- Email: [email protected]
MUSE: An Open Urban Management Decision Support System
Proceedings of the 14th IFAC Symposium on Information Control Problems in Manufacturing Bucharest, Romania, May 23-25, 2012
MUSE: An Open Urban Management Decision Support System M. Bonhomme*. H. Ait Haddou**. L. Adolphe* * (FROH1DWLRQDOH6XSpULHXUG¶$UFKLWHFWXUHGH7RXORXVH 83 rue Aristide Maillol, BP 10629, 31106 Toulouse cedex 1, France; e-mail: {marion.bonhomme, hassan.ait-haddou,luc.adolphe}@toulouse.archi.fr. ** Centre National de Recherches Météorologiques ± GMME/TURBAU, 42,Avenue Gaspard Coriolis ± 31 057 Toulouse Cedex 01 Abstract: This paper describes a decision support tool for urban planners. Numerous research studies show that energy consumed and produced in cities can be related to its morphology. Yet, the urban energy paradox is defined as follows: on the one hand, the densification of cities reduces transportation and buildings consumptions. On the other hand, this densification has a negative impact on urban microclimate and renewable energies potential. The goal of this work is to develop a decision support system for urban planners faced with urban energy paradox. Our research is based on a previous work developed by Luc Adolphe et al. in the SAGACités project. This research led to the development of the geographic information system (GIS) platform called MORPHOLOGIC, which, among other things, evaluates the energy consumption of city blocks. Our goal is to add new features in MORPHOLOGIC: one of them calculates solar potential (photovoltaic and thermal), using a simplified model of shadows. The new version of MORPHOLOGIC called MUSE, based on the open source GIS called OrbisGis developed by the IRSTV institute, will allow urban planners to evaluate the best urban form to reduce GHG emissions. Keywords: Decision Support Systems, Integrated Modelling System, Spatial Database Management System, energy consumption, renewable energy, urban form
1. INTRODUCTION
The complexities and difficulties of issues involved in urban management and climatic change makes very difficult taking decision by urban planners and force theme to new tools and methods. In fact, spatial decision problems in urban planning are the aggregation of different evaluation criteria that urban planners should be taken into account for evaluating different development policies. Therefore, multi-criteria decision analysis (MCDA) has become the most important tool in environmental decision-making for modelling the problem of satisfying multiple objectives (Janssen 1992, Lahdelma et al. 2000, Linkov et al. 2006, Regan et al. 2007, Yatsalo et al. 2007). Recall that the conflicting aspects between the objectives and preferences during the decision process, and the significant evaluation criteria to be considered reduce significantly the chance to reach a solution supported by all the urban planners in the decision process. It is then necessary to develop and compute new tools capable of processing data inputs of varying formats, numerical models and expert opinions in multi objective decision making scenario. Spatial and environmental Decision Support Systems (DSS) combined with Geographic Information System (GIS) are among the most promising approaches to confront such situations. Therefore, DSS and GIS are gaining importance and widespread acceptance as a tool for decision making in the infrastructure, water resources, environmental 978-3-902661-98-2/12/$20.00 © 2012 IFAC
management, spatial analysis and urban development planning. Different large scale models, mostly based in the applied mathematics (operational research and economics) such linear programming, optimization techniques, costbenefit analysis have been developed to deal with spatial decision problems in urban planning. The work presented in this paper is part of a national research program that assesses the impact of various urban forms (dense, sprawled or green city) on energy consumption, renewable energies production and climate change until 2100. This work received support from the French National Research Agency (Agence Nationale de la Recherche) with the reference ANR-09-VILL-0003. For the last decades, numerous researches have studied energy-efficient buildings. Many models have been developed to take into account isolation, inertia, solar gain and renewable energy production. However, those tools do not take into account issues related to urban scale, and cannot be used by urban planners questioning energy±efficiency in the city. This change in spatial and temporal scale to the scale of urban development requires a change of paradigm mixing urban form, energy use, renewable energy potentially produced by buildings and urban microclimate. Few researches deal with the physical and morphological complexity of energy- efficient cities.
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INCOM 2012, May 23-25, 2012 Bucharest, Romania
However, cities are responsible for the majority of energy consumption and greenhouse gas emissions on the planet. In 2008, residential and commercial buildings in France emitted 96.9 Mt CO2 (25.9% of emissions in France) and consumed 69.4 Mtoe (43% of energy consumption in France) (Ministère GHO¶(FRORJLHGX'pYHORSSHPHQW'XUDEOHGHV7UDQVSRUWVHW du Logement 2010). Among these consumptions, 48.1 Mtoe concerned heating and air conditioning alone (68% of buildings consumption) (ADEME 2008).Transport systems are major emitters of greenhouse gases, being responsible for 31.1% of GHG emissions and for 38.4% of energy consumption in France in 2008 (MinistèUH GH O¶(FRORJLHGX Développement Durable, des Transports et du Logement 2010). In a context where urban population is increasing (80% of the population in developed countries) the energy consumed in cities will consequently be intensified. As a result, urban planners require Decision Support Systems (DSS) to plan more sustainable cities.
