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"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"

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"roof area"
photovoltaic potential

Introduction

Focacci, A. (n.d.). Residential plants investment appraisal subsequent to the new supporting photovoltaic economic mechanism in Italy. Renewable and Sustainable Energy Reviews, In Press, Corrected Proof.

Italy has a “Feed-In Programme” but not a tariff.



IEA, 2006. World energy outlook 2006. International Energy Agency, OECD Publication Service, OECD, Paris. <www.iea.org> Accessed June 30, 2009.

Current situation of global energy use.



The Intergovernmental Panel on Climate Change (IPCC). Summary for Policymakers. Climate Change 2007: Synthesis Report. Valencia, 2007.

The updated facts on climate change.



Kuwano, Y. (1994). The PV Era is coming -- the way to GENESIS. Solar Energy Materials and Solar Cells, 34(1-4), 27-39. doi: doi: DOI: 10.1016/0927-0248(94)90021-3.

This article emphasizes solar as the “ideal form of energy to resolve global environmental problems.”



Ontario East Economic Development Commission (OEEDC). 2009. "Ontario East Economic Development need to record specific article titles.



Ontario Power Authority (OPA). 2009. “Feed-in Tariff Program: Home page.” Accessed July 2, 2009, from <http://www.powerauthority.on.ca/FIT/>.

Outlines the current status of the FIT.


Ontario Power Authority (OPA). 2009 (b). “Integrated Power System Plan: Backgrounder.” Accessed July 2, 2009 from <http://www.powerauthority.on.ca/Storage/49/4437_Backgrounder.pdf>

For referencing in the phasing out of coal by 2014.



Ontario Power Authority (OPA). 2009 (c). “Feed-in Tariff Program: Ontario Unveils North America's First Feed-In Tariff.” Accessed July 2, 2009, from <http://www.powerauthority.on.ca/fit/Page.asp?PageID=122&ContentID=10098>.

Outlines the current status of the FIT.



Pearce, J. M. (2002). Photovoltaics -- a path to sustainable futures. Futures, 34(7), 663-674.

This article outlines the feasibility of PV as a technology which can help to solve society’s energy problems in terms of economics, environmental impacts and social equity. Particularly useful is the Overview section which outlines other subsidy programs in Japan, Germany and California.


R.E.H. Sims, R.N. Schock, A. Adegbululgbe, J. Fenhann, I. Konstantinavicuite, W. Moomaw, H.B. Nimir, B. Schlamdinger, J. Torres-Martinez, C. Turner, Y. Uchiyama, S.J.V. Vuori, N. Wamukonya, X. Zhang, 2007: Energy supply. In Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

This document outlines all of the options for mitigation of climate change and mentions solar PV as a potentially effective renewable technology.



Sand�n, B. A. (2005). The economic and institutional rationale of PV subsidies. Solar Energy, 78(2), 137-146.

This article outlines the need for subsidies to render PV technologies competitive on the market, even with cap and trade policies in place. This will spur technological innovation and mitigate the “Catch-22” situation of high PV prices and low uptake. It also discusses the success of Japanese and German PV subsidies.



UN, 1992. UN Framework Convention on Climate Change. Climate Change Sectretariat, United Nations, Geneva.

Highlights the urgency of the global warming problem.



Weiss, I., Sprau, P., & Helm, P. (2003). The German PV solar power financing schemes reflected on the German PV market. In Photovoltaic Energy Conversion, 2003. Proceedings of 3rd World Conference on (Vol. 3, pp. 2592-2595 Vol.3). doi: 10.1109/WCPEC.2003.1305121.

This article can be referenced in order to compare future predictions about Ontario’s FIT to the recent success of German subsidies. This paper serves as a review of all other government incentive programs as of 2001, including Japan, the US, the Netherlands, Spain and Australia.


Yamaguchi, M. (2001). Present status and prospects of photovoltaic technologies in Japan. Renewable and Sustainable Energy Reviews, 5(2), 113-135.

This article mentions the success of Japanese government programs to promote residential solar technologies.


http://library.queensu.ca/webdoc/ MADGIC

http://library.queensu.ca/webdoc/maps/guides/SOLRIS_FAQ_APR2008.pdf SOLRIS info

http://library.queensu.ca/webdoc/maps/census-geog/2006/census-geog-2006.htm Census boundary files


http://www12.statcan.gc.ca/census-recensement/2006/dp-pd/prof/92-591/index.cfm?Lang=E Stats Can


http://publicdocs.mnr.gov.on.ca/View.asp?Document_ID=16150&Attachment_ID=33873
http://www.groupealta.com/en/projects/229/drape.aspx DRAPE and LIO overview


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.


Part II: Relationship between Population Density 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.


Part III: 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.


Potential Publication Journals

Energy and Buildings (4)

Impact factor = 1.590
Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing energy needs of a building and improving indoor environment quality.

