**2. Methodology: an interdisciplinary integrated approach**

From the literature basing on a compilation of fragmented definitions, the section puts forward a synthetic description of key terminologies used, in order to facilitate and improve the debates on this emerging field [4]. UIEP is defined by [1], as a model-based energy planning process. This is divided into the following four main phases: Phase I, preparation and preliminary analysis; Phase II, detailed urban buildings energy modelling; Phase III, prioritization and decisional process and Phase IV, implementation and monitoring (**Figure 1**).

To address a complex issue of UIEP, which consists of many different planning phases involving multi-sectors and objectives, there is a need for an interdisciplinary approach [5]. In fact, the planning processes in urban energy problems may be not specifically innovative approach; however, its management by means of integrated, cross-sector, multi-criteria and multi-actor approaches is absolutely a novel approach to be resolved [4]. In this vein, many cities should struggle to develop innovative methods to successfully reinforce the collaboration among different research disciplines dealing with energy issues [6].

is fundamentally based on 'multi-methodology integration' defined by [1], in which parts of

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In structuring the UIEP, it is important to select different appropriate approaches and to choose them considering the decision context and the type of planning project. Furthermore, it is crucial to analyse how it is possible to implement the interaction among the different stakeholders. As a result, the developed MC-SDSS for UIEP in the built environment uses

• Spatial database, which constitutes the GIS platform including all the relative information and data and enables the use of analytical process and outcomes such as the maps, graphs

different methodologies are combined (e.g. statistical, engineering, focus groups, etc.).

techniques at the crossroads of three domains (see **Figure 2**):

**Figure 2.** Schematic overview of the three main components of this research.

**Figure 1.** Urban integrated energy planning (UIEP) phases, adopted from [1].

and tables

On one hand, considering the existing research gaps and methodological directions, this study follows an interdisciplinary path. Both technical (e.g. energy modeling) and societal (e.g. an active engagement of relevant actors and interest groups) elements help to perform a proper UIEP, especially from the stakeholders' perspective [7]. On the other hand, this study Multi-criteria Spatial Decision Support System for Urban Energy Planning: An Interdisciplinary… http://dx.doi.org/10.5772/intechopen.80883 81

**Figure 1.** Urban integrated energy planning (UIEP) phases, adopted from [1].

emissions from urban infrastructure and building stock towards low-carbon cities requires a supportive planning process. In this regard, the use of appropriate tools and methods in order to address complex interactions of urban integrated energy planning (UIEP) processes is needed [1]. However, there is still not an integrated method to meet the urban integrated energy planning (UIEP) purposes [2]. In the mentioned study, the necessary approaches, which are needed to create the future urban energy consumption paths for scenario analysis, are described. Particularly, the importance of using geographic information system (GIS) for calculating, managing, storing and visualizing data at the urban scale

The chapter discusses in detail the steps design of methodological approach of a new integrated multi-criteria spatial decision support system (MC-SDSS) to evaluate and visualize the results of different UIEP scenarios involving the relative stakeholders and decision-makers (DMs) from the early stage of planning [3]. This will help in defining and evaluating energysaving scenarios taking into account the participation of stakeholders in an interactive way. The meaning of integrating different tools and methods in this framework is due to their

The proposed methodology framework is explained in Section 2. Afterwards, Section 3 illustrates the first results of the methodology application. Finally, some concluding and limita-

From the literature basing on a compilation of fragmented definitions, the section puts forward a synthetic description of key terminologies used, in order to facilitate and improve the debates on this emerging field [4]. UIEP is defined by [1], as a model-based energy planning process. This is divided into the following four main phases: Phase I, preparation and preliminary analysis; Phase II, detailed urban buildings energy modelling; Phase III, prioritization and decisional process and Phase IV, implementation and monitoring

To address a complex issue of UIEP, which consists of many different planning phases involving multi-sectors and objectives, there is a need for an interdisciplinary approach [5]. In fact, the planning processes in urban energy problems may be not specifically innovative approach; however, its management by means of integrated, cross-sector, multi-criteria and multi-actor approaches is absolutely a novel approach to be resolved [4]. In this vein, many cities should struggle to develop innovative methods to successfully reinforce the collabora-

On one hand, considering the existing research gaps and methodological directions, this study follows an interdisciplinary path. Both technical (e.g. energy modeling) and societal (e.g. an active engagement of relevant actors and interest groups) elements help to perform a proper UIEP, especially from the stakeholders' perspective [7]. On the other hand, this study

complementarity in fulfilling various tasks in the UIEP process.

