estimating the impact of occupant behaviours on ...

Energy for Sustainability 2015 Sustainable Cities: Designing for People and the Planet Coimbra, 14-15 May, 2015

ESTIMATING THE IMPACT OF OCCUPANT BEHAVIOURS ON ENERGY CONSUMPTION OF SMALL COMMERCIAL AND SERVICES BUILDINGS Ana Reis1 , Marta Lopes2,3 *, and Nelson Martins4 1: University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal, Portugal e-mail: [email protected] 2: Dept. of Environment, Polytechnic Institute of Coimbra - ESAC, 3045-601 Coimbra, Portugal e-mail: [email protected] 3: INESC Coimbra, Rua Antero de Quental 199, 3000-033 Coimbra, Portugal 4: Dept. of Mechanical Engineering, University of Aveiro, Campus de Santiago, 3810-193 Aveiro, e-mail: [email protected]

Keywords: Energy efficiency, energy consumption, energy savings, occupant behaviour, dynamic buildings simulation, services buildings, offices Abstract Energy consumption in buildings is influenced by the constructive characteristics, energy systems operation and occupants’ behaviour. Occupants’ behaviour is one of the most significant uncertainties when predicting energy consumption of buildings using computational simulation tools due to its stochastic nature. This work proposes a quantitative methodology, based on dynamic simulation techniques, to quantify energy savings potential of occupants’ behaviours in small offices buildings. Occupants’ behaviour interaction with buildings was restricted to HVAC systems’ control, lighting operation and office equipment use. Three occupant behaviour profiles were defined (inefficient, reference and efficient). Results quantitatively demonstrated the significant impact of occupants’ behaviour on energy consumption of offices buildings. Depending on the occupants’ performance, savings of 38% or increases of 18% in the total buildings energy consumption were achieved. Energy efficiency interventions and policies should target behavioural dimensions in offices buildings since they play a significant role in the promotion of energy efficiency.

Ana Reis, Marta Lopes and Nelson Martins

1. INTRODUCTION The buildings sector is currently one of the largest energy consumers, with a share of 41% of the final energy consumption in Europe [1]. Energy demand in services is projected to be 26% higher in 2030 relatively to 2005 [2] which points to the need for investigating the use of energy in this sector and proposing energy efficiency measures. Energy use in offices buildings is closely linked to constructive characteristics, operational and zone utilisation, and the occupants’ behaviour. In fact, the occupants’ behaviour is referred to be one of the most important determinants of the energy consumption of buildings, affecting it in the same proportion as the equipment [3-9]. Occupants’ control over heating and ventilation systems have been increasing due to the design of systems, thus influencing the buildings energy consumption and the overall energy efficiency performance [10-12]. This is particularly relevant in small offices where energy management is not an explicit operational function. In these contexts, a significant amount of energy is often utilised to ensure indoor comfortable environmental conditions, which are controlled by actions, preferences, and attitudes of occupants [5, 13]. Accordingly, the occupants’ behaviour carry a considerable untapped energy saving potential, in many situations greater than the technological solutions [6]. This role is expected to gain increasingly relevance since the marginal savings due to energy efficiency standards of both equipment and buildings is becoming reduced [14-16]. However, energy behaviours are referred to be the most complex processes within buildings being influenced by environmental, contextual, physiological, social and psychological factors [17, 18]. Understanding the determinants of buildings energy consumption and in particular the behavioural factors and their intrinsic complexity, is therefore paramount to improve energy efficiency in offices buildings [19]. The present work proposes a quantitative methodology based on dynamic simulation techniques to quantify energy savings potential of occupants’ behaviours in small offices buildings, tackling the uncertainty associated with occupants’ behaviours. Emphasis is given to the occupants’ actions impacts when adapting their environmental comfort adjusting artificial lighting, HVAC systems and general equipment use. 2. BUILDINGS ENERGY SIMULATION AND OCCUPANTS’ BEHAVIOUR Energy simulations have been used to determine occupants’ behaviour impact on buildings energy consumption [20]. Typically, occupants’ actions (operation of lights, blinds and windows) have been modelled based on fixed predefined schedules or rules [7]. For example, different assumptions on occupants’ window-opening behaviours are available in the literature, based on occupancy and environmental conditions (e.g., temperature, humidity, wind, airflow rates) [7, 9, 12, 13]. Since these assumptions do not necessarily represent the occupants’ actual behaviour, recent research has been developing algorithms to simulate occupants’ interaction with building control systems which are based on field investigations [12, 21]. However, quantifying the total influence of occupants’ behaviour and activities through simulations is difficult due to behaviours diversity, complexity and randomness. Variation in 2


