2nd Skin approach to zero energy rental properties: Occupancy patterns to improve energy simulation

2

SKIN APPROACH TO ZERO ENERGY RENTAL PROPERTIES:

OCCUPANCY PATTERNS TO IMPROVE ENERGY SIMULATION

Olivia Guerra-Santin TUDelft, Industrial Design Engineering o.guerrasantin@tudelft.nl Sacha Silvester TUDelft, Industrial Design Engineering s.silvester@tudelft.nl Thaleia Konstantinou TUDelft, Architecture t.konstantinou@tudelft.nl

Figure 1. The 2ndSkin solution

WHICH ARE YOUR ARCHITECTURAL (R)SOLUTIONS TO THE SOCIAL, ENVIRONMENTAL AND ECONOMIC CHALLENGES OF TODAY?

Research summary

A number of second skin solutions have been developed in recent years to solve the problem of large scale renovation of housing. The 2ndSkin approach presented in this paper is currently under development by a consortium of academic and industry partners in the Netherlands. The objective of the 2ndSkin project is to develop a strategy for an integrated and effective renovation solution. This approach aims at developing a market-ready zero-energy solution that can be applied to rental apartment blocks in the Netherlands. The project will develop a process for post-occupancy monitoring and evaluation to provide feedback to users to ensure the zero-energy target. The 2ndSkin approach seeks a zero energy target regardless of the user. Thus in this approach, both building– related and user-related energy consumption are considered within the strategy. The objective is a solution that is zero energy regardless of the type of occupancy of the building. The strategy also aims at developing an integrated approach in which the user is part of the renovation strategy from early stages of the development in order to increase the acceptability of the renovation process and the understanding of the functioning of the systems. In addition, The WoON 2012 Dutch database was used to determine occupancy profiles for building simulation. Through statistical analysis, seven household types have been identified. Seven different occupancy profiles, based on the household types and seven occupancy factors created through Factor Analysis have been defined. Results of the pattern compositions are presented. The results of this investigation aim to inform the design process.

1. Introduction

A number of facade solutions have been developed in recent years to solve the problem of large-scale renovation of housing. The 2ndSkin approach presented in this paper is currently under development by a consortium of academic and industry partners in the Netherlands. The study addresses the challenges of refurbishment the existing building stock and is sponsored by the EU Climate-KIC’s flagship Building Technology Accelerator (BTA) project and the Dutch TKI/Energy program.

The renovation of the building stock has a large potential to save energy and to reduce carbon emissions, given the large percentage of buildings constructed before the introduction of energy regulations. The building stock in the Netherlands accounts for 7.5 million dwellings (CBS, 2014). Dwellings of the post war period account for approximately 1/3 of the residential stock (Itard & Meijer, 2008), out of which 1.3 million are social housing (Platform31, 2013). Housing associations are important stakeholders in this context. There are approximately 400 housing associations in the Netherlands that manage 2.4 million residential properties, constituting 34% of the total housing stock (Aedes, 2013). A large amount of those properties are in need for renovation. Front-running housing associations have the ambition to achieve an energy-neutral renovation approach. Currently, the average energy-use for the post-war building according to AgentschapNL (2011) is approximately 350-400 kWh/m2_{/year primary energy. Therefore,} the renovation of omnipresent post-war buildings offers great potential for carbon reductions. However, there is a lack of fast, affordable and robust processes for large-scale building renovation. This problem is magnified in multi-family rented buildings, in which the incentives for saving energy and increasing indoor comfort are split between owners and

tenants. The challenges of the 2ndSkin project are manifold:

1) Design of a refurbishment strategy for

facades integrating building systems for heating, ventilation and energy generation.

2) Investment and financing of low carbon

technologies.

3) Uncertainties related to energy savings and

payback periods.

4) Occupants understanding of the innovative

systems for building control.

5) The influence of building operation and

occupant behavior in the efficiency of the building.

6) Acceptance of the owners (housing

associations) and residents (tenants) of the renovation process

These challenges are addressed in the two main aspects in this investigation: the technical solution and the process solution.

