Thesis Building performance simulation to support building energy regulation. A case study for residential buildings in Brazil.

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Thesis

Building performance simulation to

support building energy regulation.

A case study for residential buildings in Brazil.

Author

A.N. van der Knaap

Student ID 0596915

Date

December 21

st

2011

Study

Master Building Services

Department of the Built Environment

Eindhoven University of Technology

Supervisors

prof.dr.ir J.L.M. Hensen

_______________________

dr.ir. M.G.L.C. Loomans

_______________________

dr. Daniel Cóstola

_______________________

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Building performance simulation to support

building energy regulation.

A case study for residential buildings in Brazil.

A.N. van der Knaap

*

Master Building Services

Department of the Built Environment, Eindhoven University of Technology, Eindhoven, The Netherlands

ABSTRACT

Many countries around the world are introducing building energy regulation to increase energy efficiency in the built environment. But effective building energy regulation depends on appropriate indicator and method for assessment, for which a simplified method is common practice. This paper assesses the ability of the Simplified Method of building energy regulation in Brazil to represent energy efficiency of residential buildings. The assessment compares energy labels, performance indicators and sensitivity to parameters with building performance simulations for detached houses and apartments. The results indicate that the Simplified Method underestimates energy efficiency of natural ventilated residential buildings and overestimates energy efficiency of air-conditioned residential buildings. Therefore, air-conditioned residential buildings could be less energy efficient in reality, which may lead to ineffective building energy regulation.

Keywords: building energy regulation, energy label, simplified method, building performance simulation. Date: December 21st₂₀₁₁

1. Introduction

Residential energy consumption is rising on a global scale due to an increasing number of households, increasing living standards and an increasing percentage of households connected to national power grids [1]. This trend causes technical, financial and environmental issues for national governments. These issues are expanding the capacity for energy production and energy distribution, high investments in the energy sector and increasing CO2 emissions and resource

depletion [2].

A common solution to overcome the negative effects of rising residential energy consumption is increasing energy efficiency of residential buildings. Higher energy efficiency of buildings makes it possible to increase the level of services provided with the same amount of energy [3]. Governments worldwide pursuit higher energy efficiency of buildings by implementing building energy regulation, which is known as an effective measure [3-5]. Building energy regulation is implemented in many different forms between countries. Differences occur in voluntary or mandatory regulation and in using a label system, a threshold for a performance indicator or design guidelines [5]. But for controlling and limiting energy consumption in buildings effectively with building energy regulation it is important to use an appropriate performance indicator and an appropriate method to assess the performance indicator [6, 7]. An appropriate performance indicator has the following properties: (1) it indicates a quantitative amount of energy per year, (2) it includes basic elements of energy consumption, (3) it is expressed in primary energy demand and (4) it limits the total energy demand, from both renewable and non-renewable sources. Important issues for the method of assessment of the performance indicator are: accuracy, scope, reproducibility, complexity, sensitivity to energy parameters and user skills. However, successfull building energy regulation depends mostly on: (1) the ability to obtain better labels cost effectively, (2) the credibility achieved by real energy savings and (3) the degree of commitment to environmental problems of stakeholders in the building sector [6-8].

In terms of implementing energy efficiency policies for buildings, Brazil is the leading country in Latin-America [9] and introduced a voluntary label system for commercial buildings in 2009 and for residential buildings in 2010. This is a result of the Energy Conservation Act, implemented after a major electricity generation crisis in 2001 [10, 11].

Energy efficiency policy for residential buildings in Brazil consists of a voluntary label system which is intended to be mandatory in the future. The energy efficiency label ranges from ‘A’ (high efficient) to ‘E’ (low efficient) and is determined by using the Standard for Energy Efficiency in Residential Buildings (RTQ-R). The calculation of the energy label is based on a ratio between the efficiency of the thermal envelope (65-95%) and the domestic hot water system (5-35%). This ratio depends on the geographic location in order to take the large climatic differences in Brazil into accoutn. The efficiency of the thermal envelope is based on a ratio between (1) the number of degree hours overheating and the annual energy consumption for heating of natural ventilated residential buildings and (2) the annual energy consumption for cooling and for heating of air-conditioned residential buildings. These indicators can either be determined by a Simplified Method, consisting of linear equations, or by using building performance simulations with prescriptions from the RTQ-R [12].

In order to contribute to effective building energy regulation in Brazil, this research assesses the ability of the Simplified Method to represent energy efficiency of residential buildings in Brazil. The assessment consists of a comparison between the results of the Simplified Method and the results from building performance simulations. Using simulations for evaluating building energy regulation has been common practice for many years [13]. The comparison of the results from the Simplified Method and building performance simulations are in terms of (1) energy labels, (2) performance indicators and (3) sensitivity to input parameters. The assessment is based on samples for detached houses and apartments with variations in 23 input paramters to approach representation of residential buildings in Brazil. The methods applied in this research are described in the following chapter and the results of assessing the Simplified Method are described in chapter 3. The discussion of the results is described in chapter 4 and this paper ends with the conclusions from this research in chapter 5.

2. Methods

The assessment of the Simplified Method is performed by comparing its results with results from building performance simulations. Figure 1 shows an overview of the process of this research, consisting of the pre-processing, performing simulations and post-processing.

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Figure 1: Overview of research process.

This chapter first explains how the RTQ-R labels energy efficiency of residential buildings using the Simplified Method. Secondly, it describes the Baseline Scenario, which is the point of reference in this research and thirdly, it describes how the Simplified Method is assessed using building performance simulations. Finally, this chapter describes the validation of the models used in this research.

2.1. Labelling with the Simplified Method

As stated in the introduction of this paper, the energy label for residential buildings is based on a ratio between the thermal envelope and the domestic hot water system. This research focus on the efficiency of the thermal envelope, which is based on a ratio between the performance indicators for overheating, heating and cooling. This ratio depends on the bioclimatic zone [14] in which the residential building is situated. The location used in this research is the city of Florianópolis in the state of Santa Catarina, which is in bioclimatic zone number 3. Therefore the ratio used in this research is 0.64 times the performance indicator for overheating or cooling, plus 0.36 times the performance indicator for heating. This ratio changes between bioclimatic zones 1 till 4 and consists of solely the performance indicator for cooling or overheating in bioclimatic zones 5 till 8.

Each of the three performance indicators can be determined by the linear equations of the Simplified Method or by using building

performance simulations. The equations from the Simplified Method calculate the performance indicators based on a limited number of input parameters, such as the building geometry and properties of the thermal envelope. After calculating the performance indicator, the result is categorized in an equivalent number from 1 till 5. Each category represents a bandwidth for the performance indicators for overheating, heating or cooling.

The efficiency of the thermal envelope is then determined by the ratio between the equivalent number for overheating or cooling and the equivalent number for heating. This ratio expresses the efficiency in another equivalent number between 1 till 5, with 1 as low efficient and 5 as high efficient. This equivalent number represents the energy label for the thermal envelope, ranging from E till A.