**2. Methodology: an interdisciplinary integrated approach**

tion among different research disciplines dealing with energy issues [6].

tion remarks of this study are given in Section 4.

is highlighted.

80 Sustainable Cities - Authenticity, Ambition and Dream

(**Figure 1**).

**Figure 2.** Schematic overview of the three main components of this research.

is fundamentally based on 'multi-methodology integration' defined by [1], in which parts of different methodologies are combined (e.g. statistical, engineering, focus groups, etc.).

In structuring the UIEP, it is important to select different appropriate approaches and to choose them considering the decision context and the type of planning project. Furthermore, it is crucial to analyse how it is possible to implement the interaction among the different stakeholders. As a result, the developed MC-SDSS for UIEP in the built environment uses techniques at the crossroads of three domains (see **Figure 2**):

• Spatial database, which constitutes the GIS platform including all the relative information and data and enables the use of analytical process and outcomes such as the maps, graphs and tables


The integration and combination of this technical know-how allow providing maps of energy, economic, environmental, social and technical indicators resulting from the evaluation of energy-saving scenarios. This provides a supportive tool for the urban actors in the participatory planning processes allowing several stakeholders with different backgrounds and interests to gather and discuss the issues of several urban and regional energy-saving scenarios [8]. In the following section, the integration of theoretical proposed framework and how it is supposed to be applied to the study practice are shown.

#### **2.1. Research framework of MC-SDSS for UIEP**

A new MC-SDSS, which is an interactive plug-in of ArcGIS 10.3 (www.arcgis.com) environment helps dynamically analyse the energy retrofitting scenarios based on the stakeholders' preferences over an urban scale. The methodological framework of this study consists of several phases involved in the framework of an integrated urban energy planning according to Mirakyan and De Guio [1]. Hence, it is helpful to break it down into the main elements that frame it to understand the research process steps employed in this study. To this end, in **Figure 3** a schematic flowchart of the methodological approaches is shown.

#### *2.1.1. Phase I: preparation and preliminary analysis*

Accordingly, the fieldwork should be started from the quantitative data collection to characterize the building stock and to create a supportive geodatabase. This phase (Phase I) is entitled 'preparation and preliminary analysis'. Phase I is the foundation of all processes and modeling approaches in the next Phases, II and III. Of course, the GIS database can be always updated, and more data can be joined into the framework. In this step, the information characterizes by georeferenced and non-georeferenced data. Therefore, the georeferencing procedure should be performed for those non-georeferenced ones in order to create a strong geospatial database. All the collected data have been then overlapped and integrated into the GIS platform. In this regard, each building polygon has been associated with its available and necessary information. The goal of this phase is to create a 2D-GIS-database platform for the city including the several factors, which may influence the building energy issues. The use of GIS was crucial since it offers the opportunity to characterize the building stocks and to visualize the spatial distribution of a large number of data through its location-based feature and its multiple layers representation.

#### *2.1.2. Phase II: detailed urban buildings energy modeling*

Consequently, Phase II intends to perform to model the energy consumption of building stock in a detailed way. First, a bottom-up statistical model has been developed to estimate

**Figure 3.** A schematic overview of the methodological approach.

Multi-criteria Spatial Decision Support System for Urban Energy Planning: An Interdisciplinary…

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83

Multi-criteria Spatial Decision Support System for Urban Energy Planning: An Interdisciplinary… http://dx.doi.org/10.5772/intechopen.80883 83

**Figure 3.** A schematic overview of the methodological approach.

• Spatial building energy modeling, which develops a bottom-up modeling to evaluate the current and future energy consumption at the city scale concluding a sufficient level of detail • Spatial decision support system, which is the fact that the decision-makers (DMs) can express and exert their preferences with respect to multiple evaluation criteria and/or alternatives and, consequently, get back feedback in a real time to increase the DMs trust in the outcomes

The integration and combination of this technical know-how allow providing maps of energy, economic, environmental, social and technical indicators resulting from the evaluation of energy-saving scenarios. This provides a supportive tool for the urban actors in the participatory planning processes allowing several stakeholders with different backgrounds and interests to gather and discuss the issues of several urban and regional energy-saving scenarios [8]. In the following section, the integration of theoretical proposed framework and how it is

A new MC-SDSS, which is an interactive plug-in of ArcGIS 10.3 (www.arcgis.com) environment helps dynamically analyse the energy retrofitting scenarios based on the stakeholders' preferences over an urban scale. The methodological framework of this study consists of several phases involved in the framework of an integrated urban energy planning according to Mirakyan and De Guio [1]. Hence, it is helpful to break it down into the main elements that frame it to understand the research process steps employed in this study. To this end, in

Accordingly, the fieldwork should be started from the quantitative data collection to characterize the building stock and to create a supportive geodatabase. This phase (Phase I) is entitled 'preparation and preliminary analysis'. Phase I is the foundation of all processes and modeling approaches in the next Phases, II and III. Of course, the GIS database can be always updated, and more data can be joined into the framework. In this step, the information characterizes by georeferenced and non-georeferenced data. Therefore, the georeferencing procedure should be performed for those non-georeferenced ones in order to create a strong geospatial database. All the collected data have been then overlapped and integrated into the GIS platform. In this regard, each building polygon has been associated with its available and necessary information. The goal of this phase is to create a 2D-GIS-database platform for the city including the several factors, which may influence the building energy issues. The use of GIS was crucial since it offers the opportunity to characterize the building stocks and to visualize the spatial distribution of a large number of data through its location-based feature and its multiple layers representation.

Consequently, Phase II intends to perform to model the energy consumption of building stock in a detailed way. First, a bottom-up statistical model has been developed to estimate

**Figure 3** a schematic flowchart of the methodological approaches is shown.

supposed to be applied to the study practice are shown.

**2.1. Research framework of MC-SDSS for UIEP**

82 Sustainable Cities - Authenticity, Ambition and Dream

*2.1.1. Phase I: preparation and preliminary analysis*

*2.1.2. Phase II: detailed urban buildings energy modeling*

the heating energy consumption for the built environment space heating at the city level. This model is based on the integration of statistical analysis with 2D-GIS to map the current energy consumption of the city [9]. The novelty of the proposed statistical model lies on its simplicity and applicability and the high level of their robustness in the literature. However, these statistical methods rely strongly upon monitored real data. It should be noted that fortunately, the author succeeded to collect a sample of information of energy billings as a data source for modeling purpose and for analysing the link between energy consumption and a wide range of different variables. Moreover, the statistical models are also able to take into account socio-economic effects in the equations [10]. They perform reliable consumption information about the present condition of buildings and for the calibration process of engineering-based models. However, due to the strong dependency of statistical methods on available historical consumption data, these methods are limited to predict the impact of innovative technology options and energy-saving potential after applying renewal solutions.

and questionnaires. Particularly, the use of focus groups by stakeholders in this study has the following implication: it reflects the 'mixed methodology' choice for Phase III with the use of qualitative (semi-structured focus groups, questionnaires, playing card) and quantitative

Multi-criteria Spatial Decision Support System for Urban Energy Planning: An Interdisciplinary…

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85

Regarding the definition of evaluation criteria, several methods exist in the literature [13]. The present study proposes a participative approach in order to define the evaluation criteria through the multi-stakeholders workshop including semi-structured focus group organized involving relevant stakeholders [14]. The definition of evaluation criteria side by side with the real local stakeholders leads to have trustable results that grantee the robustness of planning process. A vast number of available MCDA approaches make it necessary to carefully select the most appropriate method for each specific decision context. In this framework, the 'playing cards' is chosen [15] due to some reasons. First, it is a simple and intuitive method and easy to be understood, even by non-experts in the field of decision processes [16]. Second, they can help DMs in managing values that cannot be quantified without difficulty, involving qualitative judgments. Finally, the technical parameters involved in the playing card methodology can be interpreted easily, allowing a simplification of the problem. Lombardi et al. [14]

Subsequently, each of the selected criteria from the workshop has been analysed and assessed to be implemented in a new MC-SDSS tool (see Section 3). Two main instruments, Interactive Impact Assessment (IIA) and Suitability Analysis (SA), are modeled and adapted in order to develop a new MC-SDSS. Several dynamic attributes and indicators were modeled and coded using CommunityViz as a planning support system (PSS) tool [17]. This PSS tool is selected as a base for further modeling processes due to its several strengths. It helps in analysing and understanding the potential alternatives and their impacts through visual investigation and scenario analysis. Moreover, this tool is interactive and provides dynamic feedbacks on changing the assumptions and viewing the influences of changes on the future scenarios on the fly. Furthermore, it engages stakeholders in participative and collaborative decision-making processes through visualization in real-time approach. All the above strengths lead to stronger consensus and better decisions in resolving complex problems. The detailed methodological procedure developed for supporting this phase of research is

This methodological approach provides a significant innovative progress in the research field that is developing an interactive plug-in tool for UIEP in the GIS environment. In this regard, finally, the second workshop is organized to test the usability and validate the tool from the real stakeholder point of view. For evaluation purposes, this workshop is included in two semi-structured focus groups. This step attempts to understand the weaknesses and strengths of the mentioned framework. In this workshop, the questionnaires also were designed for analysing the stakeholders' feedbacks about the developed tool. Within the use of this GIS extension, public administrative users, such as urban energy planners, policymakers and built environment stakeholders, can plan, design and manage low-carbon cities. This plug-in will provide the stakeholders with the ability to visualize interactively and explore a range of

(building stock energy data, costs, etc.) data collection and analysis methods.

describe the main the procedure of 'playing cards' method and its results.

under progress of publication.

possible future-saving scenarios.

In counterpart, engineering methods are very detailed models based on traditional thermodynamic relationships and heat transfer calculations [10]. Although the historical data can be used for making a comparison against measured consumption data, these methods can assess energy consumption without any historical information. However, the engineering modeling approaches require a high quantity of information about building structure and parametric input to calculate the energy consumption of a set of reference buildings of the stock based on a numerical model. In this, 3D city models can significantly help [11]. One of the main benefits of engineering-based methods is their ability to predict energy-saving quantity for buildings after renovating solutions application [12]. In this phase, the methodology proposes to simulate the energy consumption of urban areas after applying retrofitting actions. Although the engineering methods are able to predict future conditions, simulating whole cities using energy demand software can be very extensive in terms of computer resources and data collection. The reduction of these time-consuming methods thus still remains to be resolved. Therefore, a new methodology, using city archetypes, is proposed to simulate the energy consumption of urban areas including urban energy planning scenarios. The objective of this part is to present an innovative solution for the simulation of the energy demand of cities by using a simplified 3D-GIS model, designed as a function of the city urban characteristics.

In fact, the methodology framework combines both the statistical and engineering approaches to obtain a more robust prediction of the urban energy consumption. The framework is performed in order to reduce time-consuming processes of energy demand simulation and assessment and for designing urban energy-saving scenarios. A spatial distribution of urban building energy consumption in 2D/3D visualization provides spatial decision support system (SDSS) tool in order to identify where the energy consumption is mostly concentrated to make the better decisions.

#### *2.1.3. Phase III: prioritization and decisional process*

Phase III of the study follows 'a mixed methodology' that combines qualitative and quantitative approaches [7]. Qualitative research refers to semi-structured focus groups formed in which the qualitative data such as stakeholders' opinion are collected through discussions and questionnaires. Particularly, the use of focus groups by stakeholders in this study has the following implication: it reflects the 'mixed methodology' choice for Phase III with the use of qualitative (semi-structured focus groups, questionnaires, playing card) and quantitative (building stock energy data, costs, etc.) data collection and analysis methods.

the heating energy consumption for the built environment space heating at the city level. This model is based on the integration of statistical analysis with 2D-GIS to map the current energy consumption of the city [9]. The novelty of the proposed statistical model lies on its simplicity and applicability and the high level of their robustness in the literature. However, these statistical methods rely strongly upon monitored real data. It should be noted that fortunately, the author succeeded to collect a sample of information of energy billings as a data source for modeling purpose and for analysing the link between energy consumption and a wide range of different variables. Moreover, the statistical models are also able to take into account socio-economic effects in the equations [10]. They perform reliable consumption information about the present condition of buildings and for the calibration process of engineering-based models. However, due to the strong dependency of statistical methods on available historical consumption data, these methods are limited to predict the impact of innovative technology

