Regional Rooftop Solar Photovoltaic Potential Literature Review

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Contents

[edit] Search Keywords and Engines

Google Scholar
"roof area" "land use" data
determine "roof area" "large scale"
"roof area" "land use" regional scale
photovoltaic potential
photovoltaic potential urban area
population and roof area
population density and "roof area"
"Feature Analyst" roof
"Feature Analyst" solar

Queen's Library
"roof area"
photovoltaic potential
"Feature Analyst"


[edit] Part I: Determining Roof Area Using GIS and Image Recognition

Akbari, H., Shea Rose, L., & Taha, H., Analyzing the land cover of an urban environment using high-resolution orthophotos. Landscape and Urban Planning 63 1 (2003), pp. 1-14.

Tag: Image recognition, spectral-based techniques, 0.3m resolution, Sacramento, Monte Carlo, extrapolation

This paper describes a method used to analyze urban fabric for the Urban Heat Island Pilot Project (UHIPP). The goal is to classify urban area into several types, including roofs. To do this, the researchers obtained custom 0.3m resolution orthoimages of Sacramento using aircraft, GPS and digital imaging tools. This cost approximately $150/km^2. To classify the images, the researchers tried two image interpreting techniques: RGB banding and ERDAS/Imagine software. RGB banding did not work because of the variability of colour. The ERDAS/Imagine software can automatically outline pavements, roofs, and green areas. The researchers found that this was not adequate because it was unable to differentiate between driveways and parking lots, etc.

The researchers decided to process the images in a semi-automated process whereby pixels were selected using a random number generator, then were inspected and classified visually. They employed the Monte Carlo statistical technique, in which the statistical error decreases with the number of samples taken. This should be investigated further. Finally, the researchers extrapolated the orthophoto analysis to the greater Sacramento area by using available land-use land-cover (LULC) data which has a 200m resolution. They did this by grouping the photos into their respective LULC categories, then averaging over the entire land use areas.



Aramaki T, Sugimoto R, Hanaki K, & Matsuo T., Evaluation of appropriate system for reclaimed wastewater reuse in each area of Tokyo using GIS-based water balance model, Water Science & Technology 43 5 (2001), pp. 301–308.

Tag: Tokyo, source of data, water not PV

This article is referenced by Kraines et. al. with respect to how to construct the database for the DOME land-use applications from the GIS data. This was originally done in Tokyo for this paper, which spatially evaluates different wastewater reuse systems. They use raster GIS data in the form of floor area, rooftop area, and rainfall data. Unfortunately, this paper reveals that the Bureau of Urban Planning in Tokyo possesses this data, and thus they did not develop an innovative way to extract this data.



Castro, M. et al., Grid-connected PV buildings: analysis of future scenarios with an example of Southern Spain, Solar Energy, 79, pp.86-95, (2005).

Tag: Not really useful but referenced in Izquierdo.

This paper also focuses on the solar photovoltaic potential in Spain, but in a future context. The authors have developed a methodology to experiment with future scenarios to determine overall progress in grid-connected building-integrated photovoltaics (BIPV), called the “ScenariosPV” software. The model encorporates the global projection of markets for solar cells, the associated price reductions and emissions savings. Thus, it considers the economic and social issues surrounding BIPV. However, as with most solar potential analyses, it leaves roof area as a variable.



Gadsden, S., Rylatt, M., Lomas, K., & Robinson, D., Predicting the urban solar fraction: a methodology for energy advisers and planners based on GIS, Energy and Buildings 35 1 (2003), pp. 37-48.

Tag: SEP

Like “GIS-based decision support for solar energy planning in urban environments” by Rylatt, this paper describes the solar energy planning (SEP) system for energy advisers and policy makers. It is a method used to predict domestic energy consumption and to reduce this using solar applications.