Technical solution

The 2ndSkin process differs from conventional renovation process in the fact that the technology is seen as independent from the underlying structure of the building, and integrated into the facade. The 2ndSkin integrates heating, ventilation and cooling systems into the skin so it can be easily accessible from the outside of the building, therefore facilitating the maintenance. Photovoltaic panels and solar boilers are also integrated in the skin in order to reach the zero energy targets. The flexibility of the system and the accessibility from the outside allows upgrading the installations in further phases of the development during the lifetime of the building, thus increasing the time-span of the initial investment, which could help reaching final users with lower rents or living costs. The flexibility of the system will also allow its customization for different types of building archetypes, for different countries and for different climate zones.

Process solution

The 2nSkin process should be implemented fast and easily, limiting the nuisance for the occupants, and thus allowing them to remain in their houses during the installation process. Keeping occupants in their homes is needed to decrease the costs of the renovating process related to the relocation of occupants, and it will facilitate the acceptance of the occupants to take part in renovation processes.

In addition, this 2ndSkin project goes further that others by establishing an user-proofed zero energy target. The 2ndSkin strategy aims at developing and integrated approach in which the user acceptance (both occupants and owners) are part of the renovation strategy from early stages in the development, and in which post-occupancy monitoring and evaluation can provide the users with the right feedback to further decrease their energy consumption. This 2ndSkin approach aims at developing a solution that can be applied to low-density rental apartment blocks in the Netherlands. This type of building was selected because of two main reasons: 1) the percentage of this type of building stock represents a good market, and 2) the complexities of the tenure structure.

The goal of the 2ndSkin project is to develop a methodology for a zero energy approach in renovation projects. This paper shows the first insights into the relation between the ambitions of zero-energy and the different groups of occupants and their energy-related patterns. The results if this investigation will be used to inform the design process regarding the amount of energy production required to reach a zero energy solution, and the effect of specific installation types in the design. Figure 2 shows the technical solution of the 2ndSkin project.

2. Approach

The definition of the zero energy approach is very important in this project, since both

building-related and user-related energy consumption should be considered within the strategy. The objective of 2ndSkin is a solution that is zero energy regardless of the occupancy of the building. For the 2ndSkin project, two possible approaches are investigated: 1) the zero-on-the-meter approach, and the 2) energy neutrality. Both approaches have advantages and disadvantages. The zero-on-the-meter approach consists on a concept in which, on a yearly base, the energy meter is on zero, meaning that the energy produced equals the energy used. This approach is favored by industry and it is easy to quantify and evaluate. However, recent research has shown that occupancy has a large influence on energy consumption, and household energy requirements vary greatly in very similar dwellings and even in households of similar composition (Guerra-Santin & Itard, 2010). Therefore, the zero-on-the-meter target will be only valid for some types of households, namely households that use and average energy consumption. To ensure that all of the apartments in the complex will be zero-on-the-meter in a given period of time will be risky, especially considering that 2ndSkin focuses on social rental properties.

Taking into account the differences on occupancy and the great effects of occupant behavior, especially on electricity usage, a different zero-energy approach could be used instead. This approach would consider the total apartment building as the evaluation unit, instead of a single apartment. In this manner, the higher energy requirement of some households would be compensated on the lower energy requirements of other households. In addition, in this approach, the performance could be evaluated in terms of primary energy consumption. Both approaches (zero-on-the-meter and energy neutrality) will be evaluated in the project to determine the

more suitable approach for the business case. This is however, out of the scope of this paper. 3 Defining occupancy profiles: household

type, reference users and occupancy patterns

Because of the great differences in energy-use between households in the same situation, it is very important to get clear insights into the relation between type of usage or occupancy and energy-use. These insights will help to better predict the feasibility of energy-neutrality and reduce the risks of unexpected energy bills.

This part of the study consists on the definition of the household types and corresponding occupant profiles and occupancy patterns. Firstly we define household types as the set of characteristics such as background and socio-economical variables that tend to prevail on a national sample. Secondly the reference users are defined as the typical user of the building to be refurbished and will consist on a number of specific household types, including socio-economical variables such as education level, background, income and lifestyle. These are the aspects that could vary greatly between rental and owned properties. Thirdly the occupancy patterns are defined in this study as a set of building operation and use patterns, for example heating patterns, ventilation patters and presence at home. Fourthly, occupancy profiles are defined as the specific occupancy patterns followed by a determined household type.

The main goal of the occupancy profiles is to determine the intensity on the use of the building and the installations and appliances. These profiles could be used for building simulation or to determine statistically the effect of occupant behavior on energy use. Figure 1 represent visually the relation and differences between these four concepts.