(Adolphe et al. 2002)). After measurements and numerical model evaluations in urban areas, this research led to the development of a geographic information system (GIS) platform called MORPHOLOGIC. This tool allows, among other things, to evaluate the energy consumption from one single to several city blocks taking into account buildings, streets, squares and vegetated areas. It also calculates a set of urban morphological indicators such as compactness, density and contiguity. MORPHOLOGIC allows the intersection of geometric data (buildings footprint, buildings height, length of the streets), topological data (adjacency of buildings, facades, distance to a bus station etc.) and demographic data (population per block). The platform calculates a system of indicators which balance energy, environment and comfort to help out in the urban decision making.
The energy consumed and produced in the city is closely related to its morphology (Adolphe et al. 2002; Traisnel 2001; APUR 2007).Those research stand to show that a dense urban form is more effective in terms of consumption for heating. We can observe the same trend for transport consumptions and GHG emissions: travel number and distance are reduced in a dense city (Traisnel 2001; Newman & Kenworthy 1989). In France, one of the key objectives of the Grenelle of the environment is to increase the production of renewable energy up to 20 Mtoe in 2020. Integration of renewable energy in the city is now a priority for decision makers. The solutions can be numerous: solar energy (thermal and photovoltaic), wind turbines, district heating, geothermy or ELRPDVV /RFDO SROLWLFDO DFWLRQV VXFK DV ³pFR-TXDUWLHUV´ VXVWDLQDEOH QHLJKERXUKRRGV RU ³3ODQ FOLPDWH´ FLW\ planning guidelines to reduce climate change) are increasing. As an example, we can mention a study for the city of Lyon that has assessed the renewable energy potential to 10 to 15% of his consumption (AXENNE 2006).
Fig. 1. Screen showings thematic map "land occupation coefficient" Following the same approach, we developed a new software called MUSE (Modelling Urban Shape and Energy) which is a part of the already existing Geographical Information System (GIS) dedicate to the scientific modelling and simulation OrbisGIS. The OrbisGIS software was develop at IRSTV (Research Institute on Urban Sciences and Techniques, CNRS/FR-2488). The MUSE tool reproduces the functionalities of MORPHOLOGIC along with a userfriendly interface. Furthermore, some new features have been added to MUSE: one of them calculates solar potential (photovoltaic and thermal), using a simplified model of shadows in which urban geometry LVUHGXFHGLQWRD³&DQ\RQ VWUHHW´
Yet, the urban energy paradox is as follows: on the one hand, the densification of cities reduces transportation and buildings consumptions. On the other hand, this densification has a negative impact on urban microclimate and renewable energies potential. Indeed, solar gains in urban areas remain highly dependent on the urban morphology and the shadows it make. In the same way, small wind turbines can only be installed in a few cases, when the roughness caused by the buildings does not reduce wind speed too much. We can clearly identify the challenge for urban planners faced with urban energy paradox.
Model for solar energy potential assessment
1.1 MUSE: towards renewable energies in the city
The model for the assessment of solar energy potential takes into account the following parameters:
The SAGACités project Our research is based on a previous work developed in 2002 by Luc Adolphe and team in the SAGACités project (Système d'Aide à la Gestion des Ambiances urbaines,
1506
-
the position of the sun for each hour of one day per month, depending on the location;
-
the weather data related to solar radiation for each hour of one day per month (standardized data from Météo-France),
INCOM 2012, May 23-25, 2012 Bucharest, Romania
-
urban geometry,
-
urban surface materials.
One may also notice that the urban geometry is extracted from existing urban databases. We chose to use the urban GDWDEDVH ³%' 7RSR´ SURGXFHG E\ WKH ,*1 1DWLRQDO Geographical Institute) which is available in a standardized form common to all French cities. This database gives information regarding the shape (footprint and height) of buildings but also on the nature of the surface: the database contains several layers of information (buildings, vegetation, roads, altimetry, hydrographic network,) directly usable by MUSE. This work is based on a simplified model of shadow masks previously developed by Robinson (Robinson et al. 2007). This method is based on abstracting real urban skylines into an effective canyon which reduces sky views but also contributes to the reflected radiation to receiving surface. The "Canyon street" method is used along with the anisotropic tilted surface irradiance model developed by Perez (Torres et al. 2006). This section describes the method to account for sky obstructions for a single façade. We calculate solar irradiance for each façade and for each hour of one day (the 15th) of each month. -
Assuming the variables as follows:
-
The direct horizontal irradiance Ibh
-
The direct normal irradiance Ibh
-
The diffuse horizontal irradiance Idh
-
The global horizontal irradiance Igh
-
-
Once the urban geometry is simplified, we determine the three components of the irradiance on a surface: the direct irradiance IE, the diffuse irradiance IG and the reflected irradiance I! Calculation of diffuse irradiance incident on a plane of slope :
The facade or roof slope ZKHUH Ls KRUL]RQWDO IDFLQJ XS LV YHUWLFDO DQG LV horizontal facing down) the reflectance of surfaces (wall, roof or ground) !
We abstract real urban skylines into an effective canyon: we combine the solar obstructions into an equivalent continuous skyline of angular height u (cf. ). The angle u may be found from the following expression:
1 1 cos( + u) = ³³ cos .d& 2
Fig. 2. Canyon street model
(1)
:KHUH ¶LVWKHDQJOHRILQFLGHQFHRIVRPHVPDOOREVWUXFWLRQ element and G& its solid angle.
