**4. Conclusion**

**3. Methodology application**

86 Sustainable Cities - Authenticity, Ambition and Dream

**3.1. An interactive MC-SDSS tool**

The present section illustrates the interface of the developed MC-SDSS tool based on the methodology framework explained in the previous section (Section 2). As mentioned before, this tool is an interactive plug-in in GIS environment, which has been adapted from an existing urban planning tool called CommunityViz. The developed MC-SDSS tool supports the stakeholders in urban energy planning through participatory and collaborative processes. It helps make better decisions by expressing the stakeholders' preferences and their conflicting objectives.

All the phases were integrated in order to create a new MC-SDSS tool. This tool uses CommunityViz, which is an ArcView modular GIS-based decision support system developed by the Orton Family Foundation (http://www.communityviz.com). The above-said tool is able to integrate different types of data such as scripts, numbers, 2D maps, 3D visualization and raster in a real-time and multidimensional environment [17]. CommunityViz encompasses two main components as extensions to ArcGIS: (i) Scenario 360 to map and analyze and (ii) Scenario 3D to visualize. Conceptually, Scenario 360 can be described as a spatial spreadsheet allowing for calculations on spatially related data and formulas that call standard GIS functions [18]. Since each formula, assumption and dependency is viewable and editable, there is

CommunityViz Scenario 360 adds interactive analysis tools and a decision-making framework to the ArcGIS platform with which stakeholders can understand the planning processes easily.

not any 'black box' element to a model defined in Scenario 360 [18].

**Figure 4.** CommunityViz interface; the case study of Settimo Torinese.

This chapter summarizes the overall conclusion and relative limitation for each phase of planning. In particular, this work creates a link between energetical, economical, societal, technical and environmental performances of retrofitting interventions. The research boundaries were delineated by focusing on existing residential building stock since they characterize the context of most European cities. The relative available data of these buildings were first collected and georeferenced from various sources. Based on the created geospatial database, the building energy consumption patterns were statistically modeled to map the current energy patterns over the entire city. Afterwards, the archetype model of the city was created in order to speed up and ease the future energy-saving simulations by applying the retrofitting solutions. The geospatial database was used as the object of multi-criteria analysis assessments. Finally, an interactive MC-SDSS was created to support the DMs in defining energy-saving scenarios in real time.

*4.1.1. Limitations*

faces several barriers including:

Regarding the data collection

This study suggests first a spatial data collection and then an integrated procedure of urban energy modeling approaches based on the data collected (i.e. statistical and engineering). This

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• The energy consumption data is not usually open source; thus, a huge effort was needed to collect the data from different entities and to ask the collaboration from local stakeholders. • The georeferencing procedure of data could be also a challenging issue. In many cases, the necessary information related to the buildings are associated with the building number (as points) rather than the building polygon. The tricky issue is that these points are sometimes situated between two or three buildings having the same distance. Thus, it is not easy to

• A vast amount of historical available data is needed. For many regions, it is almost impos-

• The intrinsic limitation of statistical methods concerns the microclimate effects, which were not taken into account in the present work. In fact, a microclimate model that would give a single value for the whole city for air temperature would not significantly improve the

• The need for high-level detailed thermo-physics data of the buildings in the city.

• Setting up the simulations can be a tedious task requiring a lot of time and expertise.

• The simulations themselves are very time-consuming, and they require high-performing

As illustrated in **Figure 3**, a MC-SDSS has been developed to support the stakeholders with different backgrounds and preferences. The tool is an interactive plug-in in ArcGIS environment. MC-SDSS is able to help participants in a user-friendly way to define energy refurbishment scenarios. Moreover, the tool gives an opportunity to generate the suitability maps, with which the stakeholders can analyze the grade of the suitability of their decisions. The development of MC-SDSS is based on an existing tool, named CommunityViz. Originally, CommunityViz is a software used to support urban planning purposes. Within this research, CommunityViz was adapted and modeled to support UIEP. Two main integrated instruments, Interactive Impact

understand that the data belongs precisely to which building.

sible to have a monitored data in terms of energy performances.

results of the current model presented in this work.

Regarding the statistical modeling approach

Regarding engineering modeling approach

processors in order to perform the entire city.

**4.2. Phase III**

#### **4.1. Phases I and II**

As illustrated in **Figure 3**, to model the energy consumption over the entire city, a large number of historical data are needed. The most challenging issue was related to collecting and integrating the built environment data and information since the data are significantly scattered among several entities at the local level, and there is a lack of interoperability among the data sources. Actually, this section reports that one of the main barriers to developing a robust and detailed analysis is correlated with the data collection procedure. Especially in Italy, information about building stock and their energy performances are derived from different regional and local authorities, and they are not often homogeneous. Therefore, in order to set up an effective energy planning at the local scale, it is crucial to improve the quality of data availability and management. Data availability of buildings' energy consumption will hopefully improve in the future thanks to smart metering and real-time data monitoring following recent open data policy. To this end, a supportive GIS database where all the scattered information and data were georeferenced is first created.

Referring to the energy consumption modeling at the urban scale, this framework proposed a geospatial statistical modeling. Generally, statistical methods estimate the energy consumption based on a historical data. The model succeeded to estimate the energy consumption of most existing buildings, where the monitored data was not available. However, due to the strong dependency of statistical models on existing available data, these methods are not able to predict the impact of the future refurbishment solutions. Therefore, there was a need to simulate the future city energy performances. However, the simulation of the whole city may be extremely time-consuming.