2.2. Building the Baseline Scenario

The first step in this research was building the Baseline Scenario. This scenario consists of two different types of residential buildings, detached houses and apartments, with similar building properties. Both types are labelled by the Simplified Method and simulations. The Baseline Scenario is used as a point of reference from which the variations in input parameters are taken into account and for validation of the models.

The two different types of residential buildings in this research consist of 3 detached houses with different sizes and 20 apartments in an apartment

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building. The detached houses represent single-family houses and account for 85.2% of the housing stock in the state of Santa Catarina. The apartments represent multi-family apartment buildings and account for 14.6% of the housing stock in the state of Santa Catharina [15].

The building properties for the Baseline Scenario are based on the prescriptions for using building performance simulations by the RTQ-R. Additional building properties were based on previous research for detached houses and apartments in Brazil using simulations [16-18]. Using building properties from previous research enables the validation of the Baseline Scenario. The Baseline Scenario for the Simplified Method is made in Excel and for the building performance simulations in EnergyPlus.

2.3. Creating the samples for assessment

Assessing the Simplified Method requires taking a high number of variations in input parameters into account to represent residential buildings in Brazil. In total, this assessment, takes variations into account for 23 different input parameters. Due to the limited number of input parameters in the Simplified Method, only 8 of the 23 are variating in the Simplified Method. The variations are based on Brazilian standards and

guidelines, previous research and experts opinion of residential buildings in Brazil [12, 14, 16-19].

Within each parameter, some variations are more common than others in Brazilian residential buildings. Therefore each variation has a probability of occurring in a residential building in Brazil. In this research the probability indicates the percentage of appearances of the particular variation in the total number of samples. The probabilities are based on experts opinion.

Table 1 shows an overview of the variations in the 23 input parameters, including their probability and source. The 8 input parameters for the Simplified Method are indicated with ‘SM’ in the column for Method. The indication ‘E+’ refers to the simulations which are performed with EnergyPlus.

Based on the variations and their probability, the samples for the assessment were made using the SimLab tool in MatLab for using the Latin Hypercube Sampling method [20-22]. These samples were based on the Baseline Scenario, taking the variations into account. In total 600 samples were made for the 3 detached houses and the 20 apartments, divided by 300 samples for natural ventilation and 300 samples air-conditioning. The number of samples is based on the number of input parameters and the complexity of the models in the simulations [23].

Table 1

Overview of variations in input parameters.

Parameter Values Probability Method Source

Orientation relative to North U-value external Walls U-value Roof Solar Absorptance walls and roof Window to Floor Ratio Openable Window Fraction Blinded Window Fraction Thermal Capacity Roughness of Terrain Discharge coefficient Crackflow coefficient Glasstype in windows COP of HVAC-system Setpoint for Heating Setpoint for Cooling Setpoint for Opening Windows IHG of People per Bedroom IHG of People in Livingroom IHG of Lighting IHG of Equipment Schedule of Blinds Schedule of HVAC-system Schedule for Opening Windows [°] [0;90;180;270] [25;25;25;25] SM / E+ [-] [W/m²K] [3.13;2.49;1.80;1.12] [16.7;45.8;25.0;12.5] SM / E+ [13] [W/m²K] [2.24;1.92;1.09;0.62] [16.7;45.8;25.0;12.5] SM / E+ [13] [-] [0.2;0.4;0.8] [25;50;25] SM / E+ [17] [-] [0.10;0.15;0.20] [33.3;33.3;33.3] SM / E+ [-] [-] [0.25;0.50;1.00] [25;50;25] SM / E+ [12] [-] [0.1;0.5;0.9] [33.3;33.3;33.3] SM / E+ [12] [KJ/m²·K] [50;167;250] [25;50;25] SM / E+ [12,13] [-] [0.14;0.22;0.33] [20;40;40] E+ [18] [-] [0.4;0.6;1.0] [25;50;25] E+ [15-18] [kg/s·m] [0.00010;0.00028;0.00100] [33.3;33.3;33.3] E+ [15-18] [mm] [3.0;6.0;9.0] [60;30;10] E+ [-] [-] [2.6;2.8;3.0;3.2] [25;25;25;25] E+ [12] [°C] [18.0;19.0;20.0;21.0;22.0] [10;20;40;20;10] E+ [12] [°C] [22.0;23.0;24.0;25.0;26.0] [10;15;20;35;20] E+ [12] [°C] [20.0;22.0;24.0;26.0] [25;25;25;25] E+ [12,15-17] [no.] [1;2] [40;60] E+ [12] [no.] [2;4;6] [33.3;33.3;33.3;33.3] E+ [12] [W/m²] [4.0;6.0;8.0] [25;50;25] E+ [12] [W/m²] [1.0;1.5;2.0] [25;50;25] E+ [12] [months.hours] [0.10;6.10;9.10] [25;50;25] E+ [12,15,17] [months.hours] [9.12;12.12;12.24] [25;50;25] E+ [12] [months.hours] [12.12;9.24;12.24] [25;50;25] E+ [12,15-17]

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2.4. Assessing the Simplified Method

The Simplified Method and building performance simulations are both used to determine the three performance indicators for all samples that determine the energy label. These indicators are (1) the number of degree hours overheating, (2) the annual energy consumption for heating and (3) the annual energy consumption for cooling.

The results for these performance indicators are available for the bedrooms and living room in the detached houses and apartments. These results are then transformed to house level by taking the weighted average based on floor area. Afterwards the results on house level are transformed to the two types of residential buildings in this research, by taking the average of the 3 detached houses and the 20 apartments.

After processing the results, the assessment of the Simplified Method is performed by taking the following three steps. The first step is assessing the ability of the Simplified Method to represent energy efficiency in terms of energy labels. This is assessed by comparing the energy labels determined by the Simplified Method to those determined by simulations in EnergyPlus. In this comparison the frequency of energy labels per category for all the samples are compared. Note that direct comparison of the labels per sample is difficult due to extra variations in input parameters in the samples in simulations in EnergyPlus.

The second step in the assessment of the Simplified Method indicates its ability to represent the performance indicators for overheating, heating and cooling. This assessment is performed by comparing the range and distribution of the results for these performance indicators from the Simplified Method with the results from simulations in EnergyPlus. The range and distribution of the performance indicators are determined by using the statistics of their results.

The third and last step of the assessment of the Simplified Method is to assess its ability to take input parameters into account properly in the performance indicators. This is assessed by, first, performing sensitivity analyses for the performance indicators separately for the Simplified Method and for simulations in EnergyPlus. Sensitivity analyses indicate the influence of variations in input parameters on the results of the performance indicators. The sensitivity to input parameters in this research is expressed in the Spearman correlation coefficient. This coefficient is the most suitable because it is able to express single and higher order correlation

between the input parameter and the performance indicator with the same coefficient [20]. Afterwards the results from these sensitivity analyses of both methods are compared to assess whether the Simplified Method takes input parameters into account properly.

2.5. Validating the Simplified Method and simulation models

In order to gain confidence in using the Simplified Method and the simulation models in EnergyPlus, both methods are validated. This validation is performed by using intermodel comparison of the Baseline Scenario in both methods from this research.