It is published with the editorial support of the International Council for Research and Innovation in Building and Construction (CIB)

Topics covered include: • Energy demands and consumption in existing and future buildings • Thermal comfort and indoor air quality • Natural and mechanical ventilation • Air distribution in air conditioned buildings • Application of solar and other renewable energy sources in buildings • Energy balances in major building complexes (industrial, public and other buildings) • HVAC and refrigeration systems in residential, public and industrial buildings • Heat recovery systems in buildings • Buildings and district heating • Energy conservation in built environment • Energy efficient buildings • Building physics • Sustainable buildings and energy demands • Evaluation and control of indoor thermal and lighting systems • Intelligent buildings • Links between architectural design, mechanical and lighting systems • New materials in buildings and their impact on energy demands • External and internal design conditions for energy efficient buildings

Papers with results based on simulations are welcome but those with clear links to laboratory or field measurements are preferred. These links may include calibration, benchmarking, or comparisons of results.

Solar Energy (4)

Impact factor = 1.519
Solar Energy, the official journal of the International Solar Energy Society®, is devoted exclusively to the science and technology of solar energy applications. The Society was founded in 1954 and is now incorporated as a non-profit educational and scientific institution. With participation encompassing 100 countries, ISES® serves as a centre for information on research and development in solar energy utilisation. Through its publications and its sponsorship of technical conferences, the Society provides a world forum for the active consideration of solar energy. Solar Energy welcomes manuscripts presenting information not previously published in journals on any aspect of solar energy research, development, application, measurement or policy. The term "solar energy" in this context includes the indirect uses such as wind energy and biomass. Because of the international character of Solar Energy, articles that deal solely with the solar radiation or wind data base of a specific country are not normally considered suitable for Solar Energy. Submitted manuscripts may take the form of reports of original studies or reviews of significant prior work in a given area. All manuscripts are subject to reviews to assure accuracy, clarity, and long-term value. Manuscripts of general interest not being suitable for Solar Energy should be submitted to Refocus, which publishes magazine-style feature articles concerning all aspects of renewable energy. Please e-mail David Hopwood, Editor for further details (d.hopwood@elsevier.co.uk) and visit http://www.re-focus.net.


Computers, Environment and Urban Systems (2) - Top consideration

Impact factor = 1.025
Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics. Interdisciplinary perspectives are strongly encouraged. Application areas include environmental analysis, modeling and management, urban planning, economic development, emergency response and hazards, housing, land and resource management, infrastructure and facilities management, physical planning and urban design, transportation, business, and service planning. Examples of methodological approaches include geographic information systems, decision support systems, geocomputation, spatial statistical analysis, complex systems and artificial intelligence, visual analytics and geovisualization, ubiquitous computing, virtual rendering and simulation.

Contributions emphasizing the development and enhancement of computer-based technologies for the analysis and modeling, policy formulation, planning, and management of environmental and urban systems that enhance sustainable futures are especially sought. The journal also encourages research on the modalities through which information and other computer-based technologies mold environmental and urban systems.

Audience
Urban and regional planners and policy analysts, environmental planners, economic geographers, geospatial information scientists and technologists, regional scientists and policy makers, architectural designers.

Energy Policy (2) - Top consideration

Impact factor = 1.903
Energy Policy is established worldwide as the authoritative journal addressing those issues of energy supply, demand and utilization that confront decision makers, managers, consultants, politicians, planners and researchers. Major articles cover a comprehensive range of topics from national energy pricing to energy efficiency potential in the domestic sector; from the politics of US energy policy to the economic evaluation of nuclear power; from the environmental impacts of fossil fuel use to energy demand management in developing countries. The scope of Energy Policy embraces economics, planning, politics, pricing, forecasting, investment, conservation, substitution and environment. • Energy and greenhouse gas mitigation: the IPCC Report and beyond. • Valuing the benefits of renewables. • Financing the energy sector in developing countries.

Renewable Energy (2)

Impact factor = 1.663
The journal seeks to promote and disseminate knowledge of the various topics and technologies of renewable energy and is therefore aimed at assisting researchers, economists, manufacturers, world agencies and societies to keep abreast of new developments in their specialist fields and to unite in finding alternative energy solutions to current issues such as the greenhouse effect and the depletion of the ozone layer. The scope of the journal encompasses the following: Photovoltaic Technology Conversion, Solar Thermal Applications, Biomass Conversion, Wind Energy Technology, Materials Science Technology, Solar and Low Energy Architecture, Energy Conservation in Buildings, Climatology and Meteorology (Geothermal, Wave and Tide, Ocean Thermal Energies, Mini Hydro Power and Hydrogen Production Technology), Socio-economic and Energy Management.

Renewable Energy accepts original research papers or any other original contribution in the form of reviews and reports on new concepts. It promotes innovations, papers of a tutorial nature and a general exchange of news, views and new books on the above subjects.

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