Therefore, Phase II within the research framework in **Figure 3** proposed a novel engineering methodology to accelerate the urban area energy consumption simulations, including urban planning renovation scenarios. The energy demand of cities, as well as the microclimatic conditions, was calculated by using a simplified archetype 3D model designed as a function of the city urban characteristics. By the proposed archetype modeling approach, this method shows that the number of buildings to be simulated can be drastically reduced with no particular influence on the accuracy of the results. On one hand, the main advantage of an engineeringbased method is the capability of predicting energy savings for buildings after the application of renovation measures. On the other hand, these methods are very detailed models based on thermodynamic relationships and heat transfer calculations. As a general remark, the historical data can be used for the comparison against measured consumption data.

#### *4.1.1. Limitations*

context of most European cities. The relative available data of these buildings were first collected and georeferenced from various sources. Based on the created geospatial database, the building energy consumption patterns were statistically modeled to map the current energy patterns over the entire city. Afterwards, the archetype model of the city was created in order to speed up and ease the future energy-saving simulations by applying the retrofitting solutions. The geospatial database was used as the object of multi-criteria analysis assessments. Finally, an interactive MC-SDSS was created to support the DMs in defining energy-saving

As illustrated in **Figure 3**, to model the energy consumption over the entire city, a large number of historical data are needed. The most challenging issue was related to collecting and integrating the built environment data and information since the data are significantly scattered among several entities at the local level, and there is a lack of interoperability among the data sources. Actually, this section reports that one of the main barriers to developing a robust and detailed analysis is correlated with the data collection procedure. Especially in Italy, information about building stock and their energy performances are derived from different regional and local authorities, and they are not often homogeneous. Therefore, in order to set up an effective energy planning at the local scale, it is crucial to improve the quality of data availability and management. Data availability of buildings' energy consumption will hopefully improve in the future thanks to smart metering and real-time data monitoring following recent open data policy. To this end, a supportive GIS database where all the scattered

Referring to the energy consumption modeling at the urban scale, this framework proposed a geospatial statistical modeling. Generally, statistical methods estimate the energy consumption based on a historical data. The model succeeded to estimate the energy consumption of most existing buildings, where the monitored data was not available. However, due to the strong dependency of statistical models on existing available data, these methods are not able to predict the impact of the future refurbishment solutions. Therefore, there was a need to simulate the future city energy performances. However, the simulation of the whole city may

Therefore, Phase II within the research framework in **Figure 3** proposed a novel engineering methodology to accelerate the urban area energy consumption simulations, including urban planning renovation scenarios. The energy demand of cities, as well as the microclimatic conditions, was calculated by using a simplified archetype 3D model designed as a function of the city urban characteristics. By the proposed archetype modeling approach, this method shows that the number of buildings to be simulated can be drastically reduced with no particular influence on the accuracy of the results. On one hand, the main advantage of an engineeringbased method is the capability of predicting energy savings for buildings after the application of renovation measures. On the other hand, these methods are very detailed models based on thermodynamic relationships and heat transfer calculations. As a general remark, the histori-

cal data can be used for the comparison against measured consumption data.

information and data were georeferenced is first created.

be extremely time-consuming.

scenarios in real time.

88 Sustainable Cities - Authenticity, Ambition and Dream

**4.1. Phases I and II**

This study suggests first a spatial data collection and then an integrated procedure of urban energy modeling approaches based on the data collected (i.e. statistical and engineering). This faces several barriers including:

Regarding the data collection


Regarding the statistical modeling approach


Regarding engineering modeling approach


#### **4.2. Phase III**

As illustrated in **Figure 3**, a MC-SDSS has been developed to support the stakeholders with different backgrounds and preferences. The tool is an interactive plug-in in ArcGIS environment. MC-SDSS is able to help participants in a user-friendly way to define energy refurbishment scenarios. Moreover, the tool gives an opportunity to generate the suitability maps, with which the stakeholders can analyze the grade of the suitability of their decisions. The development of MC-SDSS is based on an existing tool, named CommunityViz. Originally, CommunityViz is a software used to support urban planning purposes. Within this research, CommunityViz was adapted and modeled to support UIEP. Two main integrated instruments, Interactive Impact Assessment and Suitability Analysis, were modeled. The main difficulty was to adapt the tool to energy urban planning, considering many complex aspects of this issue. Modeling of all retrofit dynamic attributes and the type of connection between all the attributes was another difficulty of this part. The modeling design process is quite complex. The model should chain all the data, attributes and indicators. This means that once the stakeholders change one parameter, others will change automatically in their proposed scenario. The participants are able to rapidly experiment different energy renovation scenarios and change the assumption. This creates an effective interaction between the stakeholders. They can visualize very complex problem of energy-saving scenarios simply by different dynamic colorful maps, charts and indicators.

**Conflict of interest**

**Other declarations**

**Author details**

**References**

to create an interactive MC-SDSS.

University of Turin, Turin, Italy

10.1016/j.rser.2013.01.033

jclepro.2017.07.142

fenrg.2017.00010

Sara Torabi Moghadam\* and Patrizia Lombardi

\*Address all correspondence to: sara.torabi@polito.it

The authors declare no conflicts of interest.

The present chapter is emerged from the 3 years research of PhD thesis of Sara Torabi Moghadam under the supervision of the Professor Patrizia Lombardi and co-supervision of the Professor Guglielmina Mutani in order to illustrate the methodological framework used

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91

Interuniversity Department of Regional and Urban Studies and Planning (DIST), Polytechnic

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[3] Moghadam ST. A new integrated multi-criteria spatial decision support system for urban energy planning in the built environment.PhD Theis, Doctoral Program in Urban

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#### *4.2.1. Limitations*

This study suggests the development of a new MC-SDSS, which can define dynamic retrofitting scenarios side by side with stakeholders. This faces several barriers including:


The proposed framework will help urban actors to develop energy planning projects, guiding them in the choice among a considerable number of existing planning approaches. The main advantages of the developed MC-SDSS in the field of urban energy planning can be summarized as follows:

