In this work is intended to quantify the change in energy consumption through to modify the parameters that characterize the urban context. In this regard, it is necessary to identify the parameters that influence the changes in energy consumption. After reading the publications from the mid of the 90s to present, the most important parameters in this case have been defined as:

BCR building coverage ratio [m2/m2]

BD building density [m3/m2]

BH building height [m]

FAR floor area ratio [-]

H/W aspect ratio [-]

SVR sky view factor [-]

MOS streets orientation [-]

A albedo [-]

So the equation of Urban context consumption can be defined as:

kWh [Urban context] =/( BCR, BD, H/W, BH, SVF, MOS, A)

After defining these parameters and calculating them through ArcGIS software, it is possible to identify a relationship between some of them such as Global Urban Morphology and Solar Factors, to define the function of the urban context. In order to verify this hypothesis, it is necessary to go to find an energy analysis software that could be used in urban scale simulation. The most relevant programs to this work are UMI (MIT); Energy Proforma (MIT); CitySim (Kaemco) which are currently being developed in various universities and companies, as there is not yet a clear methodology for calculating the energy consumption at the urban scale. After several observations, it was noted that the most adequate program is CitySim, the software produced by Kaemco (Swiss company, which provides consultancy in the energy and urban physical sector, in collaboration with Ecole Polytechnique Fédérale de Lausanne).

Since in Turin in the major part of the residential building, there are not present the cooling systems so has been chosen three public building that their energy consumption data were available. Two of these buildings are similar in terms of urban context and volume. Both of them are located in the zones which are common in Turin. Another one is located in a different zone, in the vicinity of a skyscraper and in term of volume and construction is completely vary of two ones. The data of different sections are taken from ArcGIS program. In addition, it was needed the annual energy consumption of each building and also the climate files, which edited according to the monthly average temperatures obtained by ARPA (The Regional Agency for the Protection of the Environment). All data are related to the year of 2015.

After collecting all the necessary data, by changing the urban variables (BCR, H/W, BH, MOS, Albedo) through CitySim program, have been reached new energy consumptions of sample buildings. Therefore it is possible to identify how energy consumptions change through changing the urban form of sample area. This work does not consider to propose a definitive solution in urban planning because the result will change according to the various case study with different context features and climate characters, but could propose a new basis for understanding this phenomenon without claiming to have a decisive response to the new urban design. In fact, the most important reasons of these studies are the need to decrease the energy consumption of our cities, which in recent years has led designers not only in the scale of buildings but also in the urban scale.

2.1.Studies about energy consumption and urban form

2.2.Models for Analysis of Energy Demand

2.3.Tools for evaluating the energy sustainability in urban scale

3.Main Factors that influence the energy consumption of a district

3.2.Urban Morphology and Solar Factors

4.CitySim

5.Case study

5.1.The Province of Turin

5.1.1.Weather data

5.1.2.Calculate the Characters of census sections using GIS

5.1.2.1.Procedures for calculation of the Albedo

5.2.Sample Buildings

5.2.1.Employment center

5.2.2.Mechanical means Center

5.2.3.Province of Turin

5.3.Compare the real consumptions with CitySim results

6.Calculate the correlation between Urban Morphology factors and Energy Consumption of buildings

6.1.BCR Modification

6.2.H/Hm Modification

6.3.Hm/Wm Modification

6.4.MOS Modification

6.5.Albedo Modification :

7.Analysis the results

7.1.Simulation I

7.1.1.Calculate the consumption as a function of the BCR factor

7.1.2.Calculate the consumption as a function of the H/Hm factor

7.1.3.Calculate the consumption as a function of the Hm/Wm factor

7.1.4.Calculate the consumption as a function of the MOS factor

7.1.5.Calculate the consumption as a function of the Albedo factor

7.1.6.Calculate the consumption as a function of all the urban variables

7.2.Simulation II

7.2.1.Calculate the consumption as a function of the BCRfactor

7.2.2.Calculate the consumption as a function of the H/Hm factor

7.2.3.Calculate the consumption as a function of the Hm/Wm factor

7.2.4.Calculate the consumption as a function of the MOS factor

7.2.5.Calculate the consumption as a function of the Albedo factor

7.2.6.Calculate the consumption as a function of all the urban variables

Conclusion

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