a single parameter can significantly impact model results (up to 40% over a run using “all average” values), and, generally results in increased, rather than decreased, predicted energy use [22]. Existing relationships and dependencies between parameters (e.g., loads, schedules) may further contribute to variability in energy modelling results [22]. Over the years several studies have been trying to reduce this uncertainty by focusing on occupants’ behaviour relation with indoor and outdoor environmental parameters, but broader (geographically and culturally) and long-term studies are still referred to be needed to produce more realistic models of occupants actions in buildings [17, 23, 24]. In summary, most simulations do not deal with the stochastic nature of occupant’s energy behaviour. Often, a deterministic description of occupant’s activities is made to calculate internal gains due to occupancy, lighting and equipment use. Since the behaviour of occupants can have large effects on the building energy consumption, this approach results in huge gaps between real and predicted energy performance of buildings [13, 25]. By consequence, reducing the uncertainty associated with the occupants’ behaviour by better detailing the behavioural parameters is paramount to improve buildings energy simulations accuracy [17]. 3. EVALUATING OCCUPANTS’ BEHAVIOUR IMPACTS USING BUILDINGS SIMULATION 3.1 Methodology The methodology is based on the use of buildings dynamic simulation to quantify energy performance indicators. Simulations aim at quantifying the impact of occupants’ behaviour in energy consumption of small offices buildings during the period of one year. Characteristics associated with the building envelope, the total load and the weather conditions are considered constant and representative of Portuguese reality. Three representative Portuguese cities with different climate conditions were selected: Oporto (cold atlantic climate); Lisbon (mild atlantic climate); and Faro (Mediterranean warmer climate). The impact of occupants’ actions in energy consumption is assessed reproducing occupants’ behaviours on the considered operation profiles for HVAC systems, artificial lighting and office equipment (Figure 1).

a)

b)

c)

Figure 1. Schedule reference of: a) occupancy, b) lighting, and c) equipment [26]

3


Three typical behavioural profiles were established as a function of a realistic set of parameters reflecting different levels of occupants’ performance: the standard occupant, the efficient occupant, and the inefficient occupant. 3.2 Results 3.2.1 Energy consumption breakdown Annual energy consumption ranged between 109 and 116 kWh/(m2.year) depending on the location, which complies with the maximum value of 186 kWh/(m2.year) established by the Portuguese energy regulation to offices [26]. Annual energy consumption breakdown of the reference scenario is depicted at Figure 2. It was found offices from Lisbon hold the highest energy consumption (about 11.6 MWh/year) during the reference scenario, 7% higher than the lowest, at Oporto, with 10.9 MWh/year). Equipment and lighting have the largest share of energy consumption, accounting for a share of 65 to 70%, which is independent from the location. However, their relative importance varies due to the change of weatherisation needs. Almost one third (30-35%) of energy consumption is originated by the HVAC system. The difference between locations are mostly related with the scattering between heat and cooling, the later gaining importance as the test cell location is moving south, i.e., to warmer climate conditions, being four times more important in Faro than in Oporto. FARO

11,3%

LISBON

22,2%

17,5%

PORTO

18,2%

25,7%

0%

25,5%

20% Heating

24,7%

5,7%

26,3%

40% Cooling

41,0%

10,899.8 kWh/year

39,7%

11,619.7 kWh/year

42,3%

60% Lighting

80%

11,245.2 kWh/year 100%

Equipment

Figure 2. Annual energy consumption of the reference scenario: breakdown by end-use and location