º ª a ( 1+ cos + u) I d = I dh «( 1 F1 ). + B.F1 . 0 + S.F2 sin » (2) 2 a1 ¼ ¬ Where F1, F2, a0 anda1are variables of Perez anisotropic sky irradiance model (Torres et al. 2006). Calculation of direct irradiance incident on a plane of slope :
I bE = I bK .G t . cos ]
(3)
:KHUH 1t is the proportion of the surface that can be seen 1t DQG (rad) is the mean angle of incidence between the patch and our plane together. 1t is calculated by discrediting the façade in a grid. We test if the sun can be seen from the center of each cell of the grid. For each time interval (24 hours per 12 months) we calculate the ratio of cells where the sun is visible on the total number RIFHOOV7KLVJLYHVDPDWUL[1t. Calculation of reflected irradiance incident on a plane of VORSH The sun is reflected from the wall facing the plane and from the ground. As a consequence, we must calculate the global irradiance incident on the opposite wall of the street canyon. ,*HTXDOVWKHVXPRIGLUHFWLUUadiance incident on wall 2
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INCOM 2012, May 23-25, 2012 Bucharest, Romania
(calculated from (3)), diffuse irradiance incident on wall 2 (calculated from (2)) and reflected irradiance incident on wall 2 from our first wall and from the ground (calculated by iteration from (5), (6) and (7)) In the same way, we evaluate global irradiance incident on the ground of the street as the sum of direct irradiance I EVRO (calculated from (3)), diffuse irradiance IGJURXQG and reflected irradiance I!JURXQG.
cos/1 cos/ 2 )+ 2 1 cos /1 1 cos / 2 ) (!.I GVO11 . + !.I GVO12 . 2 2
A database that allows running SQL queries from connections created via a user form were developed. The knowledge structure used in MUSE is based on a system of environmental indicators, as well as a multi-criteria analysis the weighted average. The indicators are grouped into four areas: energy, building, transportation and vegetation.
I GJURXQ = I bJURXQ + I dh .(
(4)
With these contributions known, reflected radiation may be modelled by iteratively solving those expressions until convergence is reached. Reflected irradiance can be calculated using those equations: For the upper part of the wall 2:
I !8 = I G . !2 .
cos1 cos(1 + u) 2
(5)
For the lower part of the wall 2:
I !/ = I G . !2 .
cos(1 u ) cos 2
Fig. 3. Graphical User Interface.
(6)
3. MUSE GLOBAL ARCHITECTURE
For the ground:
I !JURXQG = I GJURXQ . ! ground .
1 cos(1 u ) (7) 2
I GPX1 = I b + I d + I !8 + I !/ ! 2 + I !* ground (8) From this model we can evaluate solar irradiance for each façade and roof in W / m² for each hour of one day of each month. We then calculate the potential of usable energy by using characteristics of existing solar panels. 2. FROM MODELLING TO MUSE SYSTEM In order to validate this model we propose to compare its results with field measurements and results from some validated existing software. The model will then be integrated into the MUSE platform to obtain a functional tool. The MUSE platform is open-source software based on Java, which has the support of a large community of developers and for which many open source libraries, for viewing and processing spatial data, exist. The Graphical User Interface (GUI) of MUSE under OrbisGIS, shown in Figure 3, is composed of modules serving different needs and the GIS module is used to integrate most needed datasets while analyzing the various spatial entities and preparing the input for models. It also allows to store and to visualize the results.
For purposes related to the development of software MUSE, two solutions are retained: first Java, which has the support of a large community of developers and for which many open source libraries exist, and secondly SVG, a standard format in which the tools of creation are those in which XML and plugins for reading are free. We choose to compute MUSE with Java. The solution envisaged initially was based entirely on free libraries on Apache Felix and Geotools usually used for implementation of the OPEN SERVICES GATEWAY INITIATIVE (OSGi) model and GIS. These libraries are compatible with the general public licence (GPL 3) of Muse and will facilitate the implementation of the desired solution. This method has some very clear advantages over other methods illustrated by the simplicity of implementation and the robustness of libraries. The principal difficulty of this method comes from the fact that many graphical components must be created and the complexity for integrating component of other free GIS solutions. The first developments by using this solution show the difficulty of integrating components of other open source GIS libraries. In order to solve this problem, it is important to introduce an intermediate method. The solution adopted here is based on the same overall architecture (Figure 4 and 5) but no longer based on the same libraries.
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INCOM 2012, May 23-25, 2012 Bucharest, Romania
is relevant at
generates Objective Indicator
V
V
V
Definition level
Full application framework;
9
Easy reuse of models developed in other solutions;
9
Use of Eclipse technologies, including the notion of perspective that will enable us to easily configure the interface for implemented features and block generator.
V
influences
Evaluator
9
The only limitation of this method is recoding some of the code already developed.