While SEP may not be directly applicable to the RER project, the related work section describes three other related projects which should be investigated: LT-Urban, which uses DEMs and image processing; EEP, which has already been described in the above summary; and BREHOMES, which is a physically based model. BREHOMES calculates energy use using dwelling type, age, tenure, etc. This paper also mentions a potentially useful tool – the GIS footprint tool.



Grosso, M., Urban form and renewable energy potential, Renewable Energy 15 1-4 (1999), pp. 331-336.

Tag: PRECis overview

This paper outlines the objectives, work content and expected outcomes of the PRECis initative. It also describes the people and institutions responsible for the content. It also describes the basics of the relationship between built form and climate/microclimate relating to solar and wind considerations.



Guindon, B., Ying Zhang, & Dillabaugh, C., Landsat urban mapping based on a combined spectral-spatial methodology, Remote Sensing of Environment 92 2 (2004), pp. 218-232.

Tag: Canada, Ottawa, object-based, image recognition, urban, relationship between population density and building density

This paper describes methodology for monitoring urban growth in Canada using Landsat images (satellite imaging). An object-based approach was taken using Definiens e-Cognition software. The outcome of the analysis is to classify land into 5 classes: forest, water, herbaceous, residential and commercial/industrial. This is particularly important in Canada due to the recent explosion of low-density residential area (ie. suburbs) that is difficult to classify.

Once having obtained classifications, the researchers used six test sites sized 7.5 x 7.5 km in Ottawa to determine the accuracy of their results. They determined building density through manual image counts and population density from census data. They then compared residential classification components obtained with these data. Poor correlation was generally found, at best, the relationship was very complex. This suggests that Landsat classification methods are not directly applicable to the RER project; however, the methods used to test the image processing results do have relevance.

Quotation: “The similarity of the two plots of Fig. 9 suggests a high correlation between building and population density. While the Landsat sensor detects areas as urban because of the spectral reflectance component of impervious surfaces such as building rooftops, Fig. 9 indicates that indirect inference of population density is possible. We are currently validating this result with similar data for other major Canadian cities and will report on the findings later.”



Izquierdo, S., Rodrigues, M., & Fueyo, N., A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations Solar Energy, 82, pp. 929-939, (2008).

Tag: Spain, GIS, population density, coefficients, per capita values

This paper presents methodology for determining roof area available for solar photovoltaic applications on a large geographic scale (the country of Spain). The method involves stratifying geographic units based on population density and building density. They consider only the urban classified areas of the country. Then, a statistically representative sample of roof area is taken for each category using Geographic Information Science (GIS) techniques and a specific plug-in for Google Earth. Researchers were also able to quantify error. Finally, various reduction coefficients were applied to the roof area to account for shading, void spaces and alternative uses. The final estimate of available roof area for solar photovoltaic applications in Spain is 14.0 +/- 4.5 m2/capita. This paper also shows a definite decrease in RA/cap with increase in population density. Coefficients applied include pb and Cv to get from urban designated area on a 200mx200m resolution to an estimate of roof area. This is where our analysis begins, because we have obtained raw data for roof area. To roof area, coefficients Cs and Cf area applied for shadowing and facility (other uses of roof) respectively.

Spain Cs*Cf = 0.34
Average Cs*Cf for low density (<2400cap/km2) = 0.38
Spain RA/cap = 14m2/cap
Average RA/cap for low density (<2400 cap/km2) = 64m2/cap



Jardim, C. D. S., Rüther, R., Salamoni, I. T., Viana, T. D. S., Rebechi, S. H., & Knob, P. J., The strategic siting and the roofing area requirements of building-integrated photovoltaic solar energy generators in urban areas in Brazil, Energy and Buildings 40 3 (2008), pp. 365-370.

Tag: Brazil, PV efficiency estimation, ability to meet demand

This paper explores the benefit of grid-connected BIPV in creating negative distributed loads and “shaving the peak” of energy demand in cities in Brazil. This works particularly well where peaks are caused by air conditioning demand, indicating that there is strong sunlight at the time. The researchers have estimated total available roof area in regions surrounding feeders in the region (Table 2), but they do not indicate how the numbers were obtained or how they relate to the area/population. There is a table showing different efficiencies for different panels.