3.2.2 Influence of occupants’ behaviour on energy consumption Considering a reference operation profile, an energy consumption deviation range from - 38% to +18% may be expected as a result of occupants’ behaviour alternating from efficient to inefficient practices (Figure 3). Efficient occupants’ behaviours can prevent the emission of 20 kg CO2/ m2 per year. Potential direct financial savings associated to efficient occupants’ energy behaviour may be estimated as 8.5 €/m2 per year, while inefficient behaviours originate an increase of 4.0 €/m2 per year in the energy bill (assuming an energy cost of 0.2 €/kWh). Energy savings potential associated to artificial lighting can be as high as 22.3% (-5 €/m2 per year) and equipment efficient use may lead to 12.5% savings (-3 €/m2 per year). Inefficient behaviours related to acclimatisation may result in a potential increase of the yearly energy consumption of 14.5% (+3 €/m2 per year). 4


Efficient occupant

-38.3% -34.7% -33.0%

12.9% 14.4% 18.2%

20% Inefficient occupant

3.3% 3.5% 5.8%


2.3% 2.1% 3.0%


-50.0%

-40.0%

-30.0%

-20.0%

-10.0%

Faro

0.0%

10.0%

Lisbon

Oporto

20.0%

30.0%

Figure 3. Efficient and inefficient behaviours effect on the energy consumption

The influence of occupants’ behaviours on energy consumption is presented in detail at Table 1, Table 2, and Table 3. In Oporto (Table 1) if the office occupants forget turning off the HVAC system during 20% of the year it will contribute to increase the overall energy consumption by 14.5%. In contrast, there is a great potential for energy savings when office occupants make the most of natural lighting (-20.5% of the annual energy consumption) and when set up the energy saving plan of their computers (-10.5% of the annual energy consumption). In Lisbon (Table 2) inefficient occupants’ actions with the highest energy consumption impact (+13.1%) is associated with lighting (i.e., leaving lights switched on all day). Less efficient HVAC system operation may result in a considerable energy consumption increase of 11.3%. Efficient energy behaviours, such as turning off artificial lighting when sun light is sufficient, create the greatest energy saving potential, with a reduction of 21.9% of the annual energy consumption. In Faro (Table 3) the results are aligned with those presented previously. The higher impact on energy consumption occurs when office occupants use natural lighting (-22.2%), but if they leave lighting on during the all working period, it will increase annual energy consumption by 14.6%. Inefficient HVAC system operation generates an increase of 9.1%, while setting up the energy saving plans of computers impacts energy consumption by -12.5%. Analysing the impact of occupants’ behaviour on energy consumption by end-use (heating, cooling, lighting and office equipment), it was found the most inefficient HVAC use significantly impacts energy consumption of heating by +72.6% in offices from Faro. Furthermore, and particularly in Oporto and Lisbon, if occupants forget switching off the cooling function in 5% or 10% of the overall period, a reduction of cooling energy consumption of almost 5% is obtained. This interesting and somewhat unexpected result is a consequence of the free cooling effect occurring when outdoor temperature is lower than indoor set point temperature, which occurs mainly during the evening. Apparently, the free cooling effect is sufficient to balance the over consumption resulting from leaving the HVAC system on during the evening, if this occurs in 5% or 10% of the days. For the most careless 5


HVAC operation profile, the free cooling effect is not enough to compensate the associated energy consumption increase. To test this theory, the simulation experiments were repeated for Oporto, decoupling the cooling and ventilation systems, and leaving the later unaffected by the changes associated with the inefficient occupant behaviour. Results showed cooling energy consumption increased when free cooling was not available, thus confirming the proposed explanation (Table 4). This effect is not observed when occupants forget to switch off the cooling function in 20% of the period, and an increase of cooling energy consumption is observed (e.g., +4% in Faro). There is no observable change between energy consumption by lighting among the different locations, since identical thermal loads were considered for all places. However, efficient occupants’ from Faro originated the most significant energy saving potential in lighting, 85.4%. In fact, Faro benefits from favourable climate conditions to maximising the use of natural lighting. Nevertheless, other significant savings were also achieved in Lisbon and Oporto (-81.4% and -83.7% in lighting, respectively). Inefficient practices associated with leaving lights on during the entire working time lead to an increase of 54.0% in lighting energy consumption. Furthermore, efficient occupants’ behaviour using lighting results in an increase of heating needs, and consequently, of the energy consumption for heating purposes (although this effect in not relevant in the annual energy consumption). Occupants’ behaviour regarding office equipment led to energy savings of 26.7% when practices are efficient, and 26.9% when they are inefficient. Table 1. Potential energy consumption and savings related to occupants’ behaviour in Oporto offices Final energy consumption [kWh/year]