Parameter
4. DISCUTION
Fig. 4. Conceptual Model for Evaluator. Eclipse RCP (Rich Client Platform) is a framework for creating modular applications based on Eclipse technologies that will provide the foundation for Muse. This framework allows reuse of all software components on which the Eclipse IDE is built. Developing on this basis is a time saver for the definition of a graphical user interfaces (GUI). Moreover this Framework allows a very modular development through the introduction of notions of plugins Eclipse RCP (OSGI VWDQGDUG ³H[WHQVLRQSRLQWV´DQG³H[WHQVLRQV´FRQFHSW7KH concept of extension points allows contributing functionality to plugins without changing the existing code of the plugin. This is a powerful concept as it allows to developed functionality decoupled. The extension mechanism is declarative, therefore the dependencies can be evaluated without loading any code. This allows easy loading of plugins and therefore scales very well with lots of plugins.
As the model is still in its development process, a number of points are worth discussing. Firstly, the geographical origin of the data leads to uncertainty in the calculations which must be estimated. Indeed, each object in the urban database ³%'7RSR´,*1LVUHSUHVHQWHGE\DSRO\JRQSXQFWXDWHGE\D vertex, meaning a point at which an altitude is specified. In addition, there may be inconsistencies in the databases. The provided accuracy calculation will verify that the error rate may be coherent with the urban scale of analysis. Secondly, we must not forget that the renewable energy potential is not the only limiting factor. Indeed, many obstacles still remain such as the architectural integration, structural issues, visual effects and acceptance of citizens. Moreover, the model proposes as well to take into account the technical and economical feasibility of the solar installation in order to calculate the solar potential of an urban block, neighbourhood or city. 5. CONCLUSION MUSE will allow urban planners to target urban areas that are not energy-efficient and to evaluate the best solutions: thermal renovation and / or development of decentralized energy systems. MUSE will also help designers to plan new or existing districts and to evaluate the best urban form to reduce GHG emissions and energy consumption.
Fig. 5. Global Architecture of MUSE. The Eclipse RCP also integrates Eclipse Equinox OSGi, implementation of the model similar to Apache Felix in the Eclipse ecosystem. Eclipse RCP also integrates all modules developed for Eclipse, its derivatives, and for solutions based on Eclipse RCP as the open-source GIS such gvSIG and uDig.
The innovation of MUSE lies in the development of a Decision Support System (DSS) integrating the morphology of cities with energetic issues: It allows the intersection of geometrical, topological, energetic and demographical data. For instance, this software allows the user to easily set up a system to monitor several energy related variables: Floor Area Ratio (FAR), building height, length of streets, adjacency of buildings, facades, street-distance to bus stop and other demographic parameters. The importance of this platform comes from the fact that it allows users to evaluate energy consumption and renewable energy potential across one or more urban blocks. In the future, other models of renewable energy under development such as: wind turbines, district heating, geothermal or biomass will be integrated in the platform in RUGHUWRZLGHQSODQQHUV¶RSWLRQV.
We summarize here the principal advantages of this alternative method:
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REFERENCES ADEME (2008). Maitrise des énergies et énergies renouvelables, chiffres clés 2007. $385 &RQVRPPDWLRQVG¶pQHUJLHHWpPLVVLRQVGHJD] à effet de serre liées au chauffage des résidences principales parisiennes. AXENNE, Pour le Grand Lyon (2006). Diagnostic énergies UHQRXYHODEOHGXWHUULWRLUHGHO¶DJJORPpUDWLRQO\RQQDLVHj ILQHWpWXGHGXSRWHQWLHOjO¶KRUL]RQ Adolphe, L. et al. (2002). SAGACité, Vers un Système G¶$LGHjOD*HVWLRQGHV$PELDQFHVXUEDLQHV Janssen, R. (1992). Multiobjective Decision Support for Environmental Management. Dordrecht, Kluwer Academic Publishers. Lahdelma, R., P. Salminen (2000). "Using Multicriteria Methods in Environmental Planning and Management." Environmental Management 26 (6): 595±605. Linkov, I., F. K. Satterstrom, et al. (2006). "From comparative risk assessment to multicriteria decision analysis and adaptive management: Recent developments and applications." Environment International 32: 1072±1093. 0LQLVWqUH GH O¶(FRORJLH GX 'pYHORSSHPHQW 'XUDEOH GHV Transports et du Logement (2010). Observation & statistiques - Définitions du domaine « Énergie ». Available at: http://www.statistiques.developpementdurable.gouv.fr/rubrique.php3?id_rubrique=537 [Consulté janvier 25, 2011]. OrbisGIS, Institut de recherche en Sciences et Techniques de la Ville, IRSTV, Nante, www.orbisgis.org Newman, P.W.G. & Kenworthy, J.R. (1989). Gasoline consumption and cities. Journal of the American Planning Association, 55(1), p.24±37. Regan, H. M., F. W. Davis, et al. (2007). "Comprehensive criteria for biodiversity evaluation in conservation planning." Biodiversity Conservation 16: 2715± 2728. Robinson, D. et al. (2007). SUNtool-A new modelling paradigm for simulating and optimising urban sustainability. Solar Energy, 81(9), p.1196±1211. Torres, J.L., Blas, M.D. & García, A. (2006). New equations for the calculation of the horizon brightness irradiance in the model of Perez. Solar Energy, 80(7), p.746±750. Traisnel, J.P. (2001). Habitat et développement durable, bilan rétrospectif et prospectif. Les cahiers du CLIP, (13), p.5 - 72. Yatsalo, B., G. A. Kiker, et al. (2007). "Application of Multicriteria Decision Analysis Tools to Two Contaminated Sediment Case Studies." Integrated Environmental Assessment and Management 3 (2): 223±233.