Jones, P.J., Vaughan, N.D., Cooke, P., Sutcliffe, A., An energy and environmental prediction model for cities. In Brandon, P.S., Lombardi, P.L. and Bentivegna, V (Eds.), Evaluation of the Built Environment for Sustainability, E & FN Spon (2009), pp.53-66.

Tag: EEP, GIS, demand rather than area

This article describes the EEP model which is used to predict energy use and emissions for different scenarios of urban built form. While it does use GIS-related built form analyses, they are focused around predicting flows of people and energy and do not relate to roof area.



Kraines, S. B., & Wallace, D. R., Urban sustainability technology evaluation in a distributed object-based modeling environment. Computers, Environment and Urban Systems 27 2, pp. 143-161 (2003).

Tag: DOME, GIS, Tokyo

This paper describes an internet-based infrastructure called DOME (distributed object-based modeling environment) for simulating integrated sustainable technologies in a large system, ie. an urban region. This infrastructure can serve to integrate Geographic Information Systems (GIS) with other environmental analysis tools such as life-cycle analysis, input-output analysis, and technology process modeling.

It is proposed that this infrastructure can be used for a wide variety of decision-making. Most importantly, however, it outlines an example of using DOME to determine the feasibility of using rooftop PV modules for electric power generation in Tokyo (Section 5). Using information from the 1991 Tokyo GIS database, the researchers simulated PV deployment on all available roof space as well as partial implementation scenarios. See also Kraines et al, Aramaki et al.



Kraines, S. B., Wallace, D. R., Iwafune, Y., Yoshida, Y., Aramaki, T., Kato, K., et al., An Integrated Computational Infrastructure for a Virtual Tokyo: Concepts and Examples, Journal of Industrial Ecology 5 1 (2001), pp. 35-54.

Tags: DOME overall description, Tokyo

This paper is related to the paper by Kraines et. al. which describes the DOME integrated software for coordinating several computational processes in order to generate a thorough analysis of a project to reduce emissions. This paper explains the DOME software, and most importantly, demonstrates how it can be used with the example of evaluating large-scale deployment of solar photovoltaics in Tokyo.

The analysis includes finding roof area with a GIS-based Land-Use Aggregator. It draws on a database which is constructed using total planar area of each of 30 land use types (15 of which are building types) in Tokyo. The Aggregator is implemented as a DOME plug-in with a Java-based graphic user interface (GUI). Some interesting and potentially useful assumptions were made in the process.

Other relevant sections are the Rooftop Irradiation Profile Model and the PV Generation Model.



Ratti, C. & Richens, P. (2009). Urban Texture Analysis with Image Processing Techniques. Proceedings of the CAADFutures '99 Conference, Atlanta.

Tag: Image recognition for urban texture, NIH Image, use DEM

Partly funded by PRECis, this conference paper describes an image processing method which was developed involving the use of NIH Image (Mac software, the Windows equivalent is Image/J) and the Image Processing Toolbox in Matlab. This method is used for urban analysis.

The paper describes various operations that can be done using digital elevation models (DEMs), which are a form of raster data, of urban form. Important techniques discussed include - LUT (look-up table) modification, which can enable thresholding and density slicing
- Applying filters, which can remove noise from an image, smooth or blur an image, or even detect edges of buildings
These researchers were able to find the built area using Image without the use of Matlab. They also developed a shadow casting algorithm and sky view factors (amount of time exposed to the sun). More high level applications were possible with the use of Matlab.



Richens, P. (1997). Image Processing for Urban Scale Environmental Modelling. Proceedings of the International Conference Building Simulation ’97, Prague, pp.163-171.