In the 2ndSkin project, the occupancy patterns are used to calculate the expected energy consumption. Different combination of

occupancy profiles (household types with different occupancy patterns) will be studied to determine worst case and best case occupancy scenarios, as well as average scenarios. The occupancy scenarios will be examined to determine whether the zero energy target is reached in all cases. The occupancy scenarios are out of the scope of this paper. The results will be compared with the results form a common approach to calculate energy consumption (i.e. using an ‘average’ household). These results would indicate how realistic is the zero energy target for the reference building, and what would be the legal, marketing and business models for the zero energy 2ndSkin project.

4 Household types

Statistical analyses were used to determine the types of households more likely to live in the reference building than in other types of buildings in the Netherlands. For the analysis, the WoON dataset was used. The dataset contains data on building characteristics, demographic characteristics and occupant behavior for a random sample of households in the Netherlands. The use of the WoON dataset has previously proved useful to study occupant behavior in residential buildings (Guerra-Santin, 2011). The dataset was used to determine the households’ types in relation to their size, composition, age and the absence or presence of seniors and children, important variables on energy consumption (Guerra-Santin & Itard 2010). Eleven types of households were identified in the sample. Four groups were too small in the sample and therefore were not further studied. Table 1 shows the descriptive statistics of the groups. ANOVA tests of variance were conducted to investigate the relation of these types of households with electricity, gas and water consumption. The results showed that gas consumption (F(6,16080)=659.1, p<0.001 welch statistic), electricity consumption

(F(6,16059)=3054.8, p<0.001 welch statistic) and water consumption (F(6,15546)=73059.5, p<0.001 welch statistic) are statistical significantly different for the seven types of households.

Household type %

1 senior (> 65 years old) 9.6 1 adult (18 to 64 years old) 16.7 2 adults (partners or not) 20.1 2 adults, at least 1 senior 11.9

3 to 4 adults 14.3

5 or more adults * 3.5 3 or more adults, at least one senior * 0.7 1 adult and one or more children 3.1 2 adults and 1 to 3 children 18.8 2 adults with more than 4 children * 1.1 3 or more people, children and senior * 0.1

Table 1. Descriptive statistics of households types (*eliminated households)

A Chi-square test was used to determine the prevalence of specific types of households in the reference building. The WoON dataset was then split into a sub-dataset containing only the cases of building similar to the reference building. The sub-dataset contains 2194 cases. The characteristics are: low rise (three to five levels) rental apartments built between 1946 and 1975. The Chi-square test showed that the households more likely to inhabit the reference buildings are: ‘single senior’, ‘single adult’ and ‘single parent household’; while the households less likely to inhabit the reference buildings are ‘three to four adults’, ‘nuclear family, ‘two adults’ and ‘two seniors’ (χ2_{(6)=1231.97, p<0.001).}

A second ANOVA test was carried out on a subset of the dataset containing only the cases determined as reference buildings. The results showed that gas consumption (F(6,538)=10.7, p<0.001 welch statistic), electricity consumption (F(6,536)=39.5, p<0.001 welch statistic), and water consumption (F(6,528)=4200.4, p<0.001 welch statistic) are statistical significantly different for different types of households. However the differences between the household types in the reference buildings are not as large as in the complete

building stock. These results suggest that in the reference building, occupant behavior might have a smaller effect than in other type of buildings. This could be caused by the fact that all social rental apartments have similar characteristics, and by the fact that the households in these apartments tend to have lower incomes. For the 2ndSkin strategy this is an important finding since it means that the differences on energy consumption between households is not too large, and so facilitating the division of the energy generated in the complex among the corresponding apartments.

5 Occupancy patterns and occupancy profiles

Seven different household types have been described (see table 1). In this section we define the occupancy patterns that these household types are more likely to follow. We define occupancy patterns as the use of heating and ventilation system, opening windows, preferences for temperature settings, and presence at home. To define the occupancy patterns, the complete dataset was used, assuming that households would have the same schedules and indoor temperature preferences. The advantage is that we have a much larger dataset for the analysis.