The three equations for the performance indicators in the Simplified Method are validated by comparing the results of the Baseline Scenario with the results from the Simplified Method provided by LabEEE. The results of this comparison are available in Appendix A and show that the results are in agreement.

The simulation models in EnergyPlus are validated by comparing the results of the Baseline Scenario to results from previous research [18]. The common performance indicator with this previous research is the number of degree hours overheating for detached houses. Therefore the validation consists of this performance indicator for the three detached houses in this research. Due to different schedules and modelling of the thermal envelope, an extra model in EnergyPlus is made to represent the previous research. The results of this additional model are used for comparison with the results from the Baseline Scenario. The results for validation of the models in EnergyPlus are available in Appendix A and show a high level of agreement.

3. Results of assessing the Simplified

Method

This chapter provides the results of the assessment of the Simplified Method by comparison with Building Perofrmance Simulations in EnergyPlus. The results are divided in three sections, covering (1) the representation of energy efficiency by energy labels, (2) the role of the performance indicators for overheating, heating and cooling and (3) the sensitivity to variations in input parameters. Each section shows the results for the detached houses and apartments seperately, then discuss them and draw conclusions for that specific section. More detailed results for the 3 detached houses and 20 apartments are available in appendix B and C.

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Figure 2: Results of energy labels for natural ventilated and air-conditioned residential buildings by the Simplified Method and simulations.

3.1. Representation by energy labels

The representation of energy efficiency by the Simplified Method in terms of energy labels is based on comparing the labels of the samples from the Simplified Method with EnergyPlus. The results of this comparison is shown in figure 2 for the 300 samples of the detached houses and the apartments. Note that direct comparison is difficult due to more variating input parameters in EnergyPlus than in the Simplified Method. Which is done in order to increase representation of residential buildings in Florianópolis.

Figure 2a and 2c show that the Simplified Method labels a high number of samples in category B or C for natural ventilated detached houses and apartments. The majority of the samples are in category B for the detached houses and in category C for the apartments. Figure 2a and 2c also show that EnergyPlus labels a high number of samples in category A and B. A majority of the samples are in category A for the detached

houses and are in category B for the apartments. If we compare the labels per sample, the results show that the Simplified Method labels 33 samples of the detached houses and 98 samples of the apartments in the same categorie as EnergyPlus. The Simplified Method underestimates the label in 233 samples of the detached houses and in 192 samples of the apartments. Overestimation of the label only occurs in 1 sample of the detached houses and in 10 samples of the apartments.

Figure 2b and 2d show that the Simplified Method labels most of the samples in category B for air-conditioned detached houses and in category C for air-conditioned apartments. The large amount of samples in a single category is a remarkable result, regarding the wide variations in input parameters. This indicates that the Simplified Method has limitations in making a distinction in high-efficient residences from low-efficient residences in case of airconditioning. In contrast, Figure 2b and 2d, show that EnergyPlus labels a high number of samples in category B, C or D for both types of residential buildings. Comparing the

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results for labels per sample shows that the Simplified Method labels in the same categorie as EnergyPlus in 59 samples of the detached houses and in 57 samples of the apartments. The Simplified Method underestimates the label in 132 samples of the detached houses and in 28 samples of the apartments. Overestimation of the label occurs in 109 samples of the detached houses and in 215 samples of the apartments.

The comparison of the Simplified Method with EnergyPlus, in terms of energy labels, indicates that the Simplified Method (1) underestimates the efficiency of natural ventilated detached houses and apartments and (2) overestimates the efficiency of air-conditioned detached houses and apartments.

3.2. Role of overheating, heating and cooling

The ability of the Simplified Method to represent energy efficiency by energy labels, as presented in the previous section, depends on its ability to represent the performance indicators for overheating, heating and cooling. Therefore the ability to represent these indicators is also assessed by comparison with EnergyPlus. Figure 3 show the comparison of the range and distribution of the performance indicators for the detached houses and the apartments.

The comparison of the performance indicator for overheating, in figure 3a and 3d, show that the Simplified Method overestimates this indicator in the detached houses but not in the apartments. In case of the detached houses the range and the average in the Simplified Method are twice the range and the average in EnergyPlus. In case of apartments the range is similar and the average is slightly higher in the Simplified Method than in EnergyPlus. This overestimation for overheating contribute to underestimation of energy labels and explains the underestimation of mainly natural ventilated detached houses and to some extent apartments, as indicated in the previous section.

Comparing the performance indicator for heating in figure 3b and 3e show that the Simplified Method overestimates this indicator for the detached houses and apartments. In both cases the range of the Simplified Method is completely higher than the range of EnergyPlus. This overestimation of heating contributes to the underestimation of energy labels for natural

ventilated and air-conditioned detached houses and apartments.

The performance indicator for cooling in figure 3c and 3f show a narrow range for the Simplified Method compared to EnergyPlus. The range in EnergyPlus is between twice and four times the range of the Simplified Method. Its range is also positioned in the lower region of the range from EnergyPlus. The average on the other hand is to some extent comparable, although it is lower in the Simplified Method than in EnergyPlus. The small range in the Simplified Method indicates comparable energy efficiency between the samples despite the large variations in input paramters. This explains the high number of similar labels for air-conditioned detached houses and apartments, as indicated in the previous section. Because the range of the Simplified Method is in the lower region of the range from EnergyPlus, the Simplified Method is most likely to understimate annual energy consumption for cooling. This contributes to overestimation of labels by the Simplified Method and explains why labels for air-conditioned residential buildings are overestimated, as presented in the previous section.

The comparison of the performance indicators show that the Simplified Method overestimates overheating and heating, which lead to underestimation of labels for natural ventilated residential buildings. The comparison also shows that representation of the performance indicator for cooling is very limited, which leads to a high number of similar labels and to overestimation of labels for air-conditioned residential buildings.

Another remarkable result is the ratio between the annual energy consumption for heating and for cooling from the Simplified Method. The results show that energy consumption for heating is between 10 and 20 kWh/m² per year and energy consumption for cooling is between 5 and 15 kWh/m² per year. This is in contrast to the ratio of 0.64 for cooling and 0.36 for heating as prescribed by the RTQ-R to determine the label for the thermal envelope in the bioclimatic zone of this research. This result also indicates that energy consumption for heating is overestimated by the Simplified Method.

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Figure 3: Results of performance indicators for overheating, heating and cooling by the Simplified Method and simulations.

3.3. Sensitivity to input parameters

The last step in the assessment of the Simplified Method is comparing the sensitivity of the performance indicators for overheating, heating and cooling to variations in the input parameters. The sensitivity of these indicators for the Simplified Method and EnergyPlus are shown in figure 4. Note that only the sensitivity for the detached houses is shown, due to comparable trends in the apartments, which are available in appendix C.

The sensitivity of overheating in figure 4a and 4b indicates that the Simplified Method is most sensitive to the solar absorptance of the thermal envelope and the thermal capacity. EnergyPlus also indicate that overheating is most sensitive to the solar absorptance of the thermal envelope. But it shows far less sensitivity to thermal capacity than the Simplified Method. It indicates more sensitivity to the roughness of the terrain and the blinded window fraction, which has comparable influence in the Simplified Method. EnergyPlus does not indicate an input parameter with a high negative correlation, which could give an explanation of overestimating overheating, as shown in the previous section. In contrast, despite the high influence of the thermal capacity in the Simplified Method, it still overestimates overheating compared to EnergyPlus.