Tag: Image recognition, NIH Image, use DEM

This conference proceeding provides greater detail regarding the image processing techniques outlined by Ratti and Richens in Urban Texture Analysis with Image Processing Techniques (above) for obtaining urban texture analyses using NIH Image. These techniques are conducted on digital elevation model (DEM) images and include LUT modification and filtering. This paper also explains the origin of DEM images, the necessary input data: they started as hand-drawn figure-ground maps which were scanned and then loaded into NIH Image. Each image then had to be calibrated to fix the pixel dimensions. Unless Ontario already has DEM imaging, it will not be feasible to create it for the RER project.



Rylatt, M., Gadsden, S., & Lomas, K., GIS-based decision support for solar energy planning in urban environments, Computers,Environment and Urban Systems 25 6, pp. 579-603 (2001).

Tag: SEP, EEP, PRECis, GIS, Europe

This paper discusses a solar energy planning system which has been developed for use by planners and energy advisers. The system is scalable from individual property analyses up to entire cities. Importantly, the model is linked to a customized geographical information system (GIS). The paper is specifically focused on solar thermal generation; however it is acknowledged that the method could be shifted to both solar photovoltaics and passive solar applications.

The paper uses the Solar Energy Planning (SEP) model. It also describes other related projects, including Energy and Environment Prediction (EEP) and Potential for Renewable Energy in Cities (PRECis). These models are generally set up to determine energy demand and whether the proposed technology (solar thermal) can meet the demand. However, the methods may still be applicable to the needs of the RER project. To briefly summarize,

SEP
- fully uses digital urban maps to visualize at different levels and to derive information from GIS
- conducts analysis at the level of the individual dwelling unit but permits coarser models
- uses building age and type to classify buildings; discriminates based on income
- gives feedback on level of confidence
- see Gadsden

EEP
- statistical clustering method; divided at the level of postal code areas
- estimates the Standard Assessment Procedure ratings shared among members of a cluster
- uses 4 variables related to built form while all others are approximated based on global statistics
- see Jones et al.

PRECis
- most coarse-grained approach to broad urban areas
- uses novel image processing techniques using digital elevation models (DEM) and Lighting and Thermal (LT) data
- does not relate to georeferenced objects in GIS
- was conducted for only non-domestic buildings
- see Ratti & Richens, Grosso,

It is felt that EEP and PRECis, not SEP, should be further investigated for the RER project.



Taubenbock, H., Roth, A. and Dech, S., Linking structural urban characteristics derived from high resolution satellite data to population distribution. In Coors, V., Rumor, M., Fendel, E.M. and Zlatanova, S. (Eds), Urban and Regional Data Management, pp. 35-45, Taylor & Francis Group, London, (2008).

Tag: Reverse relationship of population density and roof space, remote sensing, image recognition, object-based approach

These researchers used remote sensing (satellite) images to estimate localized populations in rapidly changing areas of developing countries, where municipal data is inaccurate, outdated or nonexistent. They do this by generating homogenous spatial units (ie. soil, grass, apartment buildings) within the larger heterogeneous municipalities. This land use characterization is done through an OBJECT-BASED approach. This shows that there is indeed a relationship between population density, population and roof area.

Interestingly, the authors also report detection rates of 84.2% for houses (similar to ours), 78.1% for land use classifications and 94.2% for building heights.



[edit] Part II: Past Related Use of Feature Analyst

Ioannidis, C., Psaltis, C., & Potsiou, C. (2009). Towards a strategy for control of suburban informal buildings through automatic change detection. Computers, Environment and Urban Systems, 33, (1), 64-74.


Kunapo, J., Sim, P.T., & Chandra, S. (2006). Towards automation of impervious surface mapping using high resolution orthophoto. Monash University ePress: Applied GIS, 1, (1). Retrieved August 19, 2009 from http://publications.epress.monash.edu/doi/abs/10.2104/ag050003.




Miller, J.E. (2005). Impervious Surface Cover: Effects on Stream Salamander Abundance and A New Method of Classification Using Feature Analyst. Master’s thesis, North Carolina State University. Retrieved August 19, 2009, from http://www.lib.ncsu.edu/theses/available/etd-11022005-110912/.




