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ИНВЕНТАРИЗАЦИЯ ТЕПЛООТДАЧИ В ГОРОДСКИХ РАЙОНАХ С ЦЕЛЬЮ УМЕНЬШЕНИЯ ПОТЕРЬ ТЕПЛА В СЕТЯХ ЦЕНТРАЛЬНОГО ТЕПЛОСНАБЖЕНИЯ
Маттиас Венк
TRIGIS GeoServices GmbH, Ehrenbergstrasse 20, 10245 Берлин, Германия, тел.: +49 (0)30 501506-0, тел: +49(0)163-8501608, e-mail: [email protected]
Теплоизлучение от зданий, не отвечающее требованиям теплоизоляции, приводит к потере энергии и ненужным затратам. В системе центрального отопления передача тепла чаще всего вызывает не выявляемые потери. Знание источников таких потерь может в значительной степени повысить КПД энергии, снизить общую мировой потребляемую энергию, уменьшить газы, вызывающие парниковые эффект, и одновременно снизить затраты энергии. В статье рассматривается особый подход к решению всех этих проблем на основе инфракрасной термографии, применяемой в компании TRIGIS GeoServices GmbH.
Ключевые слова: дистанционное зондирование, кадастр потери тепла,
инвентаризация теплоотдачи, КПД энергии, обнаружение по тепловому излучению.
HEAT EMISSION INVENTORIES IN URBAN AREAS - REDUCTION OF TRANSMISSION LOSSES OF DISTRICT HEATING PIPES
Matthias Wenk
TRIGIS GeoServices GmbH, Ehrenbergstrasse 20, 10245 Berlin, Germany, tel: +49 (0)30 501506-0, email: [email protected]
Heat emission from buildings as a result of inadequate thermal insulation leads to energy loss and thus unnecessary costs. In long-distance heating lines thermal transfer also often causes undetected transmission losses. The knowledge of the sources of such loss would help increase energy efficiency significantly, decrease global energy consumption, reduce greenhouse gases and cut energy cost at the same time. A technological approach based on infrared thermography is presented in the following by TRIGIS GeoServices GmbH.
Key words: remote sensing, heat loss cadastre, Heat emission inventories, energy efficiency, thermal detection.
PHYSICAL FUNDAMENTALS OF INFRARED THERMOGRAPHY
Heat or thermal radiation is a subset (part of the spectrum) of the electromagnetic radiation and invisible for the human eye. It was discovered in 1800 by William Herschel when he shone sunlight through a dispersion prism and detected radiation beyond the visible spectrum of light with a thermometer. Nonetheless infrared radiation does follow the same physical laws as visible light.
Figure 1: electromagnetic spectrum and the working areas of different sensors; Source: „Einfuhrung in die Fernerkundung“, Albertz, J., TU Berlin, S.11
The following figure shows the electromagnetic spectrum with the wavelengths of the particular radiations as wells as the usable work areas of sensors. A distinction of the infrared is made between near infrared NIR (0,8 ^m ... 3^m), medium infrared MIR (3^m ... 7^m) and far, long-wave infrared LIR (7^m ... 20^m)1. This classification is especially relevant when choosing sensors and considering emission ratio of bodies.
All bodies emit infrared radiation at temperatures > 0 Kelvin. This radiation is measured by the IR-Thermography. In this context temperature can be regarded as a function of the radiation intensity in the infrared radiation spectrum. The warmer a body radiates, the shorter the wavelength respectively the higher the frequency of the electromagnetic waves. For infrared thermography on buildings and longdistance heating lines mainly the LIR and MIR bands are used.
HEAT LOSS ON AND IN BUILDINGS
(1) Transmission
(2) LOftung
@ WSmieerzeugung und -vertellung
Figure 2: Sources of building heat loss;
Source: Dr. Ralf Plag (http://www.u-wert.net)
Exact calculations on the overall heat loss of the buildings are currently not available. There are estimations for Germany, whereby up to 35% of primary energy use is attributed to the heat loss of buildings. Accordingly the potential savings add up to ca. 4.800 PJ (35% of 13.645 PJ, equaling Germany primary energy consumption in 2012).2
The heat loss on and in buildings is categorized in three heat loss sources according to its cause. Fig. 2 illustrates these sources.
1 Vgl. Schuster, N./Kolobrodov, V., Infrarotthermographie, 2004, S. 13
2 Vgl. Pehnt, M., Energieeffizienz, 2010, Institut fur Energie- und Umweltforschung, Heidelberg, S.22
The types of loss contribute differently to the total heat loss. If trying to decrease the losses, transmission heat loss and losses during production and distribution are the main focus as this part can be permanently decreases through technical means.
Lokalisation der Transmissionsverluste
■ AufJenwand
Dach
■ Keller
■ Fenstsr
■ Warmebriicken
Figure 3: Transmission losses of building parts;
Source: Own illustration based on data of Ingenieurburo Junge (www.ing-buro-junge.de)
Heat loss through ventilation is very much depending on behavior and habits of the occupants und thus can be less or hardly influenced.
Transmission losses are directly correlated to the condition of the building’s outer shell and insulation (roof, facades, windows).
As the adjacent figure shows, different parts of the building contribute differently to the total heat loss.
AIRBORNE DETECTION OF HEAT LOSSES (THERMAL FLIGHT)
In order to detect heat loss of buildings and long-distance heating lines, 4 different detection procedures are suitable.
- Terrestrial (static) detection through an infrared camera
- Mobile detection from vehicles
- Mobile detection via drones (UAV)
- Mobile detection from planes
Satellite based IR-Detection is also possible, but due to the insufficient thermal and geometrical resolution, it is not suitable for this field of application.
The airborne detection (thermal flight) has proven to be the most economic method of completely measuring entire settlement areas. The goal of such an approach consist mainly in creating a heat map of building roof surfaces as wells as of the street and road network. The latter is used to detect underground long-distance heating lines and their heat emission. The following figure shows exemplarily a complete thermal image of an airborne detection of the city of Hoogeveen, NL (cs. 58.000 inhabitants, area ca. 130 km2).
Figure 4: Colored thermal image from an aerial photograph, city of Hoogeveen; Source: Aerodata France, Nov. 2011
The thermal image is initially just a map of the heat condition at an appointed date. In order to generate interpretable data from it, the image is firstly orthorectified, i.e. rectified while preserving positional information. Subsequently the image is georeferenced, i.e. transformed into a higher-level, geodetic position and height framework. Both of these steps are required to view the information congruent with other geodata and to enable visualization with different layers. In digital cartography or geographical information systems, the layer principle is used to combine different data and map themes and to manage different views.
The following example shows an image selection (daylight photograph in summer) and infrared image of the same area (nightly photograph in winter). The surface temperatures are display from blue (cold) to red (warm). Clearly visible is, e.g. the course of a long-distance heating line with relatively strong heat emission (see white arrow). This suggests a leakage in the line or insufficient line insulation.
Figure 5: image detail left: digital orthophotograph, right: IR thermal photograph of the same area;
Source: Aerodata France
The following conditions must be met in order for thermal flight to provide reliable measurements:
Condition Explanation
Night flight respectively no daylight The sunlight during a day flight would influence and distort the measurement due to reflections, heterogeneous heat emission and heterogeneous absorptions of different surface materials.
Cold weather (ambient temperature < 5°C) The heating in the building should be in use. A high as possible temperature difference between inside and outside makes for more reliable measurements.
Dry weather (no fog, no recent precipitation in the last 36 hours) Damp surfaces would affect or distort the measurement through reflection and evaporation.
No snow Snow cover would distort the measurement (insulation effect of snow).
Still air or very low wind velocity (max. level 2 on the Beaufort Scale) Strong wind would distort the measurement through convection.
The given condition limit the possible flight days for measurement considerably. Furthermore the heating habits of the occupants must be taken into consideration when planning flights. With a temporal delay heating activities lead to an increased emission. Combined with the flight and measurement conditions in table 1, this results in optimal time slots at night (20:00 to 23:00) and in the early morning (06:00 to 09:00). In such a time slot a plane can measure an area of ca. 100 km2.
ANALYSIS AND ASSESSMENT OF THERMAL DATA
The path from a thermal image to a thematic map as a preliminary stage of heat emission cadastre (HEC) will be illustrated in the following by eight major steps. Each picture shows an identical area of the historic city center of Magdeburg.