In counterpart, engineering methods are very detailed models based on traditional thermodynamic relationships and heat transfer calculations [10]. Although the historical data can be used for making a comparison against measured consumption data, these methods can assess energy consumption without any historical information. However, the engineering modeling approaches require a high quantity of information about building structure and parametric input to calculate the energy consumption of a set of reference buildings of the stock based on a numerical model. In this, 3D city models can significantly help [11]. One of the main benefits of engineering-based methods is their ability to predict energy-saving quantity for buildings after renovating solutions application [12]. In this phase, the methodology proposes to simulate the energy consumption of urban areas after applying retrofitting actions. Although the engineering methods are able to predict future conditions, simulating whole cities using energy demand software can be very extensive in terms of computer resources and data collection. The reduction of these time-consuming methods thus still remains to be resolved. Therefore, a new methodology, using city archetypes, is proposed to simulate the energy consumption of urban areas including urban energy planning scenarios. The objective of this part is to present an innovative solution for the simulation of the energy demand of cities by using a simplified 3D-GIS model, designed as a function of the city urban characteristics.

In fact, the methodology framework combines both the statistical and engineering approaches to obtain a more robust prediction of the urban energy consumption. The framework is performed in order to reduce time-consuming processes of energy demand simulation and assessment and for designing urban energy-saving scenarios. A spatial distribution of urban building energy consumption in 2D/3D visualization provides spatial decision support system (SDSS) tool in order to identify where the energy consumption is mostly concentrated to

Phase III of the study follows 'a mixed methodology' that combines qualitative and quantitative approaches [7]. Qualitative research refers to semi-structured focus groups formed in which the qualitative data such as stakeholders' opinion are collected through discussions

options and energy-saving potential after applying renewal solutions.

84 Sustainable Cities - Authenticity, Ambition and Dream

make the better decisions.

*2.1.3. Phase III: prioritization and decisional process*

Regarding the definition of evaluation criteria, several methods exist in the literature [13]. The present study proposes a participative approach in order to define the evaluation criteria through the multi-stakeholders workshop including semi-structured focus group organized involving relevant stakeholders [14]. The definition of evaluation criteria side by side with the real local stakeholders leads to have trustable results that grantee the robustness of planning process. A vast number of available MCDA approaches make it necessary to carefully select the most appropriate method for each specific decision context. In this framework, the 'playing cards' is chosen [15] due to some reasons. First, it is a simple and intuitive method and easy to be understood, even by non-experts in the field of decision processes [16]. Second, they can help DMs in managing values that cannot be quantified without difficulty, involving qualitative judgments. Finally, the technical parameters involved in the playing card methodology can be interpreted easily, allowing a simplification of the problem. Lombardi et al. [14] describe the main the procedure of 'playing cards' method and its results.

Subsequently, each of the selected criteria from the workshop has been analysed and assessed to be implemented in a new MC-SDSS tool (see Section 3). Two main instruments, Interactive Impact Assessment (IIA) and Suitability Analysis (SA), are modeled and adapted in order to develop a new MC-SDSS. Several dynamic attributes and indicators were modeled and coded using CommunityViz as a planning support system (PSS) tool [17]. This PSS tool is selected as a base for further modeling processes due to its several strengths. It helps in analysing and understanding the potential alternatives and their impacts through visual investigation and scenario analysis. Moreover, this tool is interactive and provides dynamic feedbacks on changing the assumptions and viewing the influences of changes on the future scenarios on the fly. Furthermore, it engages stakeholders in participative and collaborative decision-making processes through visualization in real-time approach. All the above strengths lead to stronger consensus and better decisions in resolving complex problems. The detailed methodological procedure developed for supporting this phase of research is under progress of publication.

This methodological approach provides a significant innovative progress in the research field that is developing an interactive plug-in tool for UIEP in the GIS environment. In this regard, finally, the second workshop is organized to test the usability and validate the tool from the real stakeholder point of view. For evaluation purposes, this workshop is included in two semi-structured focus groups. This step attempts to understand the weaknesses and strengths of the mentioned framework. In this workshop, the questionnaires also were designed for analysing the stakeholders' feedbacks about the developed tool. Within the use of this GIS extension, public administrative users, such as urban energy planners, policymakers and built environment stakeholders, can plan, design and manage low-carbon cities. This plug-in will provide the stakeholders with the ability to visualize interactively and explore a range of possible future-saving scenarios.