O’Brien, M.A. (2003). Feature extraction with the VLS Feature Analyst system. Presented at the ASPRS 2003 Annual Conference, Achorage, Alaska.




Psaltis, C., & Ioannidis, C. (2008). Simple Method for Cost-Effective Informal Building Monitoring. Surveying and Land Information Science, 68, 65-79.




Sumaryono, Strunz, G., Ludwig, R., Post, J., & Zosseder, K. (2008). Measuring urban vulnerability to tsunami hazards using integrative remote sensing and GIS approaches. Presented at the International Conference on Tsunami Warning (ICTW), Bali, Indonesia.




Yuan, F. (2008). Land-cover change and environmental impact analysis in the Greater Mankato area of Minnesota using remote sensing and GIS - modelling. International Journal of Remote Sensing, 29, (4), 1169.




[edit] Part III: Relationship between Population and Roof Area

Ghisi, E. Potential for potable water savings by using rainwater in the residential sector of Brazil. Building and Environment, 41 11 (2006), pp. 1544-1550.

Tags: Per capita values, Brazil, water catchment

This paper looks at roof area per capita in five regions of Brazil to determine available rainwater for a potable drinking source. Using weighted average system to account for apartments and individual dwellings, the authors find that total roof area ranges from 17.6-21.2 m2/capita. This seems accurate compared to our results, considering that Brazil has a much higher population and population density compared to Canada.



Izquierdo, S., Rodrigues, M., & Fueyo, N., A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations Solar Energy, 82, pp. 929-939, (2008).

Tag: Spain, GIS, population density, coefficients, per capita values

This paper presents methodology for determining roof area available for solar photovoltaic applications on a large geographic scale (the country of Spain). The method involves stratifying geographic units based on population density and building density. They consider only the urban classified areas of the country. Then, a statistically representative sample of roof area is taken for each category using Geographic Information Science (GIS) techniques and a specific plug-in for Google Earth. Researchers were also able to quantify error. Finally, various reduction coefficients were applied to the roof area to account for shading, void spaces and alternative uses. The final estimate of available roof area for solar photovoltaic applications in Spain is 14.0 +/- 4.5 m2/capita. This paper also shows a definite decrease in RA/cap with increase in population density. Coefficients applied include pb and Cv to get from urban designated area on a 200mx200m resolution to an estimate of roof area. This is where our analysis begins, because we have obtained raw data for roof area. To roof area, coefficients Cs and Cf area applied for shadowing and facility (other uses of roof) respectively.
Spain Cs*Cf = 0.34
Average Cs*Cf for low density (<2400cap/km2) = 0.38
Spain RA/cap = 14m2/cap
Average RA/cap for low density (<2400 cap/km2) = 64m2/cap



Kumar, M.D. Roof Water Harvesting for Domestic Water Security: Who Gains and Who Loses, Water International, 29 1(2004), p. 39-51.

Tag: Per capita values, India

This paper investigates potential for rainwater harvesting to meet water needs in India. In the urban areas, they find a per capita roof area of >6m2 for the upper classes, and <2m2 for the lower classes (slum dwellers). Note that this accounts for their dwelling only, not all classes.



Lehmann, H. and Peter, S. (2003). Assessment of roof & façade potentials for solar use in Europe. Institute for sustainable solutions and innovations (ISUSI), Aachen, Germany.

Tag: Plots of relationship of population density to roof area, Western Europe, per capita values, coefficients

Most important paper to my literature review. Authors plot Roof and Façade area per capita versus population density in several German cities. They find a negative relationship which follows a cubic function for facades and a quadratic function for rooftops. The trend in fact looks similar to ours but with several more data points. They find that non-residential roof area per capita ranges from 9m2/cap in low density regions to 4m2/cap in high density regions, while residential roof area ranges from 7-4.5 respectively. These numbers are derived from “specific research into solar areas,” indicating, that non useful roof has already been eliminated (ie. no need to reduce further). Note that high density refers to densities around 3500cap/km2, much higher than our absolute highest of 1200.

These researchers apply a factor of 0.9 to account for non-useable and shadowed space.

Results of this study show that Germany has 985km2, or 15km2/cap, while Spain has 528km2, or 13.5 m2/cap. The authors believe that these trends can be extrapolated to all of Western Europe and present a table with values for the EU15.



Naroll, R., Floor Area and Settlement Population. American Antiquity 27 4 (1962), pp. 587-589.

Tag: Relationship of floor area to population density, old Europe

Found that the population of a prehistoric settlement can be estimated by known floor areas, following a log-log fit.



Pillai, I. R., & Banerjee, R. Methodology for estimation of potential for solar water heating in a target area, Solar Energy, 81 2 (2007), pp. 162-172. \

Tag: Coefficients, per capita values, India

This article explores solar water heating potential using the TRYNSYS program to estimate both technical and economic potential. The method is illustrated on an example of a 2km2 area of India which includes houses, hospitals, hotels and nursing homes. The authors take 30% of total roof area to be available for solar. Over an area of 2km2, with a population density of 5000 persons/km2, they find that 12000m2 of roofspace are available. Scaling this back up from the 70% reduction, this works out to 4m2 of roofspace per capita in total.



Pratt, C.J. Use of Permeable, Reservoir Pavement Constructinos for Stormwater Treatment and Storage for Re-Use, Water Science Technology, 39 5 (1999), p. 145-151.

Tag: Per capita values, UK

This paper looks at roof area with respect to population density to determine wasted stormwater runoff. For low density areas (8 houses/hectare), they find approximately 30.7m2/capita. For high density areas (35 houses/hectare), they find approximately 10.6m2/capita.



Taubenbock, H., Roth, A. and Dech, S., Linking structural urban characteristics derived from high resolution satellite data to population distribution. In Coors, V., Rumor, M., Fendel, E.M. and Zlatanova, S. (Eds), Urban and Regional Data Management, pp. 35-45, Taylor & Francis Group, London, (2008).

Tag: Reverse relationship of population density and roof space, remote sensing, image recognition, object-based approach, developing countries

These researchers used remote sensing (satellite) images to estimate localized populations in rapidly changing areas of developing countries, where municipal data is inaccurate, outdated or nonexistent. They do this by generating homogenous spatial units (ie. soil, grass, apartment buildings) within the larger heterogeneous municipalities. This land use characterization is done through an OBJECT-BASED approach. This shows that there is indeed a relationship between population density, population and roof area.

Interestingly, the authors also report detection rates of 84.2% for houses (similar to ours), 78.1% for land use classifications and 94.2% for building heights.

[edit] Part IV: Percentage of Roof Area Available for Solar PV

Ghosh, S. and Vale, R., Domestic energy sustainability of different urban residential patterns: a New Zealand approach. International Journal of Sustainable Development 9 1 (2006), pp. 16-37.

Tag: Coefficients, New Zealand

This paper looks broadly at the “sustainability” of five area units (groups of several blocks) in New Zealand. One of the factors explored is roof area available for solar applications. Using the CITYgreen method based in GIS, the authors determined roof area using aerial photos of the areas. They then took “solar efficient roof area” to be the percentage of roof oriented within 45 degrees on either side of North (New Zealand is in the southern hemisphere). Values of efficient solar roof area as a percentage of the total roof area range from 21.8-37.1%, with an average of 26.8% (values seem to be inconsistent across two reported tables; the more conservative values were taken).



Izquierdo, S., Rodrigues, M., & Fueyo, N., A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations Solar Energy, 82, pp. 929-939, (2008).