%

Heating [kWh/year]

%

Cooling [kWh/year]

%

Lighting [kWh/year]

%

Equipment [kWh/year]

%

10,899.8

100.0

2,803.2

100.0

621.0

100.0

2,866.0

100.0

4,609.6

100.0

HVAC efficient occupant

10,733.0

-1.5

2,644.2

-5.7

613.2

-1.2

2,866.0

0.0

4,609.6

0.0

5% HVAC Inefficient occupant

11,126.7

2.1

3,049.7

8.8

601.4

-3.2

2,866.0

0.0

4,609.6

0.0


11,458.8

5.1

3,391.2

21.0

591.0

-4.8

2,866.0

0.0

4,609.6

0.0


12,479.5

14.5

4,369.2

55.9

634.7

2.2

2,866.0

0.0

4,609.6

0.0

Lighting efficient occupant

8,668.9

-20.5

3,065.7

9.4

462.3

-25.6

532.3

-81.4

4,609.6

0.0

Lighting all day On

12,285.2

12.7

2,571.5

-8.3

691.7

11.4

4,412.4

54.0

4,609.6

0.0

5% Lighting Inefficient occupant

10,975.4

0.7

2,797.0

-0.2

624.0

0.5

2,944.8

2.7

4,609.6

0.0


11,054.9

1.4

2,792.9

-0.4

626.4

0.9

3,026.0

5.6

4,609.6

0.0


11,178.6

2.6

2,778.6

-0.9

630.3

1.5

3,160.1

10.3

4,609.6

0.0

Equipment efficient occupant

9,754.8

-10.5

2,964.1

5.7

544.3

-12.4

2,866.0

0.0

3,380.4

-26.7

Equipment all day On

12,010.6

10.2

2,603.0

-7.1

690.0

11.1

2,866.0

0.0

5,851.5

26.9

5% Equipment Inefficient occupant

10,944.5

0.4

2,800.3

-0.1

622.6

0.3

2,866.0

0.0

4,655.6

1.0


11,003.4

1.0

2,796.3

-0.2

624.5

0.6

2,866.0

0.0

4,716.6

2.3


11,092.5

1.8

2,787.3

-0.6

627.5

1.0

2,866.0

0.0

4,811.7

4.4

Location

Reference occupant

Porto

6


Table 2. Potential energy consumption and savings related to occupants’ behaviour in Lisbon offices Location Reference occupant

Lisbon

Final energy consumption [kWh/year]

%

Heating [kWh/year]

%

Cooling [kWh/year]

%

Lighting [kWh/year]

%


%

11,619.7

100.0

2,033.6

100.0

2,110.4

100.0

2,866.0

100.0

4,609.6

100.0


11,416.0

-1.8

1,867.7

-8.2

2,072.7

-1.8

2,866.0

0.0

4,609.6

0.0


11,820.3

1.7

2,235.4

9.9

2,109.3

-0.1

2,866.0

0.0

4,609.6

0.0


12,004.2

3.3

2,419.2

19.0

2,109.4

0.0

2,866.0

0.0

4,609.6

0.0


12,930.1

11.3

3,310.0

62.8

2,144.4

1.6

2,866.0

0.0

4,609.6

0.0


9,078.7

-21.9

2,244.0

10.3

1,759.0

-16.7

466.1

-83.7

4,609.6

0.0

Lighting all day On

13,140.2

13.1

1,840.1

-9.5

2,278.1

7.9

4,412.4

54.0

4,609.6

0.0


11,696.1

0.7

2,028.9

-0.2

2,112.8

0.1

2,944.8

2.7

4,609.6

0.0


11,780.9

1.4

2,025.4

-0.4

2,119.9

0.4

3,026.0

5.6

4,609.6

0.0


11,912.1

2.5

2,014.1

-1.0

2,128.3

0.8

3,160.1

10.3

4,609.6

0.0


10,303.5

-11.3

2,100.6

3.3

1,956.5

-7.3

2,866.0

0.0

3,380.4

-26.7


12,866.2

10.7

1,868.5

-8.1

2,280.1

8.0

2,866.0

0.0

5,851.5

26.9


11,664.6

0.4

2,031.4

-0.1

2,111.6

0.1

2,866.0

0.0

4,655.6

1.0


11,727.3

0.9

2,028.2

-0.3

2,116.5

0.3

2,866.0

0.0

4,716.6

2.3


11,818.1

1.7

2,020.4

-0.6

2,119.9

0.4

2,866.0

0.0

4,811.7

4.4

Table 3. Potential energy consumption and savings related to occupants’ behaviour in Faro offices Final energy consumption [kWh/year]