Factor scores Variable contributing

1 Presence home

F(6, 4695)= 35.37, p<.001 From 6-9, From 9-12, From 12-15, From 15-18, From 18-23, From 23-6

2 Temperature day

F(6, 4695)= 47.96, p<.001 From 6-9, From 9-15, From 15-18, From 18-23 From 23-6

3 Bedroom radiators

F(6, 4695)= 3.74, p=.001 Main bedroom, Other bedrooms 4 Ventilation

F(6, 4695)= 1.80 NS Ventilation in living room, and In other rooms 5 Curtains, savings, stove

F(6,4695)= 79.04, p<.001 Saving measures, Curtains, Stove 6 Temperature setback

F(6, 4695)= 12.10, p<.001 From 6-9, From 23-6, Nobody home 7 Other rooms radiators

F(6, 4695)= 37.04, p<.001 Kitchen, Bathroom, Office

Table 2 Factors analysis – variables contributing to each score and scores per household type

In order to link statistically the type of households to occupant behavior, we needed to reduce the number of behavioral variables. Factor analysis was carried out with the behavioral variables following the method described in (Guerra-Santin, 2011). A solution with 7 factors was chosen. The results have been summarized on Table 2. The first column of the table shows the name given to each of the seven factors. The second column shows the behavioral variables making up each of the factors.

An ANOVA test was carried out between the factor scores and the household types (first column of Table 2). All Factors were statistically significant different between household groups, except for Factor 4: ventilation. Previous studies have also failed to find statistical correlation between ventilation habits and household types. To tests the Factors created, Pearson correlation tests between the factors and energy and water consumption were carried out (Table 3). The results showed that the ventilation factor is not correlated to energy consumption, which is in line with results from previous studies (Guerra-Santin & Itard, 2010). The rest of the factors were correlated with gas consumption, and all but temperature day and radiators in bedrooms with electricity and water consumption. The third and fourth column of Table 2 shows the household types scoring lower and higher on each Factor. This information is used to identify the intensity on the use the building (thermostats setting, use of radiators, ventilation, presence).

6. Definition of patterns for building simulation

The building simulation program used is Bink (Binksoftware.nl). In the program, specific heating patterns per day, week, month or year can be defined, as well as the presence of people, heat gains and artificial lighting and appliances use in each room. In addition, it is

possible to define whether the radiator in a room is open, semi-open or closed in each room by modifying the radiator capacity. From previous studies, we know that in Dutch houses, the radiators are usually left closed or half open on the least used bedrooms (Guerra-Santin & Itard 2010). The program does not allow the specification of the ventilation patterns; this will be the focus of further studies.

Occupancy patterns are defined for each household type based on their relation with each behavioral factor. For each household, the intensity of use per behavior was determined. A number of scenarios have been defined for each of the occupancy patterns (Table 4). The scenarios can be used to define: the presence in the dwelling, temperature settings, setback behavior, ventilation frequency, use of appliances and lighting. Table 5 shows that each household type has been specified with different occupancy patterns based on the results of ANOVA tests (see Table 2). These are the configuration of the occupancy profiles that will be used in the building simulation.

Factor scores Gas Electricity Water

1 Presence home .07** .1** .14** 2 Temperature day .09** .02 -.02 3 Bedroom radiators .05** .01 .03 4 Ventilation .03 -.01 -.02 5 Curtains, savings, stove .13** .16** .27** 6 Temperature setback .11** .13** .09** 7 Other rooms radiators -.27** -.19** -.15**

Table 3 Pearson Correlations between Factors and energy and water consumption (N=4790, ** p<.001)

7. Validation of occupancy patterns

Statistical analysis allows us to generalize the results. The WoON database is a random sample in the Netherlands with a large number or cases (70.000). However, it is necessary to validate the results given that there might be differences between the information that people fills in a questionnaire –as being applied in the WoON data gathering - and the actual behavior they have at home. In addition, the

2ndSkin approach should be able to be implemented (with some changes) in other countries in which such datasets are not available. Therefore, in order to validate the statistical patterns, or to determine the patterns in a country without statistical information, we can make us of monitoring data. In this case (for the prototyping phase of the 2ndSkin project), statistical data is used to build the occupants profiles and patterns. The validation of the patterns is then tested with two monitoring case studies, one in the Netherlands and one in Spain. With the validation, we aim at testing the methodology only since a validation in the complex selected for prototyping the 2ndSkin approach in the Netherlands was not possible yet because of the lack of access to the building. The validation of the patterns will be presented in a further paper.