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MUSE: An Open Urban Management Decision Support System M. Bonhomme*. H. Ait Haddou**. L. Adolphe* * (FROH1DWLRQDOH6XSpULHXUG¶$UFKLWHFWXUHGH7RXORXVH 83 rue Aristide Maillol, BP 10629, 31106 Toulouse cedex 1, France; e-mail: {marion.bonhomme, hassan.ait-haddou,luc.adolphe}@toulouse.archi.fr. ** Centre National de Recherches Météorologiques ± GMME/TURBAU, 42,Avenue Gaspard Coriolis ± 31 057 Toulouse Cedex 01 Abstract: This paper describes a decision support tool for urban planners. Numerous research studies show that energy consumed and produced in cities can be related to its morphology. Yet, the urban energy paradox is defined as follows: on the one hand, the densification of cities reduces transportation and buildings consumptions. On the other hand, this densification has a negative impact on urban microclimate and renewable energies potential. The goal of this work is to develop a decision support system for urban planners faced with urban energy paradox. Our research is based on a previous work developed by Luc Adolphe et al. in the SAGACités project. This research led to the development of the geographic information system (GIS) platform called MORPHOLOGIC, which, among other things, evaluates the energy consumption of city blocks. Our goal is to add new features in MORPHOLOGIC: one of them calculates solar potential (photovoltaic and thermal), using a simplified model of shadows. The new version of MORPHOLOGIC called MUSE, based on the open source GIS called OrbisGis developed by the IRSTV institute, will allow urban planners to evaluate the best urban form to reduce GHG emissions. Keywords: Decision Support Systems, Integrated Modelling System, Spatial Database Management System, energy consumption, renewable energy, urban form
1. INTRODUCTION
The complexities and difficulties of issues involved in urban management and climatic change makes very difficult taking decision by urban planners and force theme to new tools and methods. In fact, spatial decision problems in urban planning are the aggregation of different evaluation criteria that urban planners should be taken into account for evaluating different development policies. Therefore, multi-criteria decision analysis (MCDA) has become the most important tool in environmental decision-making for modelling the problem of satisfying multiple objectives (Janssen 1992, Lahdelma et al. 2000, Linkov et al. 2006, Regan et al. 2007, Yatsalo et al. 2007). Recall that the conflicting aspects between the objectives and preferences during the decision process, and the significant evaluation criteria to be considered reduce significantly the chance to reach a solution supported by all the urban planners in the decision process. It is then necessary to develop and compute new tools capable of processing data inputs of varying formats, numerical models and expert opinions in multi objective decision making scenario. Spatial and environmental Decision Support Systems (DSS) combined with Geographic Information System (GIS) are among the most promising approaches to confront such situations. Therefore, DSS and GIS are gaining importance and widespread acceptance as a tool for decision making in the infrastructure, water resources, environmental 978-3-902661-98-2/12/$20.00 © 2012 IFAC
management, spatial analysis and urban development planning. Different large scale models, mostly based in the applied mathematics (operational research and economics) such linear programming, optimization techniques, costbenefit analysis have been developed to deal with spatial decision problems in urban planning. The work presented in this paper is part of a national research program that assesses the impact of various urban forms (dense, sprawled or green city) on energy consumption, renewable energies production and climate change until 2100. This work received support from the French National Research Agency (Agence Nationale de la Recherche) with the reference ANR-09-VILL-0003. For the last decades, numerous researches have studied energy-efficient buildings. Many models have been developed to take into account isolation, inertia, solar gain and renewable energy production. However, those tools do not take into account issues related to urban scale, and cannot be used by urban planners questioning energy±efficiency in the city. This change in spatial and temporal scale to the scale of urban development requires a change of paradigm mixing urban form, energy use, renewable energy potentially produced by buildings and urban microclimate. Few researches deal with the physical and morphological complexity of energy- efficient cities.
1505
10.3182/20120523-3-RO-2023.00214
INCOM 2012, May 23-25, 2012 Bucharest, Romania
However, cities are responsible for the majority of energy consumption and greenhouse gas emissions on the planet. In 2008, residential and commercial buildings in France emitted 96.9 Mt CO2 (25.9% of emissions in France) and consumed 69.4 Mtoe (43% of energy consumption in France) (Ministère GHO¶(FRORJLHGX'pYHORSSHPHQW'XUDEOHGHV7UDQVSRUWVHW du Logement 2010). Among these consumptions, 48.1 Mtoe concerned heating and air conditioning alone (68% of buildings consumption) (ADEME 2008).Transport systems are major emitters of greenhouse gases, being responsible for 31.1% of GHG emissions and for 38.4% of energy consumption in France in 2008 (MinistèUH GH O¶(FRORJLHGX Développement Durable, des Transports et du Logement 2010). In a context where urban population is increasing (80% of the population in developed countries) the energy consumed in cities will consequently be intensified. As a result, urban planners require Decision Support Systems (DSS) to plan more sustainable cities.
(Adolphe et al. 2002)). After measurements and numerical model evaluations in urban areas, this research led to the development of a geographic information system (GIS) platform called MORPHOLOGIC. This tool allows, among other things, to evaluate the energy consumption from one single to several city blocks taking into account buildings, streets, squares and vegetated areas. It also calculates a set of urban morphological indicators such as compactness, density and contiguity. MORPHOLOGIC allows the intersection of geometric data (buildings footprint, buildings height, length of the streets), topological data (adjacency of buildings, facades, distance to a bus station etc.) and demographic data (population per block). The platform calculates a system of indicators which balance energy, environment and comfort to help out in the urban decision making.
The energy consumed and produced in the city is closely related to its morphology (Adolphe et al. 2002; Traisnel 2001; APUR 2007).Those research stand to show that a dense urban form is more effective in terms of consumption for heating. We can observe the same trend for transport consumptions and GHG emissions: travel number and distance are reduced in a dense city (Traisnel 2001; Newman & Kenworthy 1989). In France, one of the key objectives of the Grenelle of the environment is to increase the production of renewable energy up to 20 Mtoe in 2020. Integration of renewable energy in the city is now a priority for decision makers. The solutions can be numerous: solar energy (thermal and photovoltaic), wind turbines, district heating, geothermy or ELRPDVV /RFDO SROLWLFDO DFWLRQV VXFK DV ³pFR-TXDUWLHUV´ VXVWDLQDEOH QHLJKERXUKRRGV RU ³3ODQ FOLPDWH´ FLW\ planning guidelines to reduce climate change) are increasing. As an example, we can mention a study for the city of Lyon that has assessed the renewable energy potential to 10 to 15% of his consumption (AXENNE 2006).
Fig. 1. Screen showings thematic map "land occupation coefficient" Following the same approach, we developed a new software called MUSE (Modelling Urban Shape and Energy) which is a part of the already existing Geographical Information System (GIS) dedicate to the scientific modelling and simulation OrbisGIS. The OrbisGIS software was develop at IRSTV (Research Institute on Urban Sciences and Techniques, CNRS/FR-2488). The MUSE tool reproduces the functionalities of MORPHOLOGIC along with a userfriendly interface. Furthermore, some new features have been added to MUSE: one of them calculates solar potential (photovoltaic and thermal), using a simplified model of shadows in which urban geometry LVUHGXFHGLQWRD³&DQ\RQ VWUHHW´
Yet, the urban energy paradox is as follows: on the one hand, the densification of cities reduces transportation and buildings consumptions. On the other hand, this densification has a negative impact on urban microclimate and renewable energies potential. Indeed, solar gains in urban areas remain highly dependent on the urban morphology and the shadows it make. In the same way, small wind turbines can only be installed in a few cases, when the roughness caused by the buildings does not reduce wind speed too much. We can clearly identify the challenge for urban planners faced with urban energy paradox.
Model for solar energy potential assessment
1.1 MUSE: towards renewable energies in the city
The model for the assessment of solar energy potential takes into account the following parameters:
The SAGACités project Our research is based on a previous work developed in 2002 by Luc Adolphe and team in the SAGACités project (Système d'Aide à la Gestion des Ambiances urbaines,
1506
-
the position of the sun for each hour of one day per month, depending on the location;
-
the weather data related to solar radiation for each hour of one day per month (standardized data from Météo-France),
INCOM 2012, May 23-25, 2012 Bucharest, Romania
-
urban geometry,
-
urban surface materials.
One may also notice that the urban geometry is extracted from existing urban databases. We chose to use the urban GDWDEDVH ³%' 7RSR´ SURGXFHG E\ WKH ,*1 1DWLRQDO Geographical Institute) which is available in a standardized form common to all French cities. This database gives information regarding the shape (footprint and height) of buildings but also on the nature of the surface: the database contains several layers of information (buildings, vegetation, roads, altimetry, hydrographic network,) directly usable by MUSE. This work is based on a simplified model of shadow masks previously developed by Robinson (Robinson et al. 2007). This method is based on abstracting real urban skylines into an effective canyon which reduces sky views but also contributes to the reflected radiation to receiving surface. The "Canyon street" method is used along with the anisotropic tilted surface irradiance model developed by Perez (Torres et al. 2006). This section describes the method to account for sky obstructions for a single façade. We calculate solar irradiance for each façade and for each hour of one day (the 15th) of each month. -
Assuming the variables as follows:
-
The direct horizontal irradiance Ibh
-
The direct normal irradiance Ibh
-
The diffuse horizontal irradiance Idh
-
The global horizontal irradiance Igh
-
-
Once the urban geometry is simplified, we determine the three components of the irradiance on a surface: the direct irradiance IE, the diffuse irradiance IG and the reflected irradiance I! Calculation of diffuse irradiance incident on a plane of slope :
The facade or roof slope ZKHUH Ls KRUL]RQWDO IDFLQJ XS LV YHUWLFDO DQG LV horizontal facing down) the reflectance of surfaces (wall, roof or ground) !
We abstract real urban skylines into an effective canyon: we combine the solar obstructions into an equivalent continuous skyline of angular height u (cf. ). The angle u may be found from the following expression:
1 1 cos( + u) = ³³ cos .d& 2
Fig. 2. Canyon street model
(1)
:KHUH ¶LVWKHDQJOHRILQFLGHQFHRIVRPHVPDOOREVWUXFWLRQ element and G& its solid angle.
º ª a ( 1+ cos + u) I d = I dh «( 1 F1 ). + B.F1 . 0 + S.F2 sin » (2) 2 a1 ¼ ¬ Where F1, F2, a0 anda1are variables of Perez anisotropic sky irradiance model (Torres et al. 2006). Calculation of direct irradiance incident on a plane of slope :
I bE = I bK .G t . cos ]
(3)
:KHUH 1t is the proportion of the surface that can be seen 1t DQG (rad) is the mean angle of incidence between the patch and our plane together. 1t is calculated by discrediting the façade in a grid. We test if the sun can be seen from the center of each cell of the grid. For each time interval (24 hours per 12 months) we calculate the ratio of cells where the sun is visible on the total number RIFHOOV7KLVJLYHVDPDWUL[1t. Calculation of reflected irradiance incident on a plane of VORSH The sun is reflected from the wall facing the plane and from the ground. As a consequence, we must calculate the global irradiance incident on the opposite wall of the street canyon. ,*HTXDOVWKHVXPRIGLUHFWLUUadiance incident on wall 2
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(calculated from (3)), diffuse irradiance incident on wall 2 (calculated from (2)) and reflected irradiance incident on wall 2 from our first wall and from the ground (calculated by iteration from (5), (6) and (7)) In the same way, we evaluate global irradiance incident on the ground of the street as the sum of direct irradiance I EVRO (calculated from (3)), diffuse irradiance IGJURXQG and reflected irradiance I!JURXQG.
cos/1 cos/ 2 )+ 2 1 cos /1 1 cos / 2 ) (!.I GVO11 . + !.I GVO12 . 2 2
A database that allows running SQL queries from connections created via a user form were developed. The knowledge structure used in MUSE is based on a system of environmental indicators, as well as a multi-criteria analysis the weighted average. The indicators are grouped into four areas: energy, building, transportation and vegetation.
I GJURXQ = I bJURXQ + I dh .(
(4)
With these contributions known, reflected radiation may be modelled by iteratively solving those expressions until convergence is reached. Reflected irradiance can be calculated using those equations: For the upper part of the wall 2:
I !8 = I G . !2 .
cos1 cos(1 + u) 2
(5)
For the lower part of the wall 2:
I !/ = I G . !2 .
cos(1 u ) cos 2
Fig. 3. Graphical User Interface.
(6)
3. MUSE GLOBAL ARCHITECTURE
For the ground:
I !JURXQG = I GJURXQ . ! ground .
1 cos(1 u ) (7) 2
I GPX1 = I b + I d + I !8 + I !/ ! 2 + I !* ground (8) From this model we can evaluate solar irradiance for each façade and roof in W / m² for each hour of one day of each month. We then calculate the potential of usable energy by using characteristics of existing solar panels. 2. FROM MODELLING TO MUSE SYSTEM In order to validate this model we propose to compare its results with field measurements and results from some validated existing software. The model will then be integrated into the MUSE platform to obtain a functional tool. The MUSE platform is open-source software based on Java, which has the support of a large community of developers and for which many open source libraries, for viewing and processing spatial data, exist. The Graphical User Interface (GUI) of MUSE under OrbisGIS, shown in Figure 3, is composed of modules serving different needs and the GIS module is used to integrate most needed datasets while analyzing the various spatial entities and preparing the input for models. It also allows to store and to visualize the results.
For purposes related to the development of software MUSE, two solutions are retained: first Java, which has the support of a large community of developers and for which many open source libraries exist, and secondly SVG, a standard format in which the tools of creation are those in which XML and plugins for reading are free. We choose to compute MUSE with Java. The solution envisaged initially was based entirely on free libraries on Apache Felix and Geotools usually used for implementation of the OPEN SERVICES GATEWAY INITIATIVE (OSGi) model and GIS. These libraries are compatible with the general public licence (GPL 3) of Muse and will facilitate the implementation of the desired solution. This method has some very clear advantages over other methods illustrated by the simplicity of implementation and the robustness of libraries. The principal difficulty of this method comes from the fact that many graphical components must be created and the complexity for integrating component of other free GIS solutions. The first developments by using this solution show the difficulty of integrating components of other open source GIS libraries. In order to solve this problem, it is important to introduce an intermediate method. The solution adopted here is based on the same overall architecture (Figure 4 and 5) but no longer based on the same libraries.
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is relevant at
generates Objective Indicator
V
V
V
Definition level
Full application framework;
9
Easy reuse of models developed in other solutions;
9
Use of Eclipse technologies, including the notion of perspective that will enable us to easily configure the interface for implemented features and block generator.
V
influences
Evaluator
9
The only limitation of this method is recoding some of the code already developed.
Parameter
4. DISCUTION
Fig. 4. Conceptual Model for Evaluator. Eclipse RCP (Rich Client Platform) is a framework for creating modular applications based on Eclipse technologies that will provide the foundation for Muse. This framework allows reuse of all software components on which the Eclipse IDE is built. Developing on this basis is a time saver for the definition of a graphical user interfaces (GUI). Moreover this Framework allows a very modular development through the introduction of notions of plugins Eclipse RCP (OSGI VWDQGDUG ³H[WHQVLRQSRLQWV´DQG³H[WHQVLRQV´FRQFHSW7KH concept of extension points allows contributing functionality to plugins without changing the existing code of the plugin. This is a powerful concept as it allows to developed functionality decoupled. The extension mechanism is declarative, therefore the dependencies can be evaluated without loading any code. This allows easy loading of plugins and therefore scales very well with lots of plugins.