%

Heating [kWh/year]

%

Cooling [kWh/year]

%

Lighting [kWh/year]

%


%

11,245.2

100.0

1,275.0

100.0

2,494.6

100.0

2,866.0

100.0

4,609.6

100.0


11,099.6

-1.3

1,160.4

-9.0

2,463.6

-1.2

2,866.0

0.0

4,609.6

0.0


11,438.8

1.7

1,463.1

14.8

2,500.1

0.2

2,866.0

0.0

4,609.6

0.0


11,518.2

2.4

1,600.6

25.5

2,442.0

-2.1

2,866.0

0.0

4,609.6

0.0


12,269.5

9.1

2,200.4

72.6

2,593.5

4.0

2,866.0

0.0

4,609.6

0.0


8,743.2

-22.3

1,018.2

-20.1

2,727.1

9.3

418.1

-85.4

4,609.6

0.0

Lighting all day On

12,887.5

14.6

1,132.0

-11.2

2,733.5

9.6

4,412.4

54.0

4,609.6

0.0


11,321.7

0.7

1,270.4

-0.4

2,496.9

0.1

2,944.8

2.7

4,609.6

0.0


11,405.4

1.4

1,267.9

-0.6

2,502.0

0.3

3,026.0

5.6

4,609.6

0.0


11,546.7

2.7

1,261.3

-1.1

2,515.7

0.8

3,160.1

10.3

4,609.6

0.0


9,843.6

-12.5

1,318.5

3.4

2,278.6

-8.7

2,866.0

0.0

3,380.4

-26.7


12,596.3

12.0

1,151.5

-9.7

2,727.2

9.3

2,866.0

0.0

5,851.5

26.9


11,290.3

0.4

1,272.9

-0.2

2,495.8

0.0

2,866.0

0.0

4,655.6

1.0


11,352.4

1.0

1,270.2

-0.4

2,499.5

0.2

2,866.0

0.0

4,716.6

2.3


11,450.9

1.8

1,265.5

-0.7

2,507.7

0.5

2,866.0

0.0

4,811.7

4.4

Location

Reference occupant

Faro

7


Table 4. Cooling energy consumption with and without free-cooling in Oporto offices With free-cooling

Cooling energy consumption

kWh/year

%

Without free-cooling kWh/year

%

Inefficient occupant – HVAC 5% of days

601.4

- 3.2

645.8

4.0


591.0

- 4.8

669.5

7.8


634.7

2.2

757.8

22.0

4. CONCLUSIONS From an environmental perspective, any energy savings potential will bring benefits to society, regardless location or measure used. This study quantified the influence of occupants’ behaviour in the energy consumption of small offices buildings using dynamic simulation tools in a creative manner. Although behaviours have a stochastic nature, a deterministic approach allowed establishing limit values for impact indicators reflecting extreme operation scenarios. The present study clearly showed behavioural measures may be among the most effective energy efficiency promotion initiatives either from financial or environmental perspectives, reinforcing the need for behavioural energy efficiency measures in small offices. ACKNOWLEDGEMENT This work has been partially supported by the Energy and Mobility for Sustainable Regions Project (CENTRO-07-0224-FEDER-002004) and by Fundação para a Ciência e a Tecnologia (FCT) under grant SFRH/BD/51104/2010 and project grant UID/MULTI/00308/2013. REFERENCES [1] [2]

[3] [4]

[5] [6] [7]