The sensitivity of the performance indicator for heating, in figure 4c, indicates that the Simplified Method is most sensitive to solar absorptance of the thermal envelope and thermal capacity. Again EnergyPlus indicates to be sensitive to the solar absorptance, but not to thermal capacity. In

contrast EnergyPlus, in figure 4d, indicates to be most sensitive to the schedule of the HVAC-system and the setpoint for heating. This result explains the difference in energy consumption of the performance indicator for heating, as shown in the previous section. Because all variations in these two important input parameters lead to less operational hours and a lower setpoint for heating, which results in lower energy consumption. Therefore the maximum energy consumption in EnergyPlus is comparable to the minimum energy consumption in the Simplified Method, indicating overestimation of energy consumption for heating by the Simplified Method.

The sensitivity of the performance indicator for cooling indicates that the Simplified Method, in figure 4e, is most sensitive to the solar absorptance of the thermal envelope and the termal capacity. Again the results from EnergyPlus, in figure 4f, support the sensitivity to solar absorptance, but do not support the sensitivity to thermal capacity. EnergyPlus also indicates a low sensitivity to the other input parameters from the Simplified Method. In contrast, EnergyPlus indicates that cooling is most sensitive to the setpoint for cooling and, to a lesser extent, internal heat gains from the residents and the COP of the HVAC-system. Variations in these parameters are not accounted for in the Simplified Method. This, in combination with low sensitivity to other input parameters from the Simplified Method, explains the narrow range in energy consumption for cooling, as shown in the previous section. This is the main reason for the uniform labelling of air-conditioned residential buildings by the Simplified Method.

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Figure 4: Sensitivty of performance indicators to variations in input parameters for the Simplified Method and simulations.

The sensitivity of the performance indicators to variations in input parameters indicates that the Simplified Method has a correct sensitivity to the solar absorptance of the thermal envelope, but also has an incorrect sensitivity to the thermal capacity. The results from EnergyPlus indicate that the performance indicators for heating and cooling are most sensitive to the setpoints for heating and cooling. They also indicate low sensitivity to other input parameters from the Simplified Method. These trends combined, explain the narrow range in annual energy consumption for cooling, which lead to uniform labelling of air-conditioned detached houses and apartments.

4. Discussion

This research assessed the ability of the Simplified Method to represent energy efficiency of residential buildings in terms of energy labels, performance indicators and sensitivity to input parameters. The assessment is performed by comparing results from the Simplified Method with results from building performance simulations. The scope of this research is limited to the energy efficiency of the thermal envelope for detached houses and apartments in the climatic conditions of Florianópolis, Brazil. Using building performance simulations enables this research to study a wide variation of scenarios for residential buildings, but

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still is a simplification of residential energy consumption in reality. Nevertheless, building performance simulations plays an important role in developing and evaluating building energy regulation [13].

The results from this research indicate that the Simplified Method underestimates labels for natural ventilated residential buildings compared to building performance simulations. This is a result of the Simplified Method overestimating the performance indicators for overheating and for heating. The effect of underestimating energy labels of residential buildings is that in reality, the performance indicators of overheating and for heating will be lower than expected. This is favourable for the residents and contributes to the credibility of the label by society. However, taking measures to improve the label are likely to be less cost-effective, because the potential for saving energy is lower.

The results also indicate that the Simplified Method determines a high number of similar energy labels for air-conditioned residential buildings compared to building performance simulations. This is caused by the narrow range in the performance indicator for cooling, determined by the Simplified Method. The effect of the high number of similar labels is the risk of underestimation and overestimation of the actual energy consumption in residential buildings. This is a threat for successful implementation of building energy regulation, due to a lack of credibility by society if higher energy consumption occurs. And also the effect of energy saving measures is not likely to improve the label with the Simplified Method. Therefore, increasing the representation of the Simplified Method will improve the credibility of the label by the residents and improves the incentive to improve the energy label, which will both contribute to more successful implementation of building energy regulation in Brazil [7]. Increasing representation of the Simplified Method is possible and can be achieved by using equations for the performance indicators with higher accuracy [24].

The results for the sensitivity of the performance indicators to variations in input parameters indicate that the setpoints for heating and cooling are important parameters. Therefore, taking the setpoints into account in the Simplified Method would improve its representation. But this is complicated due to the influence of residents on the setpoints. However, including threshold values for the nominal capacity of the HVAC-system per square meter of conditioned area is a possibility to increase the representation for heating and cooling. The nominal capacity depends on design

indoor and outdoor temperatures and thus on the setpoints. Combining this threshold value with the COP of the HVAC-system should then be considered, since this is another important parameter.

The sensitivity to input parameters also indicates that the solar absorptance and thermal capacity are important parameters in the Simplified Method. The results from building performance simulations indicate solar absorptance as an important parameter, but do not indicate thermal capacity as an important parameter. Therefore, increasing or decreasing the thermal capacity will improve the label in the Simplified Method, but is not likely to improve the energy efficiency of residential buildings in reality. This shows importance for reconsideration of how to take thermal capacity into account in the Simplified Method.

This research assessed the ability of the Simplified Method to represent energy efficiency of residential buildings, because this is important for effective and successful implementation of building energy regulation. The results show limited representation in labels due to a high influence of parameters which are not taken into account and therefore the effectiveness the regulation is at stake. But in general residential buildings with a higher label will have a more efficient thermal envelope which will lead to lower energy consumption for HVAC-systems under similar conditions. If this leads to lower residential energy consumption depends, however, on the operation of the HVAC-system, which is controlled by the residents. Therefore increasing the awareness of residents on the importance of reducing residential energy consumption is also very important. Moreover, increasing energy efficiency in residential buildings in Brazil is also possible by improving thermal comfort in residential buildings by applying passive cooling techniques instead of air-conditioning [14, 25-28]. This will lower the energy demand for space conditioning and increases energy efficiency of residential buildings.

5. Conclusions

This research assessed the ability of the Simplified Method to determine energy efficiency labels that represent energy efficiency of residential buildings in Brazil. Based on the results of the assessment of the Simplified Method this research concludes that:

• The Simplified Method underestimates energy efficiency of natural ventilated residential buildings, due to overestimation of the

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performance indicators for overheating and heating.

• The Simplified Method underestimates and overestimates energy efficiency of air-conditioned residential buildings, due to limited representation of energy consumption for cooling.

• The setpoints for heating and cooling are important parameters for determining the annual energy consumption for heating and cooling.

