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Since the second world war specialised heat sensors have recorded thermal infrared (TIR) energy for the purpose of acquiring surveillance information. Today TIR is routinely used for determining residential waste heat footprints. For example, during the winter of 2000 and 2007 the London Borough of Haringey (UK) conducted aerial TIR surveys to provide householders with evidence of energy (in)efficiency. In 2001 the city of Aberdeen in Scotland did the same, as did Exeter in 2009. Although an important start, these surveys produced simple temperature class maps, with limited capability for in-depth visual, statistical or location-specific analysis.

Wasted Heat
Energy consumption behaviour changes dramatically only when individuals receive meaningful, dedicated feedback. Home Energy Assessment Technologies (HEAT) aims at providing Calgary waste heat data that is timely, in-depth, easy to use and location-specific for a wide range of private and governmental users. A full city airborne TIR survey (24km x 35km) will take place in early winter 2010, when the Thermal Airborne Broadband Imager (TABI-1800) becomes commercially operated. The current pilot project was developed using TABI-320 data over a smaller portion of Calgary.

TIR Sensor
The TABI-1800, developed and manufactured by ITRES (1), a specialist in airborne hyperspectral remote-sensing sensor and software systems, has a swath width of 1,800 pixels, and ability to collect up to 175km2 per hour at 1m Ground Sample Distance (GSD). This is three to five times larger and faster than most other TIR sensors. The TABI-1800 calculates radiometrically calibrated and geo-referenced data when in the air and uses diffraction-limited optics to ensure spatial independence of each pixel to avoid smearing between pixels. The cooled detectors are four times faster than the non-cooled ones used in most TIR sensors, thus allowing for the rapid detection of much weaker thermal signals. Image quality can be further improved by acquisition at 50cm GSD or smaller. Since the TABI-1800 is operational from February 2010, we in this study used TABI-320 data acquired in 2004 by ITRES to develop and test the Web 2.0-enabled HEAT protocols. The TABI-320 is a pushbroom scanner having 320 pixels in line and capturing the 8μm-12μm portion of the electromagnetic spectrum. The georeferenced mosaic we used consists of 600 x 2,000 pixels with 1m GSD and 0.1°C radiometric resolution, and covers 450 dwellings in the Brentwood community of Calgary in Alberta, Canada (see Figure 1). These houses were built between 1961 and 1965, when environmental impact and green-house-gas (GHG) footprint were unfamiliar notions.

System Architecture
The architecture is based on Open Geospatial Consortium (OGC) standards, and its four components are shown in Figure 2. A combination of PostgreSQL and PostGIS provides the geospatial database. Open-source geospatial libraries such as GDAL/OGR are used for raster and vector file handling, and PROJ.4 for coordinate system conversion in the image processing pipeline and by the geospatial database itself. Python is used for rapid program development, and the results are projected onto Google Maps. Image processing is conducted using ENVI and IDL, both software products from ITT Visual Information Solutions. The extraction of houses is done by a combination of in-house and commercial Geographic Object-based Image Analysis (GEOBIA) software (see feature by Addink in GIM International January 2010) using TABI data and City of Calgary cadastral datasets that include outlines of houses. The system generates three maps (community, residential and home) enabling multi-scale analysis ranging from individual houses to neighbourhoods, communities and cities, and offering opportunities to promote waste-heat competitions at city, inter-city, national and international levels. Two related energy models are also generated: Fuel Table Model and Annual Home Energy Use Mode.

Focused Repairs
On entering the site the user sees a Community HEAT Map overlaid on Google Maps, representing a continuous waste-heat surface defined within the civic boundaries of each community. This surface is created through an interpolation of the average roof top temperatures of each house, which are differentiated according to ten classes, from cold (blue) to hot (red). This scale allows evaluation of multiple communities at a glance. The top thousand-plus hottest houses in an area can also be identified. The percentage of houses per class for each community are dynamically graphed by mouse movements, enabling planners to evaluate and compare communities and formulate more informed advice on, for example, sponsoring energy-saving retrofit incentives for communities with high heat loss. Clicking on a specific community produces a more detailed Residential HEAT Map consisting of outlines of houses to which one of the above ten classes are assigned (see Figure 3); this enables detailed comparison of houses within a neighbourhood. By clicking on a house, the Home HEAT Map appears, showing data captured by the TIR sensor (Figure 4). The three hottest locations (hot spots) of individual houses are automatically defined by yellow circles when the user selects this option, paving the way for savings by focused repair. For example, in Figure 4 the yellow circles appear above four skylights, suggesting to the owners that they might check and repair these weaker parts of the roof. Post-renovation they will be able to verify the effectiveness of their efforts once free TIR data becomes available at some future date.

Energy Models
The Graphical User Interface (GUI) allows the user to inform the system about roof material, so that it can automatically determine the material's emissivity: a measure of a material's ability to radiate absorbed energy. Two objects of the same temperature will not show the same temperature in a TIR image if their emissivity differs. Translating emissivity as recorded by the TIR sensor into temperatures allows accurate and house-specific energy modelling: a unique feature of HEAT. Moving the mouse makes temperatures pop up in place of TIR sensor data. The GUI also enables retrieval of house area from city cadastral data, along with minimum, maximum and average home temperature. It is also possible to determine the Fuel Table Model: an energy model used to estimate GHG generation and cost per day of heating to maximum defined roof temperature using several types of fuel. In the completed HEAT system, fuel types and costs will be geographically defined. Movements of the mouse will dynamically highlight fuel sources (Figure 4). Double clicking will prompt a pop-up of the corresponding Annual Home Energy Use Model for the defined fuel type (Figure 5). This second energy model estimates total monetary and GHG costs and savings per year for each house, based on heating to the maximum and minimum defined temperatures.

Concluding Remarks
Regular TIR capture of the same area, annually for example, enables adjusting the effectiveness of renovations. You are invited to login to www.wasteheat.ca as beta, with password beta to evaluate the system.

Acknowledgements
Thanks are due to Gang Chen.

Further Reading

Darby, S., The Effectiveness of Feedback on Energy Consumption, 2006. Report published by Environmental Change Institute, University of Oxford, page 21. http://www.eci.ox.ac.uk/research/energy/downloads/smart-metering-report.pdf

Allinson, D., Evaluation of Aerial Themography to Discriminate Loft Insulation in Residential Housing, 2007. Unpublished PhD thesis, University of Nottingham, page 207.