This document summarizes a PhD thesis that defined a methodology for integrating building energy efficiency into urban policy decision-making using simple, available data. The methodology establishes a framework using a GIS tool and archetypes to model building energy needs. A case study of a district in Cagliari, Italy demonstrated the methodology by defining building typologies, estimating current energy needs, and identifying areas for technological improvements. Further research is needed to test and improve the methodology and tools with experts to help design effective energy policies.

1. “Defining a Spatial Decision Support
System for integrating building
energy efficiency in urban policy
decision-making”
PhD. Stefano Pili
Prof. Emanuela Abis
Faculty of Architecture , University of Cagliari

2. Target: defining a methodology, based on simple and available data, for integrating
buildings energy efficiency in urban policies*
Theoretical context
Methodology framework
Case study
Conclusions and further research
*Stefano Pili PhD thesis on Land engineering (year 2012) with supervisor Prof. Emanuela Abis

3. Theoretical context 2
Buildings facilities account for 33% (2003) of total Italian energy consumption.
About the 93% of the Italian building stock as built without energy regulations (before 1991)
RENOVATE ITALIAN BUILDING STOCK!
Lack of building stock energy consumption data:
Building shape, materials, building technical devices
Available technological solutions:
Technical and economic bonds
Regulations bonds
Cultural bonds
Ill structured problem*:
Iterative approach
Consensual not optimal solution
*SIMON 1960, DENSHAM 1991, TURBAN 2005

4. Theoretical context : research target 3
Shared knowledge
Main consensus Shared values
STAKEOLDERS issues Simple representations
Decision Makers Policies to support
(public RES and energy Questions
administrators) savings What are the characteristics
of the building stock energy
Building sector Environmental consumption?
companies protection
Private owners Building renovation
and urban quality Decision
Interested observers
Landscape protection Support
Random observers System
Economic and social
development
…….
New regulations Specific projects
and policies

5. Theoretical context: shared values 4
Building Energy efficiency UM
UNI 11300 parameters HVAC energy need kWh/ sm year.
Envelope heat loss kWh/ sm year
Ventilation heat loss kWh/ sm year
Solar heat gain kWh/ sm year
Internal heat gain kWh/ sm year
EPC parameters* global plant efficiency %
Heating Primary Energy need kWh/ sm year
DHW Primary Energy need kWh/ sm year
*Legge n°10/1991
Fuel consumption kWh year
D. Lgs. 192/2005 Operative cost Euro year
D. Lgs. 311/2006
D.P.R. 2 Aprile 2009 n° 59 CO2 emission kgCO2/ sm years
D.M. 26 Giugno 2009 EPC Energy Label
standard calculation (UNI 11300 1-2-3 and ISO EN 13790:2008)
No human factor
For tower buildings, EPC calculation could be For the existing building is allow to use the
done setting the thermal zone equal to the list of building structures in to 11300-1
building volume

6. Methodology : decision making process 5
hypothetic
scenarios

7. Methodology : GIS tool framework 6

8. Methodology : discussion results 7
Standard stairwell surface
Simplified external context (shadows, solar gain)
It use standard building structures and materials
from the UNI 11300-1 list
strong conservative calculation (Baggio 2008)
(DOCET user handbook)
Needh = (Qhve+ Qht) - Futh *(Qhint+ Qhsol)
(Tool-DOCET)/
DOCET
Qht=Perdite dall’involucro [kWh] -1-8,5%
Qhve = perdite per ventilazione [kWh] <+/-0,5%
Qhint = guadagni interni [kWh] <+/-0,5%
Qhsol = guadagni solari [kWh] +26,5-30%
Futh = fattore dinamico F(Costante tempo [h] +20%-30%
Needh = Fabbisogno netto [kWh] -18-24%
Superficie utile [mq] <+/-0,5%

9. Case Study: S. Benedetto di Cagliari district 8

10. Case study: area 9

11. Case study: area 10

12. Case study: typology definition 11
Archetype Date of construction wall insulation Glazing Ratio
Small building, Rendered Wall, 60-
1Before 1919 no 17%-19%
70cm thick
Small building, Rendered Wall, 60-
2Between 1919 and 1945 no 17%-19%
70cm thick
3Between 1919 and 1945 Rendered Wall, 60-70cm thick no 14%-17%
Rendered Wall and Concrete, 60-
4Between 1946 and 1961 no 18%-19%
70cm thick,
5Between 1962 and 1971 Cavity Wall, 25-35cm thick no 19%-23%
6Between 1972 and 1981 Cavity Wall, 25-35cm thick insulation ? 19%-23%
7Between 1982 and 1991 Cavity Wall, 25-30cm thick insulation (3 cm) 20%-23%
8Between 1991 and 2005 Cavity Wall, 25-30cm thick insulation (3-5cm) 21%-25%
9After 2005 different tipe25-30cm thick insulation (5-7cm) 21%-25%
10Renovated building Rendered Wall, 60-70cm thick insulation (3-5cm) 17%-19%
without energy regulation
Without or weak energy regulation Energy regulation

13. Case study: typology definition 12
After 1991 (3,7%)
0,8%
1,5% 0,8% Archetype
1,4% 3,0% 1, before 1919
3,6%
2,2% 2, 1919_45
After 1991 (7,7%)
3, 1919_45
19,1%
4, 1946_61
4,1% 1,8% 2,7%
2,7% 1,8% 21,6% 5, 1962_71
1,4%
6, 1972_81
15,8%
7, 1982_91
15,8% 8, 1992_2005
45,9% 9, 1992_2005R
18,1% 10, after_2005
Available surface
35,7%
n° of buildings

14. Case study: tipology 13
Plant:HP between 1919 and 1945 Plants: HP between 1919 and 1945
Rendered stone wall Rendered stone wall
Plant: HP between 1946 and 1961 Plant :centralized boiler between 1962 and1971
Rendered stone wall and Concrete Concrete and cavity wall

15. Case study: Actual state 14
Need
[kWh/ sm year]
5,8 – 35,0
35,0 – 50,0
50,0 – 65,0
65,0 – 80,0
80,0 – 100,0
100,0 – 160,0
Vista 3D della mappa del fabbisogno

16. Conclusions and further research 15
further researchs
Assess the potential of the technological
achieve objectives improvements defining standard metodologies
The methodology could help Proof the methodology simulating a real decision
process with experts: more detailed archetype
in building energy layer definition, more detailed technological improvements
themes in order to design and accurate policy design.
urban policies using Test more the Tools, to improve efficiency and
available Italian data precision
Define methods to provide the base data: survey,
matching existing data base, City Sensing, LIDAR, eco
feed back …
And more
THANKS for your attention
Contacts: stefano.pili@unica.it emabis@unica.it