• The Simplified Method has a correct high sensitivity to solar absorptance of the thermal envelope, but has an incorrect high sensitivity to thermal capacity of residential buildings. Based on these conclusions, the ability of the Simplified Method to represent energy efficiency of residential buildings is limited. Therefore improvements are recommended to increase effectiveness of building energy regulation in Brazil. In this framework future work should focus on increasing the accuracy of the Simplified Method to improve representation of energy

efficiency. This is possible by taking setpoints for heating and cooling into account, because these are important input parameters. Furthermore, the sensitivity to thermal capacity of the Simplified Method should be reconsidered because building performance simulations does not support its high influence. And finally, future work should focus on expanding the geographic location of this research to other regions in Brazil and types of residential buildings, for broader assessment of representation by the Simplified Method.

Acknowledgements

The author wishes to acknowledge Ana Paula Melo MSc. from the research institute LabEEE of the Universidade Federal de Santa Catarina in Florianopolis (SC), Brazil. The author also wishes to acknowledge Dr. Daniel Cóstola, Dr. Marcel Loomans and Prof. Jan Hensen from the Unit Building Physics and Systems of the Department of the Built Environment from the Eindhoven University of Technology.

List of abbreviations

ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers COP Coefficient Of Performance

HVAC Heating, Ventilation and Air-Conditioning IHG Internal Heat Gains

LabEEE Laboratório de Eficiência Energética em Edificações

RTQ-R Regulamento Tecnico da Qualidade para o nivel de eficiencia energetica de edificaoes Residenciais

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[7] L.Pérez-Lombard, J.Ortiz, R.González, I.R.Maestre, A review of benchmarking, rating and labelling concepts within the framework of building energy certification schemes, Energy and Buildings 41 (2009) 272-278.

[8] L.Pérez-Lombard, J.Ortiz, J.F.Coronel, I.R.Maestre, A review of HVAC systems requirements in building energy regulations, Energy and Buildings 43 (2010) 255-268.

[9] CIB, Task Group 66 Web Event: The implementation of energy efficient buildings policies in South America, CIB: International Council Building, 2010.

[10] S.Bodach, J.Hamhaber, Energy efficiency in social housing: Opportunities and barriers from a case study in Brazil, Energy Policy 38 (2010) 7898-7910.

[11] L.P.Rosa, L.L.Lomardo, The Brazilian energy crisis and a study to support building efficiency legislation, Energy and Buildings 36 (2004) 89-95.

[12] INMETRO, Regulamento Tecnico da Qualidade para o nivel de eficiencia energetica de edificaoes Residenciais, Instituto de Metrologia, Normalização e Qualidade Industrial, 449, 2010.

[13] Dru Crawley, Building simulation for policy support, in Jan L.M.Hensen & Roberto Lamberts (Eds.), Building Performance Simulation for Design and Operation, Spon Press, 2011.

[14] ABNT, NBR 15220-3 - Desempenho térmico de edificações - Parte 3: Zoneamento bioclimático brasileiro e diretrizes construtivas para habitações unifamiliares de interesse social, Associação Brasileira de Normas Técnicas, Rio de Janeiro, 2005, pp. 1-23.

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[15] IBGE, Síntese de Indicadores Sociais, Instituto de Brasileiro de Geografia e Estatística, Rio de Janeiro, 2008, pp. 1-280. [16] R.De Souza Versage, Ventilação naturale desempenho térmico de edifícios verticais multifamiliares em Campo Grande, MS.,

Universidade Federal de Santa Catarina, Florianópolis, 2009, pp. 1-96.

[17] A.P.Melo, R.Lamberts, R.Versage, P.A.Strachan, Manual de Simulação Computacional de Edifícios Naturalmente Ventilados no programa EnergyPlus., Laboratório de Eficiência Energetica em Edificações, Florianópolis, 2008, pp. 1-42.

[18] M.J.Sorgato, Desempho termico de edificacoes residenciais unifamiliares ventiladas naturalmente, Universidade Federal de Santa Catarina, Florianópolis, 2009, pp. 1-216.

[19] University of Illinois, Ernest Orlando Lawrence Berkeley National Laboratory, EnergyPlus Input Output Reference, University of Illinois; Ernest Orlando Lawrence Berkeley National Laboratory; 2010, pp. 1-2263.

[20] A.Saltelli, S.Tarantola, F.Campolongo, M.Ratto, How to use SimLab, Sensitivity Analysis in Practice, John Wiley & Sons Ltd, West Sussex, 2004, pp. 193-202.

[21] C.J.Hopfe, Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization, Eindhoven University of Technology, Eindhoven, 2009, pp. 1-229.

[22] Doxygen, SimLab Cook Book, Doxygen, 2010, pp. 1-993.

[23] P.-J.Hoes, Gebruikersgedrag in gebouwsimulaties: van eenvoudig tot geavanceerd gebruikersgedragmodel, Technische Universiteit Eindhoven, Eindhoven, 2007, pp. 1-118.

[24] A.P.Melo, D.Cóstola, R.Lamberts, J.L.M.Hensen, Assessing the accuracy of a simplified building energy simulation model using BESTest: The case study of Brazilian regulation, 2011.

[25] A.N.v.d.Knaap, Thermal comfort in social housing in developing countries: Application of passive cooling techniques, Eindhoven University of Technology, Eindhoven, 2010.

[26] B.Givoni, Climate considerations in building and urban design, John Wiley & Sons Inc, London 1998. [27] M.Santamouris, D.N.Asimakopoulous, Passive cooling of buildings, James & James, London 1996. [28] M.Santamouris, Advances in passive cooling, Earthscan, London 2007.

[29] R.H.Henninger, M.J.Witte, EnergyPlus Testing with Building Thermal Envelope and Fabric Load Tests from ANSI/ASHRAE Standard 140-2007, GARD Analytics, Arlington Heights, 2010, pp. 1-127.

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A. Validation of Simplified Method and simulation models

In order to validate the results of this research there are three different subjects that needed validation. The first subject for validation are the models for the number of degree hours and energy consumption for heating and for cooling, from the Simplified Method, which are put in Excel by hand. The second subject is the validation of the software EnergyPlus and the third subject is the validation of the simulation models used in this research which are simulated in EnergyPlus.

A.1. Simplified Method

The models from the Simplified Method are used to determine the (i) number of degree hours overheating (GHr), (ii) the energy consumption for heating (Ca) and (iii) the enery consumption for cooling (Cr). These

models were taken from Simplified Method and put in Excel by hand. The research institute, LabEEE, closely related to the construction of the Simplified Method delivers a similar Excel based Simplified Method model. The model from LabEEE is used for validating the the Simplified Method model in Excel by determining the GHr,

Ca and Cr of the Baseline Scenario with models and comparing them.

Figure A1: Validation Simplified Method Baseline Scenario detached house 150 m².

A.2. EnergyPlus

The validation of the software EnergyPlus is performed and presented in 2010 for the United States Department of Energy [29]. This validation was performed according to the ASHRAE Standard 140. In order to become familiar with EnergyPlus, I reproduced the BESTest Case 600, which is part of the ASHRAE Standard 140. The results of this additional validation consists of the total annual demand for heating and cooling and the peakrate in demand for heating and cooling. These results are shown in table A1.

Table A1

Results BESTEST 600.

