desogus, mura, pisano & ricciu - input2012

Tools and methods for sustainability in planningprocesses

Planning energy saving measures for existing

buildings through linear problems solving

Giuseppe Desogus, Salvatore Mura, Gian Luca Pisano, Roberto Ricciu

Department of Civil and Environmental Engineering and Architecture,

University of Cagliari


Research’s aim:

The application of a linear problem solving technique on existing dwellings energy

savings planning .

Desogus G. Mura S. Pisano G.L. Ricciu R. - Planning energy saving measures for existing buildings through linear problems solving


1.000.000

2.000.000

3.000.000

4.000.000

5.000.000

10.000.000

15.000.000

Houses by geographical area

Regional

National


1951 1961 1971 1981 1991 2001

0 0

NW Italy NE Italy Middle Italy South Italy Insular Italy Italy

years

Data from 14th Population and House Census, National Institute of Statistics (ISTAT)



Data from Energy and Environment Reports, ENEA


At the moment the most widespread solutions to improve existing buildings

energy performance in Italy are:

•Partial or complete renovation of HVAC system;

•Horizontal structures (roofs included) insulation;•Horizontal structures (roofs included) insulation;

•External wall insulation;

•External windows or doors replacement;

•Thermal or PV solar panels installation.



The choice of which energy saving measures best fit to an existing building

depends at least by three main factors:

•Building features (age, dimensions, construction technologies, HVAC systems

and use);

•Local climatic conditions;

•Market costs of energy saving measures.



Planning the best mix of energy saving works for a known context always requires

a building type classification.

Dwellings should be grouped by:

•Age;

•Dimensions;

•construction technologies;

•HVAC systems.

For a small-scale context, local climate classification can be neglected.



N = building types identified

M = energy saving works

The problem is finding the number of each work for each type of building that

gives the best results in terms of energy saving and invested capital.

xi,j unknown quantities are N·M

Mathematical expressions of energy savings and costs are linear equations, thus,

if appropriate constraints are identified, linear programming techniques can be

used.

Among them the Simplex Algorithm is adopted in this study.



A case study: optimizing national policy of energy saving subsidies (the so-called

“55% tax-reduction” )

Decision model inputs:

•number of submitted applications by geographical area;

•annual average energy saving by geographical area;•annual average energy saving by geographical area;

•average cost for works by geographical area;

•annual cost per MWh saved

All data extracted from ENEA “55%” Reports.



A case study: optimizing national policy of energy saving subsidies (the so-called

“55% tax-reduction” )

25 (5x5)decision variables:

•Number of wall energy efficiency works;

•Number of energy efficiency works for horizontal element;•Number of energy efficiency works for horizontal element;

•Number of windows energy efficiency works;

•Number of plant system replacing works;

•Number of solar panels installations.

Related to geographical areas:

•North-West Italy;

•North-East Italy;

•Middle Italy;

•Southern Italy;

•Insular Italy.



Macro area No. Works Cost € Energy saving GWh

NW Italy

Horizontal element inprovement works 204 146 3 882 657 878 3 774

Wall improvement works 1 844 099 17 729 628 811 36 702

Window improvement works 4 014 705 15 566 265 880 11 899

NE Italy

Horizontal element inprovement works 4 976 359 66 446 335 912 91 999


Best scenario for

energy efficiency

measure subsides by

category and

geographical area.


Heating system replacing 2 322 649 13 316 791 909 22 797

Middle Italy

Horizontal element inprovement works 2 773 439 38 476 681 943 51 273


Window improvement works 3 955 181 14 809 977 495 11 723

Solar panels installations 4 728 293 12 977 036 553 36 442

Heating system replacing 4 954 312 21 185 257 401 48 627

South Italy

Heating system replacing 690 658 1 975 523 610 6 779

Insular Italy

Solar panels installations 3 214 236 6 220 993 066 24 773

Total 39 799 440 264 316 356 066 468 617


50%

60%

70%

80%

90%

100%

Economic performance

% energ

y efficiency


Costs reduction - Proposed decision model trend compared to the present one.

10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%

0%

10%

20%

30%

40%

50%

Present trend Proposed model trend

% works

% energ

y efficiency


50%

60%

70%

80%

90%

100%

Energy performance

% energy efficiency


10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%

0%

10%

20%

30%

40%

50%

Present trend Proposed model trend

% works

% energy efficiency

Energy use reduction - Proposed decision model trend compared to the present one.


Conclusions:

•Energy retrofit works on existing buildings can be very expensive and invasive. In

most case only few solutions can be adopted. Among them the most profitable

should be identified;

•The actual energy efficiency policies are only private initiative based;

•Using suitable decision instruments in planning can lead to consistent energy

and public capital savings.


desogus, mura, pisano & ricciu - input2012

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