Tag: Spain, GIS, population density, coefficients, per capita values

This paper presents methodology for determining roof area available for solar photovoltaic applications on a large geographic scale (the country of Spain). The method involves stratifying geographic units based on population density and building density. They consider only the urban classified areas of the country. Then, a statistically representative sample of roof area is taken for each category using Geographic Information Science (GIS) techniques and a specific plug-in for Google Earth. Researchers were also able to quantify error. Finally, various reduction coefficients were applied to the roof area to account for shading, void spaces and alternative uses. The final estimate of available roof area for solar photovoltaic applications in Spain is 14.0 +/- 4.5 m2/capita. This paper also shows a definite decrease in RA/cap with increase in population density. Coefficients applied include pb and Cv to get from urban designated area on a 200mx200m resolution to an estimate of roof area. This is where our analysis begins, because we have obtained raw data for roof area. To roof area, coefficients Cs and Cf area applied for shadowing and facility (other uses of roof) respectively.
Spain Cs*Cf = 0.34
Average Cs*Cf for low density (<2400cap/km2) = 0.38
Spain RA/cap = 14m2/cap
Average RA/cap for low density (<2400 cap/km2) = 64m2/cap



Lehmann, H. and Peter, S. (2003). Assessment of roof & façade potentials for solar use in Europe. Institute for sustainable solutions and innovations (ISUSI), Aachen, Germany.

Tag: Plots of relationship of population density to roof area, Western Europe, per capita values, coefficients

Most important paper to my literature review. Authors plot Roof and Façade area per capita versus population density in several German cities. They find a negative relationship which follows a cubic function for facades and a quadratic function for rooftops. The trend in fact looks similar to ours but with several more data points. They find that non-residential roof area per capita ranges from 9m2/cap in low density regions to 4m2/cap in high density regions, while residential roof area ranges from 7-4.5 respectively. These numbers are derived from “specific research into solar areas,” indicating, I think, that non useful roof has already been eliminated. Note that high density refers to densities around 3500cap/km2, much higher than our absolute highest of 1200.

These researchers apply a factor of 0.9 to account for non-useable and shadowed space.

Results of this study show that Germany has 985km2, or 15km2/cap, while Spain has 528km2, or 13.5 m2/cap. The authors believe that these trends can be extrapolated to all of Western Europe and present a table with values for the EU15.



Pillai, I. R., & Banerjee, R. Methodology for estimation of potential for solar water heating in a target area, Solar Energy, 81 2 (2007), pp. 162-172.

Tag: Coefficients, per capita values, India

This article explores solar water heating potential using the TRYNSYS program to estimate both technical and economic potential. The method is illustrated on an example of a 2km2 area of India which includes houses, hospitals, hotels and nursing homes. The authors take 30% of total roof area to be available for solar. Over an area of 2km2, with a population density of 5000 persons/km2, they find that 12000m2 of roofspace are available. Scaling this back up from the 70% reduction, this works out to 4m2 of roofspace per capita in total.



Scartezzini, J-L., Montavon, M. and Compagnon, R. (2002). Computer Evaluation of the Solar Energy Potential in an Urban Environment. Proceedings of EuroSun2002, Bologna, Italy.

Montavon, M., Scartezzini, J-L., Compagnon, R. (2004). Solar Energy Utilization Potential of Three Different Swiss Urban Sites. Proceedings of 13 Status Seminar Energie un Umwelforschung im Bauwesen, Zurich, Switzerland.

Tags: Coefficients, Watts per m2, Europe

Using a computer simulation which considers a minimum required irradiance and illuminance, building fractions available, polar orientation diagrams and weighted sky view factors, the authors determine fraction of roofspace available for solar applications. The program was developed with the PRECis initiative. The authors show that a significant amount of solar potential exists in the urban fabric, reporting a fraction of 49.4% available for PV applications. They took only areas which have a minimum threshold of 1000kWh/m2 of irradiance.

The second paper uses the same technique on three particular buildings in Switzerland and finds use coefficients to be 94.9, 73.1 and 49.4%. Thus, the authors used the most conservative coefficient.