Heating energy Cooling energy Heating rate Cooling rate

EnergyPlus 4.372 MWh 7.403 MWh 3.714 kW 6.810 kW

BETEST Min 4.296 MWh 6.137 MWh 3.437 kW 5.965 kW

BESTEST Max 5.709 MWh 8.448 MWh 4.354 kW 7.188 kW

Compliance yes yes yes yes

Models in EnergyPlus

The last subject for validation are the models used in this research to test the Simplified Method. This consists of three detached houses of 36 m², 63 m² and 150 m² and an apartment building with 20 apartments on 5 floors. The detached house in this research are based on research by LabEEE [18]. Therefore this research was used for validation of the models in this research. The common indicator in both researches is the number of degree hours overheating in the Baseline Scenario. The results of this validation are shown in figure A2.

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Figure A2: Results validation large, medium and small detached houses.

The results from the validation with the results from Sorgato (2009) show that the results from the Baseline Scenario and the EnergyPlus model of Soragto are similar. The differences between the Baseline Scenario and the result from Sorgato (2009) are higher.

There are a number of differences between the Baseline Scenario and Sorgato. The following table shows an overview of these differences. In order to explain the differences between the Baseline scenario and Sorgato (2009) in EnegryPlus for the small detached house. The effect is determined for the Sorgato model by changing an input parameter to the one from the Baseline Scenario.

Table A2

Input parameters Baseline Scenario and Sorgato 2009.

Description Baseline Scenario Sorgato Effect in GHR

Model Detached House 36 m² Detached House 36 m² -

Climate Florianopolis Florianopolis -

U-value Walls 1.80 W/m²K 1.80 W/m²K -

Solar absorptance Walls 0.4 0.4 -

U-value Roof 1.92 W/m²K 1.93 W/m²K -

Solar absorptance Walls 0.4 0.4 -

Window to Floor ratio 15% 15% -

Solar heat gain coefficient 0.837 0.87 -

AFN: openable area 50% 100% +90

AFN: setpoint for opening Tset ≥ 20°C Summer: Tset ≥ 20°C Winter:

Tset ≥ 22°C -5

AFN: schedule for opening 00.00 till 24.00 00.00 till 24.00 -

AFN: Pressure coefficient EnergyPlus TNO -20

AFN: Discharge coefficient 0.60 0.60 -

AFN: Crack coefficient 0.001 & n=0.65 0.001 & n=0.65 -

AFN: Roughness City (0.33) City (0.33) -

Blinds: setpoint for closings - -

Blinds: schedule for closing 09.00 till 20.00 09.00 till 18.00 0

Blinds: blinded window area 100% 100% -

IHG: number of people living room 4 4 -

IHG: number of people bedrooms 2 2 -

IHG: Schedules people Simplified Method Sorgato -50

IHG: lighting in living room 6.0 W/m² 5.0 W/m² -

IHG: lighting in bedrooms 5.0 W/m² 5.0 W/m² -

IHG: Schedules lights Simplified Method Sorgato +5

IHG: equipment in living room 1.5 W/m² 54 W -20

IHG: Schedules equipment 00.00 till 24.00 00.00 till 24.00 -

If we add up the effect on the number of degree hours overheating, the results from the Baseline Scenario should be similar to the Sorgato model in EnergyPlus. But figure A3 shows differences around 450 degree hours between the Baseline model and the Sorgato model. Therefore, the differences in the wall and roof constructions between the Baseline model and the Sorgato model is studied.

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The constructions in both models are made of hollow brickwork or mixed layer with hollow profiles. Sorgato modeled this in EnergyPlus by adjusting the thickness and density of the layer to keep the other properties, conductivity and specific heat, constant. In the Baseline Scenario the specific heat and thickness are kept constant and thus the conductivity and density of the layer changes. The differences in input are shown in table A3, including the effect in the number of degree hours.

Table A3

Input of construction in Baseline Scenario and Sorgato.

Description Baseline Scenario Sorgato Effect in GHR

Wall 1.80 W/m²K Hollow bricks 190 mm Hollow bricks 190 mm

-450 Thickness 190 mm 82 mm Conductivity 0.56 W/m²K 0.90 W/m²K Density 749 kg/m³ 868 kg/m³ Specific Heat 920 J/kgK 920 J/kgK Solar absorptance 0.4 0.4

Roof 1.92 W/m²K Mixed Layer 120 mm Mixed Layer 120 mm

Thickness 120 mm 95 mm

Conductivity 1.33 W/m²K 1.05 W/m²K

Density 860 kg/m³ 1087 kg/m³

Specific Heat 920 J/kgK 920 J/kgK

Solar absorptance 0.4 0.4

For the external wall there is a difference in the thermal resistance (RT) of the hollow bricks layer. In the

Baseline model the RT is 0.34 m²K/W while in Sorgato model the RT is 0.01 m²K/W. For the mixed layer in the

roof the RT is 0.09 m²K/W in both models. But for the roof Sorgato uses ana air gap with a RT of 0.21 m²K/W

while the Baseline model uses an air gap with a RT of 0.28 m²K/W. Trends in EnergyPlus

Validation of the apartment building and additional validation of the detached houses is done by showing trends in indoor temperature, solar radiation, natural ventilation and internal heat gains on the coldest and warmest day of the climate in Florianopolis. The following figures show the result of these trends for the master bedroom in the large detached house in the Baseline scenario for natural ventilation.

Figure A3: Trends of temperature and energy in bedroom of large detached house on a summerday.

The trends on the summerdays shows that the outdoor temperature (dark blue) fluctuates between 24°C and 35°C, while the indoor temperature (red) fluctuates between 24°C and 28°C. The exterior solar incident (purple) on the envelope shows high up to 1,000 Watt, but the solar heat gain (light blue) indiocates that the

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blinds block the majority of the solar heat. The ventilation heat loss indicates that the window opens when the indoor temperature is higher than the outdoor temperature. The internal heat gain (orange) show a constant pattern starting in the evening.

Figure A4: Trends of temperature and energy in bedroom of large detached house on a winterday.

The trends on the winterdays show fluctuations between 5°C and 15°C for the outdoor temperature (dark blue) and between 15°C and 18°C for the indoor temperature (red). The solar incident (purple) on the envelope still shows peaks up to 1,000 Watt. But the Solar heat gain (light blue) indicate that the blinds are not closing. This is according the schedule for the blinds, which operates between September 21st_{and March 20}th_{. The}

ventilation heat loss (green) is very low because the windows are closed. These losses consist of crackflow, which is between 0.1 dm³/s and 1.9 dm³/s.

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B. Results detached houses

This appendix starts with an overview of the three performance indicators for ech of type of the detached houses and the mean of the detached houses which are also shown in the paper. The overview is followed by the results for the labels, performance indicators and sensitivity to input parameters per dwelling type, to show the results in more detail.

B.1. Overview

The following figure show the results of overheating in the small, medium and large detached houses. The figure also show the results for overheating for the mean of the detached houses, which is used in the paper.

Figure B1: Overview of number of degree hours overheating in detached houses.