Step 1 - raw data
The result of the thermal measurement is primarily available as raw grayscale data with increments of 0.03 - 0.1 Kelvin (thermal resolution, depending on utilized sensor). This raw data is read directly from the sensor.
Furthermore in this step the orientation data is readout and produced from the inertial measurement unit (IMU) and the GPS module.
This already yields a spatial orientation of the thermal image with a precision of 1.5 pixels. The actual raw data precision depends on the chosen ground resolution, which is determined by flight altitude and the geometric resolution of the employed sensor.
Step 2 - thermal analysis
Each pixel is allocated an absolute temperature value. After calibration respectively alignment of single images (image series of the individual flight runs), the thermal image is colored to the effect that the differences become clearly visible.
When doing this, the coldest measured temperature is usually assigned the color blue and the highest value the color red. All increments in-between can be automatically colored according to a freely customizable color scale. In order to test the temperature measurements, a ground measurement can be conducted parallel to the flight at locations visible from the air. With these results the output thermal sensors can be checked and the thermal imaging better calibrated.
Step 3 - digital orthophoto (DOP)
Step 4 - building data
In order to assign the thermal analysis to individual buildings, the building contour must be available as objects, thereby the contour line must have been created as a closed polygon. For the creation of the
To visualize the result of the thermal imaging a topographical basis can be used, e.g. a digital orthophoto plan (DOP). DOP are used a basic geodata and are managed and distributed by the appropriate state office for geoinformation. Beyond that, municipalities or energy providers often hold and sell higher resolution flight images and resulting DOP from individual projects. These can also be used as a topographical basis.
building contours, the following alternatives are available:
- Digitalization based on DOP
- Adoption of existing building footprints from the cadastre
- Photogrammetric deviation (image
flights)
Buildings can optionally be assigned with address data, which are added as an attribute to the building object. This makes sense when a GIS with address bases search is used for the administration of the cadastre. Another option is the deviation of 3D building models. This is conducted when a 3D visualization is desired or terrestrial thermal images of facades are combined with the thermal images of the roofs. The 3D building models can be created from digital cadastral data or an intersection of digital surface model with a digital terrain model.
Step 5 - intersection and aggregation The edited thermal map from thermal analysis is overlaid with the vector data of the building contours. This requires both data sets to be transformed into the same geodetic reference system, resulting in a so-called orthothermoplan. For each pixel within a building footprint, an arithmetic average of temperature values is calculated. Additionally anomalies and interferences like e.g. chimneys are filter out, whose temperature values are not supposed to incorporated into the analysis. Thus the data basis for the subsequent classification of the buildings is created. The data attributed to the object building as follows (aggregation on building level):
T MIN C: Minimum temperature in °C T MAX C: Maximum temperature in °C
T MED C: Median temperature in °C T_MOY_C: Mean temperature
T_STDEV_C: Standard deviation temperature CL_NOM: Class name of heat loss
CL_ID: Class ID of heat loss (from 0 to 5, from Not perceptible to Excessive)
CL_0_M2 : Building surface covered by class 0 CL_0_PT : Building percentage covered by class 0
Step 6 - deviation of heat loss classes The building object gets an additional attribute heat loss class (CL_ID). In the example six loss classes were defined. The number and basic statistical model can be chosen as seen appropriate. Even when the emission of different building parts highly varies, the building is only assigned with one loss class.
To achieve that, the determined an emission
range is calculated from the temperature averages of the building. The range is then divided into any number of intervals. Each interval represents a heat loss class. Each heat loss class is assigned a color
code for easier visualization. As best practice the use of a color scale from blue (zero emission) to red (highest emission) has been established.
Step 7 - overlaying topographical data
The result of step 6 is clipped with a topographical base map. In the example this is a DOP of step 3. Alternatively a cadastral map or an existing city map (vector map) is also possible as a basis for the map. In the end a thematic map has been created that can afterwards be visualized in different form and utilized for different applications.
Step 8 - overlaying line network data
A modified application of the heat emission cadastre (HEC) is the detection of problematic places in long-distance heating lines. For this the thermal image is overlaid with line network data of the energy provider (see turquoise lines). In the area marked in white a leakage or damage spot can be clearly seen. The raw data of this application is the same as for the previous steps. The temperature range for this analysis is chosen in a way that the temperature differences between normal and problematic line sections become visible.