EC, EU Transport in Figures 2012 - Statistical Pocketbook, E. Commission, Editor. 2012, Publications Office of the European Union. Capros, P., et al., European Energy and Transports. Trends to 2030 - Update 2007, E.C.D.-G.f.E.a. Transport, Editor. 2008, Office for Official Publications of the European Communities. Filippín, C., et al., Response of conventional and energy-saving buildings to design and human dependent factors. Solar Energy, 2005. 78(3): p. 455-470. Ürge-Vorsatz, D., et al., Bottom–up assessment of potentials and costs of CO2 emission mitigation in the buildings sector: insights into the missing elements. Energy Efficiency, 2009. 2(4): p. 293-316. Hoes, P., et al., User behavior in whole building simulation. Energy and Buildings, 2009. 41(3): p. 295-302. Masoso, O.T. and L.J. Grobler, The dark side of occupants' behaviour on building energy use. Energy and Buildings, 2010. 42(2): p. 173-177. Rijal, H.B., et al., Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings, 2007. 39(7): p. 823-836. 8


[8]

[9]

[10]

[11] [12]

[13]

[14]

[15] [16]

[17]

[18]

[19] [20] [21] [22]

[23]

Schweiker, M. and M. Shukuya, Comparison of theoretical and statistical models of air-conditioning-unit usage behaviour in a residential setting under Japanese climatic conditions. Building and Environment, 2009. 44(10): p. 2137-2149. Herkel, S., U. Knapp, and J. Pfafferott, Towards a model of user behaviour regarding the manual control of windows in office buildings. Building and Environment, 2008. 43(4): p. 588-600. Karjalainen, S., Gender differences in thermal comfort and use of thermostats in everyday thermal environments. Building and Environment, 2007. 42(4): p. 15941603. Lenzuni, P., D. Freda, and M. Gaudio, Classification of Thermal Environments for Comfort Assessment. The Annals of Occupational Hygiene, 2009. 53(4): p. 325–332. Moujalled, B., R. Cantin, and G. Guarracino, Comparison of thermal comfort algorithms in naturally ventilated office buildings. Energy and Buildings, 2008. 40(12): p. 2215-2223. Fabi, V., et al., Occupants' window opening behaviour: A literature review of factors influencing occupant behaviour and models. Building and Environment, 2012. 58(0): p. 188-198. Branco, G., et al., Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data. Energy and Buildings, 2004. 36(6): p. 543-555. Haas, R., H. Auer, and P. Biermayr, The impact of consumer behavior on residential energy demand for space heating. Energy and Buildings, 1998. 27(2): p. 195-205. Lindén, A.-L., A. Carlsson-Kanyama, and B. Eriksson, Efficient and inefficient aspects of residential energy behaviour: What are the policy instruments for change? Energy Policy, 2006. 34(14): p. 1918-1927. Fabi, V., et al. Description of occupant behaviour in building energy 2 simulation: state-of-art and concepts for improvements. in Building Simulation 2011. 2011. Sydney: International Building Performance Simulation Association. Lopes, M.A.R., C.H. Antunes, and N. Martins, Energy behaviours as promoters of energy efficiency: A 21st century review. Renewable and Sustainable Energy Reviews, 2012. 16(6): p. 4095-4104. Yu, Z., et al., A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 2011. 43(6). Ryan, E.M. and T.F. Sanquist, Validation of building energy modeling tools under idealized and realistic conditions. Energy and Buildings, 2012. 47(0): p. 375-382. Virote, J. and R. Neves-Silva, Stochastic models for building energy prediction based on occupant behavior assessment. Energy and Buildings, 2012. 53(0): p. 183-193. Clevenger, C.M. and J. Haymaker. The impact of the building occupant on energy modeling simulations. in Computing and Decision Making in Civil and Building Engineering. 2006. Montreal, Canada: International Society for Computing in Civil and Building Engineering. Nicol, J. Characterising occupant behaviour in buildings: towards a stochastic model of occupant use of windows, lights, blinds, heaters and fans. in Seventh International 9


[24]

[25]

[26]

IBPSA Conference. 2001. Rio de Janeiro, Brazil: International Building Performance Simulation Association. Bourgeois, D., C. Reinhart, and I. Macdonald, Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control. Energy and Buildings, 2006. 38(7): p. 814-823. Bonte, M., F. Thellier, and B. Lartigue, Impact of occupant's actions on energy building performance and thermal sensation. Energy and Buildings, 2014. 76(0): p. 219-227. MOPTC, RSECE, Regulation of the buildings air conditioning energy systems in Decree-law 80/2006, T.a.C. Ministry of Public Construction, Editor. 2006, Daily Republic. p. 2468-2513.

10