Figure B1 shows us that the Simplified Method overestimates the number of degree hours compared to EnergyPlus for the small, medium and large detached houses. This results in overestimation in the Detached Houses. The overestimation in terms of spread lies between 0 and 4,500 in the Simplified Method, while between 0 and 2,000 according to EnergyPlus. But overestimation also occurs in the mean, with 1,500 in the Simplified Method versus 800 according to EnergyPlus.

The differences in the results are the highest for the small detached house. In this case the spread of number of degree hours is below 1,500 according to EnergyPlus, while the spread is up to 4,500 in the Simplified Method. The mean is around 1,800 in the Simplified Method and around 800 according to EnergyPlus.

The results from the simpliffied method show a rising trend in degree hours while moving from the large detached house to the small one. This trend is not supported by EnergyPlus, where the medium detached house had the highest spread and mean of number of degree and the small detached houses the lowest. The following figure show the results of heating in the small, medium and large detached houses. The figure also show the results for heating for the mean of the detached houses.

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Figure B2: Overview of energy consumption for cooling in detached houses.

The results in figure B2 shows that the energy consumption for heating is overestimated by the Simplified Method compared to EnergyPlus. This overestimation consist of the range of the results, which lie between 8.0 and 18.0 kWh/m²·a in the Simplifies Method and between 0.0 and 8.0 kWh/m²·a according to EnergyPlus. Similarity between the results is the relative spread of the results. In both method the results lie within 10.0 kWh/m²·a.

Another interesting trend is the increasing energy consumption in EnergyPlus while moving from the large to the small detached house. This trend is not visible in the Simplified Method. The Simplified Method show a slightly lower energy consumption in the small detached house.

The results show that 25% of the results from EnergyPlus are zero. This is due to the HVAC schedule in which no heating is available in the residences. Although this might lead to less comparable results, this does represent reality in Brazilian residences and the other 75% is still far below the results of teh Simplified Method.

The following figure show the results of cooling in the small, medium and large detached houses. The figure also show the results for cooling for the mean of the detached houses.

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Figure B3: Overview of energy consumption for cooling in detached houses.

The results of energy consumption for cooling show that the Simplified Method have comparable results in the absolute energy consumption. But the spread of the results from EnergyPlus are much higher, therefore the Simplified Method represents a minor part of the results from EnergyPlus. Regarding the results of the combined detahced houses, the range of the Simplified Method is between 3.0 and 16.0 kWh/m²·a, while the range in EnergyPlus is 2.0 and 34.0 kWh/m²·a.

An explanation of the differences in spread can be the representation of reality in EnergyPlus, instead of fixed input in the Simplified Method. Therefore extra input parameters with variations are included in EnergyPlus compared to the Simplified Method. It seems these extra input parameters have substantial influence on the energy consumption. This will be shown by the Sensitivity analyses. An example of these extra input parametrs with variations is the a scenario with availability of cooling 24 hours a day in EnergyPlus instead of only at night in the Simplified Method. But also a scenario with availability of cooling in the living room, while not available in the Simplified Method.

The results from the Simplified Method show a general increase in energy consumption while shifting from the large detached house to the small one. This trend is not supported by the results of EnergyPlus.

B.2. Large detached house

The first of three cases is a large detached house with a total floor area of 150 m². It consists of a living room, three bedrooms, a kitchen, two bathrooms and a garage [17]. This case represents large detached houses in Brazil which accounts for 16.5% of the housing stock in the state of Santa Catharina [14]. The following figure shows an impression and the floorplan of this large detached house.

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Figure B4: Impressions and floorplan of large detached house of 150 m².

Representation by energy labels

The first part of the assessment is the representation of energy efficiency by energy labels. Therefore the following figure show the frequency of the energy labels per category in case of natural ventilation and airconditioning for both methods.

Figure B5: Results of energy labels for natural ventilation and air-conditioning in the large detached house.

The figure for natural ventilation shows that all the samples in EnergyPlus get a label A or B. For the Simplified Method this is not the case. The majority of the samples get a B-label but a small amount get a C-label and to a lesser extent D-label. These results show that the Simplified Method is understimating the energy efficiency of natural ventilation compared to EnergyPlus. The result for airconditioned houses show a wider distributed labels for EneryPlus, while most of the Simplified Method results are B-label. This result indicates overestimation of the efficiency by the Simplified Method compared to EnergyPlus.

Role of overheating, heating and cooling

The assessment continues by comparing the results of the performance indicators for overheating, heating and cooling. The performance indicators play an important role in the energy labels.

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Figure B6: Results of performance indicators for overheating, heating and cooling in the large detached house.

The comparison of the number of degree hours show that the Simplified Method show generally higher results than EnergyPlus. This contributes to the underestimation of the labels by the Simplified Method compared to EnergyPlus. Especially, due to the influence of 64% on the label for the location of this research, Florianopolis. The comparison of the energy consumption for heating shows that the Simplified Method is overestimating energy consumption for heating compared to EnergyPlus. This trend also contributes to the underestimation of the energylabel by the Simplified Method in the case of natural ventilation. But this should also contribute to underestimation of the label in the airconditioned case.

The comparison for the energy consumption for cooling shows a different trend. Comapred to EnergyPlus, the Simplified Method underestimates the energy consumption for cooling. Another interesting result is the difference in spread. Where the Simplified Method lies between 0 and 15 kWh/m² and EnergyPlus lies between 0 and 30 kWh/m². This contributes to the result of low spread in the energylabels in case of Simplified Method. Sensitivity to input parameters

The last step in the assessment of the simplified method is the comparison of sensitivity of the performance indicators to the variations in the input parameters.

Number of degree hours overheating (GHR)

The s of the input parameters is indicated by the Spearman Correlation Coefficients (SCC). These coefficients of the input parameters in relation to the output is shown in figure B7.

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Figure B7: Sensitivity of overheating to input parameters for the simplified method and simulations.

Regarding the results from the Simplified Method we see that Solar absorptance shows the highest correlation, followed by the thermal capacity and the shaded window fraction and openable window fraction. Comparing these results to the results from EnergyPlus, we find that solar absorptance has the highest influence, followed by the shaded window fraction and openable window fraction on a distance. EnergyPlus indicates a low correlation between thermal capacity and the number of degree hours. This is in contrast to the Simplified Method indicating it as the parameter with the second highest correlation.

EnergyPlus indicates that Terrain is also important input parameters for overheating. The roughness of the terrain is a so called physical parameter on which little influence is possible to change this. It will be build in the city, suburbs or countryside.

Energy consumption for heating (CA)

The influence of the input parameters is indicated by the Spearman Correlation Coefficients (SCC). These coefficients of the input parameters in relation to the output is shown in figure B8.

Figure B8: Sensitivity of heating to input parameters for the simplified method and simulations.

The results from the Simplified Method show that Solar Absorptance has the highest influence on energy consumption for heating. It is followed by Thermal Capacity and U value of the Walls. Comparing this to results from EnergyPlus we see that Solar Absorptance and U value of the Walls have considerable influence. Another noticeable result is the negative influence of the openable window fraction in the Simplified Method, while having a positive influence according to EnergyPlus.