As the model is still in its development process, a number of points are worth discussing. Firstly, the geographical origin of the data leads to uncertainty in the calculations which must be estimated. Indeed, each object in the urban database ³%'7RSR´,*1LVUHSUHVHQWHGE\DSRO\JRQSXQFWXDWHGE\D vertex, meaning a point at which an altitude is specified. In addition, there may be inconsistencies in the databases. The provided accuracy calculation will verify that the error rate may be coherent with the urban scale of analysis. Secondly, we must not forget that the renewable energy potential is not the only limiting factor. Indeed, many obstacles still remain such as the architectural integration, structural issues, visual effects and acceptance of citizens. Moreover, the model proposes as well to take into account the technical and economical feasibility of the solar installation in order to calculate the solar potential of an urban block, neighbourhood or city. 5. CONCLUSION MUSE will allow urban planners to target urban areas that are not energy-efficient and to evaluate the best solutions: thermal renovation and / or development of decentralized energy systems. MUSE will also help designers to plan new or existing districts and to evaluate the best urban form to reduce GHG emissions and energy consumption.
Fig. 5. Global Architecture of MUSE. The Eclipse RCP also integrates Eclipse Equinox OSGi, implementation of the model similar to Apache Felix in the Eclipse ecosystem. Eclipse RCP also integrates all modules developed for Eclipse, its derivatives, and for solutions based on Eclipse RCP as the open-source GIS such gvSIG and uDig.
The innovation of MUSE lies in the development of a Decision Support System (DSS) integrating the morphology of cities with energetic issues: It allows the intersection of geometrical, topological, energetic and demographical data. For instance, this software allows the user to easily set up a system to monitor several energy related variables: Floor Area Ratio (FAR), building height, length of streets, adjacency of buildings, facades, street-distance to bus stop and other demographic parameters. The importance of this platform comes from the fact that it allows users to evaluate energy consumption and renewable energy potential across one or more urban blocks. In the future, other models of renewable energy under development such as: wind turbines, district heating, geothermal or biomass will be integrated in the platform in RUGHUWRZLGHQSODQQHUV¶RSWLRQV.
We summarize here the principal advantages of this alternative method:
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REFERENCES ADEME (2008). Maitrise des énergies et énergies renouvelables, chiffres clés 2007. $385 &RQVRPPDWLRQVG¶pQHUJLHHWpPLVVLRQVGHJD] à effet de serre liées au chauffage des résidences principales parisiennes. AXENNE, Pour le Grand Lyon (2006). Diagnostic énergies UHQRXYHODEOHGXWHUULWRLUHGHO¶DJJORPpUDWLRQO\RQQDLVHj ILQHWpWXGHGXSRWHQWLHOjO¶KRUL]RQ Adolphe, L. et al. (2002). SAGACité, Vers un Système G¶$LGHjOD*HVWLRQGHV$PELDQFHVXUEDLQHV Janssen, R. (1992). Multiobjective Decision Support for Environmental Management. Dordrecht, Kluwer Academic Publishers. Lahdelma, R., P. Salminen (2000). "Using Multicriteria Methods in Environmental Planning and Management." Environmental Management 26 (6): 595±605. Linkov, I., F. K. Satterstrom, et al. (2006). "From comparative risk assessment to multicriteria decision analysis and adaptive management: Recent developments and applications." Environment International 32: 1072±1093. 0LQLVWqUH GH O¶(FRORJLH GX 'pYHORSSHPHQW 'XUDEOH GHV Transports et du Logement (2010). Observation & statistiques - Définitions du domaine « Énergie ». Available at: http://www.statistiques.developpementdurable.gouv.fr/rubrique.php3?id_rubrique=537 [Consulté janvier 25, 2011]. OrbisGIS, Institut de recherche en Sciences et Techniques de la Ville, IRSTV, Nante, www.orbisgis.org Newman, P.W.G. & Kenworthy, J.R. (1989). Gasoline consumption and cities. Journal of the American Planning Association, 55(1), p.24±37. Regan, H. M., F. W. Davis, et al. (2007). "Comprehensive criteria for biodiversity evaluation in conservation planning." Biodiversity Conservation 16: 2715± 2728. Robinson, D. et al. (2007). SUNtool-A new modelling paradigm for simulating and optimising urban sustainability. Solar Energy, 81(9), p.1196±1211. Torres, J.L., Blas, M.D. & García, A. (2006). New equations for the calculation of the horizon brightness irradiance in the model of Perez. Solar Energy, 80(7), p.746±750. Traisnel, J.P. (2001). Habitat et développement durable, bilan rétrospectif et prospectif. Les cahiers du CLIP, (13), p.5 - 72. Yatsalo, B., G. A. Kiker, et al. (2007). "Application of Multicriteria Decision Analysis Tools to Two Contaminated Sediment Case Studies." Integrated Environmental Assessment and Management 3 (2): 223±233.
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