8 Conclusions

Energy neutral refurbishment approaches are attractive. Not only from a CO2 _mitigation perspective but also from a financial point of view. For the acceptance by the end-user and the feasibility of the business cases of these refurbishment approaches it is important that uncertainty about the actual energy-use is minimized. Will the energy use be zero in practice? Today the differences in energy-use between the households are huge. You can hardly speak of an average household in this perspective. This is why it is important to get more insights in the relation between occupancy an energy-use.

In this research, occupancy patterns for energy consumption based on post-war rental apartments on the Netherlands were defined. Seven statistically defined household types were linked to occupancy patterns (use of building control). Factor analysis and ANOVAs were used to define the relationship between the household types and the occupancy patterns. From the seven types of households

and occupancy patterns linked to them, three were found to be more likely to occur in the studied reference building. Therefore, these will be used to simulate diverse combinations of apartment occupancy in the reference building.

The use of statistics to determine the occupancy patterns proved useful to define the occupancy of a building when real information about the occupants is not available due to the building renovation schedule, sensitive processes or when the building has already been emptied. This investigation focuses on post-war rental flats in the Netherlands, but our methods can be repeated to other type of buildings renovation projects in the Netherlands. The process could also be used in other countries provided that datasets containing information about household demographics, building characteristics and occupant behavior are available. This is a limitation if such information is not available. In this case, monitoring building occupancy pre-renovation could provide with required information.

9 References

AEDES. 2013. AEDES- Vereniging van woningcorporaties. Retrieved 20-05, 2014, from

http://www.aedes.nl/content/feiten-en-cijfers/feiten-en-cijfers.xml - corporation-system AgentschapNL. 2011. KOMPAS Energiecijfers. Retrieved

03-10, 2011, from

http://senternovem.databank.nl/ Bink software from http://binksoftware.nl CBS. 2014. Centraal Bureau voor de Statistiek.

Retrieved 20/02, 2011, from

http://www.cbs.nl/nl-L/menu/home/default.htm Guerra-Santin, O., 2011, Behavioural patters and user

profiles related to energy consumption for heating, ENB 43, 2662-2672.

Guerra-Santin O. & Itard, L., 2010, Occupants’ behaviour: determinants and effects on residential heating consumption, BRI 38(3), 318-338

Itard, L., & Meijer, F. 2008. Towards a sustainable Northern European housing stock (Vol. 22). Amsterdam: IOS.

Platform31. 2013. DOCUMENTATIE SYSTEEMWONINGEN ’50 -’75: Bouwhulp Groep.

PRESENCE

Based on the statistics of the household type. For each time period (e.g. from 6 to 9 am) the mean number of days present at home per household is used.

RADIATORS BEDROOM OFF – Radiators are off in bedrooms ON – Radiators are on in bedrooms TEMPERATURE

AVERAGE- Minimum and maximum set-point based on Mean and 1 degree lower and higher (defined per household type).

COOL –1 standard deviation below the mean is the minimum, the maximum is 2 degrees higher.

WARM – 1 standard deviation above the mean is the maximum, the minimum is 2 degrees lower.

RADIATORS OTHERS

OFF – Radiators are off in kitchen, bathroom, office ON – Radiators are on in kitchen, bathroom, office SETBACK

GOOD PRACTICE - Setback setting when nobody home and at night (from 23:00 to 6:00 hours)

WASTEFUL - No setback SAVING MEASURES

LESS – Less number of energy saving measures MORE - More number of energy saving measures

COOKING

LESS – No cooking or cooking few days per week MORE – cooking several days per week

Table 4 Scenarios for occupancy patterns and how are they defined

Presence Temperature Setback Ventilation Radiators

bedroom Radiators others Cooking Saving measures

1 senior More Warm Wasteful Higher rate Off Off Less Less 2 seniors More Warm Wasteful Lower rate Off On More More 1 adult Less Cool Setback Higher rate Off Off Less Less 2 adults Less Cool Setback Lower rate Off Off More More 3 or 4 adults Less Average Wasteful Lower rate Off On More More Single-parent Less Average Setback Lower rate On Off More More Nuclear family More Average Wasteful Higher rate On On More More

Table 5 Occupancy profile (based on household types, occupancy factors and occupancy scenarios