EnergyPlus indicates the setpoint for heating and the HVAC schedule as the most important parameters for energy condumption for heating. These are not included in the Simplified Method because both are scenario parameters. In other words, their variation depends on the occupants behaviour and changes during the

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lifetime. Perhaps the setpoint in design guidelines for HVAC systems can be discussed and revised, based on this result.

Energy consumption for cooling (CR)

The influence of the input parameters is indicated by the Spearman Correlation Coefficients (SCC). These coefficients of the input parameters in relation to the output is shown in figure B9.

Figure B9: Sensitivity of cooling to input parameters for the simplified method and simulations.

The results from the Simplified Method show that Solar Absoprtance and Thermal Capacity are the most important parameters for energy consumption of cooling. Followed by the Shaded Window Fraction. Comparing these results to the results from EnergyPlus we see that Solar Absorptance has major influence, but Thermal Capacity and Shaded window fraction have little influence. The main reason for Shaded Window fraction is that cooling is designed for operating at night, to cool the bedrooms. At these hours, the solar heat gains are low and have little influence. The low influence of thermal capacity is quite similar, but slightly different. Normally, thermal capacity should cool down at night by using night ventilation. But then the bedrooms are occupied and the windows stay closed. At daytime the windows can open, but then outside temperature is high leaving the windows closed.

EnergyPlus indicates the setpoint for cooling as the most important parameter, followed by the occupancy of the bedroom and Solar Absorptance. Interesting is that Solar Absorptance has high influence while other solar heat related have not. The main reason for this could be that solar heat gain from solar absorptance comes later in time, due to thermal capacity of the external wall. Shaded window factor and orientation deliver direct solar heat gains. The reslut for the number of people in the bedroom is also interesting. At night hours this source of internal heat gain is present while other heat sources are not. Therefore having direct effect on the indoor temperature and the energy for cooling. The setpoint for cooling is the most important parameter according to EnergyPlus. This parameter determines when the cooling is turned on and off. In order to have effect on lowering energy consumption for cooling, the setpoint should be discussed in design guidelines for HVAC systems. Also advertisement about the positive effects of increasing the setpoint is good recommendation.

B.3. Medium detached house

The following case in the numerical assessment is a medium detached house with a floor area of 63.0 m². The house consists of a living room, two bedrooms, a kitchen and a bathroom [17]. This case represents medium size detached houses in Brazil which accounts for 42.1% of the housing stock in the state of Santa Catharina [14]. The following figure shows an impression and the floorplan of this small detached house.

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Figure B10: Impressions and floorplan of medium detached house of 63.0 m².

Representation by energy labels

The first part of the assessment is the representation of energy efficiency by energy labels. Therefore the following figure show the frequency of the energy labels per category in case of natural ventilation and airconditioning for both methods.

Figure B11: Results of energy labels for natural ventilation and air-conditioning in the medium detached house.

The figure for natural ventilation shows that all the samples in EnergyPlus get a label A or B. For the Simplified Method this is not the case. Most samples get a B-label but a considerable amount get a C-label and to a lesser extent D-label. These results show that the Simplified Method is understimating the energy efficiency of natural ventilation compared to EnergyPlus. The result for airconditioned houses show a wider distributed labels for EneryPlus, while most of the Simplified Method results are B-label. This result indicates overestimation of the efficiency by the Simplified Method compared to EnergyPlus.

Role of overheating, heating and cooling

The assessment continues by comparing the results of the performance indicators for overheating, heating and cooling. The performance indicators play an important role in the energy labels.

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Figure B12: Global uncertainty in number of degree hours overheating in medium detached house.

The comparison of the number of degree hours show that the Simplified Method show generally higher results than EnergyPlus. This contributes to the underestimation of the labels by the Simplified Method compared to EnergyPlus. Especially, due to the influence of 64% on the label in the loaction of this research, Florianopolis. The comparison of the energy consumption for heating shows that the Simplified Method is overestimating energy consumption for heating compared to EnergyPlus. This trend also contributes to the underestimation of the energylabel by the Simplified Method.

The comparison for the energy consumption for cooling shows a different trend. Comapred to EnergyPlus, the Simplified Method underestimates the energy consumption for cooling. Another interesting result is the difference in spread. Where the Simplified Method lies between 0 and 20 kWh/m² and EnergyPlus lies between 0 and 40 kWh/m². This contributes to the result of low spread in the energylabels in case of Simplified Method. Sensitivity to input parameters

The last step in the assessment of the simplified method is the comparison of sensitivity of the performance indicators to the variations in the input parameters.

Number of degree hours overheating (GHR)

The influence of the input parameters is indicated by the Spearman Correlation Coefficients (SCC). These coefficients of the input paranmeters in relation to the output is shown in figure B13.

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Figure B13: Sensitivity of overheating to input parameters for the simplified method and simulations.

Regarding the results from the Simplified Method we see that Solar absorptance show the highest correlation, followed by the thermal capacity and the shaded window fraction and openable window fraction. Comparing these results to the results from EnergyPlus, we find that solar absorptance has the highest influence, followed by terrain and shaded window fraction and openable window fraction on a distance. EnergyPlus indicates a low correlation between thermal capacity and the number of degree hours. This is in contrast to the Simplified Method indicating it as the parameter with the second highest correlation.

EnergyPlus indicates that the Setpoint Open Windows, Terrain and the schedule open windows are also important input parameters for overheating. The Setpoint open windows is ofcourse controlled by the occupants and will differ during the course of its lifetime. The roughness of the terrain is a so called physical parameter on which little influence is possible to change this. It will be build in the city, suburbs or countryside. The Schedule Open Windows is a so called scenario parameter on which the building occupants have influence and will differ during the course of its lifetime.

Energy consumption for heating (CA)

The influence of the input parameters is indicated by the Spearman Correlation Coefficients (SCC). These coefficients of the input paranmeters in relation to the output is shown in figure B14.

Figure B14: Sensitivity of heating to input parameters for the simplified method and simulations.

The results from the Simplified Method show that Solar Absorptance has the highest influence on energy consumption for heating. It is followed by Thermal Capacity and U value of the Walls. Comparing this to results from EnergyPlus we see that Solar Absorptance and U value of the Walls have considerable influence. Another noticeable result is the negative influence of the openable window fraction in the Simplified Method, while having a positive influence according to EnergyPlus.

Thesis Building performance simulation to support building energy regulation. A case study for residential buildings in Brazil.

Thesis

Building performance simulation to

support building energy regulation.

A case study for residential buildings in Brazil.

Author

A.N. van der Knaap

Student ID 0596915

Date

December 21

2011

Study

Master Building Services

Department of the Built Environment

Eindhoven University of Technology

Supervisors

prof.dr.ir J.L.M. Hensen

_______________________

dr.ir. M.G.L.C. Loomans

_______________________

dr. Daniel Cóstola

_______________________

Building performance simulation to support

building energy regulation.

A case study for residential buildings in Brazil.

A.N. van der Knaap

Master Building Services

ABSTRACT

Contents

1. Introduction

2. Methods

3. Results of assessing the Simplified

Method

4. Discussion

5. Conclusions

Acknowledgements

List of abbreviations

References

A. Validation of Simplified Method and simulation models

B. Results detached houses