represent a building’s energy loss via the envelope. Blue or purple buildings have more efficient building envelopes, whereas red buildings suffer from some form of structural inefficiency that results in substantial energy loss.
From an HVAC perspective, system efficiency is affected by the system itself, by household behavioural factors such as thermostat and window usage, and by the building envelope. Companies measuring HVAC efficiency today, by reading meters and using other data, have no building-envelope scans, so they can’t really determine if the envelope is the cause.
If the audit team sees high meter usage that corresponds to really high HVAC load, but see a really strong envelope captured by the thermal imaging camera, this will alert them to an abnormality that has to be addressed by an HVAC contractor.
There have been, and continue to be, significant challenges but efficiencies are continuing to improve.
Temperature differences and vibrations, among other things, mean that infrared cameras need daily calibration. Constant tweaks have also had to be made to the GPS system, while software development is an evolutionary process.
Furthermore, it was only with the system running that they realised that when there is a tree in front of the building, in the thermal image it was hard to figure out where the tree ends and the building begins. That is what led to the installation of the LiDAR system, to better differentiate building facades from the surrounding environment.
Singh also agreed that there are and will always be outliers in the data set.
“What if someone has gone on vacation and left their heat on a really low setting, for example? For the majority of the data scanned, the information is still very useful. For someone having a party, they may have a really high internal temperature than can often be very useful as it further highlights where heat is leaking from the building. Furthermore, in cases where one home is extremely out of sync with other homes in the neighborhood, the algorithm highlights that particular home and we will look at the data for that home in more detail in order figure out exactly why that home is an outlier,” he said.
Essess estimates that by making homes just two per cent more efficient, billions of dollars could be saved.