The temperature range differs from the one applied to buildings because the heat emission of subterranean line lie in a different temperature spectrum.
VISUALIZATION OF A HEAT EMISSION CADASTRE
2D map visualization
The result of the previous steps is cartographically edited and displayed as a thematic map in discretionary scale.
As the client usually has a geographical information system at its disposal, this visualization can also be provided in digital form. Thus data administration and constant disposability are guaranteed.
Alternatively formatting as GeoPDF is also possible. As PDF readers can be used on nearly any PC, user without any GIS knowledge can digitally administrate these maps.
Figure 6: Visualization of heat loss classes in a thematic map
3D visualization
One descriptive way to present the data is the visualization as a 3D model. In many cities 3D building models are already available. Figure 7 shows thermal images from a thermal flight (roofs) combined with thermal images from a terrestrial recording.
Figure 7: 3D visualization as a combination of colored heat images from a thermal flight and terrestrial thermal imaging.
Example building complex Daumesnil, Paris Source: Lefebvre, M., Topografie: Google Earth
Here the thermal images are put as textures on the building models. This visualization is not feasible for whole settlements but for spatially limited areas (e.g. the contemplation of single housing complexes or redevelopment zones) as time and effort needed for the creation is significantly higher.
Another, simplified variant of the 3D visualization is shown in figure 8. Here, simple building models (LOD 2) were wholly colored with a single loss class. The information content of this display is the same as in 2D, but the visual effect is superior. Therefore this form is suited for presentation in front of an
audience which is not accustomed to map visualizations and interpretation.
Figure 8: 3D visualization of heat loss classes as colored LOD 2 building models; Example area Magdeburg historic city center;
Source: GeoFly
Presentation within a web information portal
The possibilities of a presentation within a web information portal are naturally more numerous as with a printed form of visualization. Through the interactive operation, information can be displayed more individually. The information portal allows:
- Zooming to any scale
- Turning on and off of generalized display, detailed display (colored thermal image), topography, line network, etc.
- Simple navigation through the map via integrated address search
- Retrieval of statics and freely selectable spatial queries.
Figure 9: HEC web information portal; Source: Lefebvre, M.
USAGE OF THE HEAT EMISSION CADASTRE FOR MANAGEMENT OF ENERGY EFFICIENCY PROJECTS
The Heat Emission Cadastre can be worthwhile for managing energy efficiency project. For this, the following information can be deducted:
The buildings with the most heat emission will be preferentially reconstructed. The cadastre shows which buildings these are and implies a prioritization.
The localization of buildings in question can be made through the map and if applicable through the address search.
The prioritization of redevelopment projects begins with the loss classes six and five and is later continued with 4 and 3.
Line leakages and insulation damage of long-distance heating lines are displayed on the map as singular points and can sequentially tended to. In this manner providers of long-distance heating can systematically eliminate their transmission losses.
REFERENCES
Albertz, J., 2009, Einfuhrung in die Fernerkundung, Darmstadt, Wissenschaftliche Buchgesellschaft.
InfraTec GmbH, 2004, Einfuhrung in Theorie und Praxis der Infrarot-Thermographie, Dresden.
Institut fur Thermografie in der Bautechnik, 2013, Institut fur Thermografie [Online],
http://www.institutfuerthermografie.de/dienstleistungen-
energieb eratung/bauthermografie/anwendung-der-bauthermografie/.
Lefebvre, M., Hagman, F., 2012, Aerial Thermography and More, October 2012, GIM international, Lemmer, : Geomares Publishing.
Pehnt, M., 2010, Energieeffizienz, Heidelberg, Springer-Verlag.
Plag, R., 2010, Die unabhangige Seite fur energie-effizientes Bauen, U-Wert.net, http://www.u-wert.net/wohin-geht-die-waerme.
Schuster, N., Kolobrodov, V., 2004, Infrarotthermographie, Kiew, Suhl, WILEY-VCH Verlag.
Other Sources, geodata and project informationen of TRIGIS GeoServices GmbH, GeoFly GmbH, Aerodata France.
© Matthias Wenk, 2014
