Occupant behaviour simulation for cellular offices in early design ...

BUILD SIMUL (2015) 8: 211 – 224 DOI 10.1007/s12273-014-0203-6

Occupant behaviour simulation for cellular offices in early design stages—Architectural and modelling considerations

School of Architecture and Built Environment, Deakin University, Geelong Waterfront Campus, 1 Gheringhap Street, Geelong VIC 3220, Australia

Abstract

Keywords

Building simulation is most useful and most difficult in early design stages. Most useful since the optimisation potential is large and most difficult because input data are often not available at the level of resolution required for simulation software. The aim of this paper is to addresses this difficulty, by analysing the predominantly qualitative information in early stages of an architectural design process in search for indicators towards quantitative simulation input. The discussion in this paper is focused on cellular offices. Parameters related to occupancy, the use of office equipment, night ventilation, the use of lights and blinds are reviewed based on simulation input requirements, architectural considerations in early design stages and occupant behaviour considerations in operational stages. A worst and ideal case scenario is suggested as a generic approach to model occupant behaviour in early design stages when more detailed information is not available. Without actually predicting specific occupant behaviour, this approach highlights the magnitude of impact that occupants can have on comfort and building energy performance and it matches the level of resolution of available architectural information in early design stages. This can be sufficient for building designers to compare the magnitude of impact of occupants with other parameters in order to inform design decisions. Potential indicators in early design stages towards the ideal or worst case scenario are discussed.

occupant behaviour,

1

Introduction

E-mail: [email protected]

building simulation, early design stages, cellular office, extreme case scenarios, modelling resolution

Article History Received: 11 February 2014 Revised: 25 September 2014 Accepted: 10 October 2014 © Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2014

question in this context is what level of resolution and complexity of these patterns and formulas is required for different stages of the building process. The architectural profession defines the process of developing a building in stages from pre-design to completion. A country’s Institute of Architects typically defines the number of stages within the process, their deliverables and the related fee structure for architects. Typically there is a pre-design stage, where the architect and the owner determine the circumstances, budget and constraints of the project and develop the architectural design brief and aesthetic design intent. This is followed by the sketch design stage, where the architect develops a schematic architectural concept, that expresses the intended architectural “character”, basic functionality and site integration of the project. Once this is agreed upon, additional stages of design development follow, where the project is refined. This paper focuses on the first two design stages, the pre-design stage and the

Architecture and Human Behavior

In the context of the climate change, a major research focus among built environment professionals is to identify parameters that play an important role in the reduction of greenhouse gas emissions related to buildings. The behaviour of occupants in buildings is one of these parameters, and the lack of consideration of occupants in comfort and energy performance predictions has been identified as one reason why predicted and actual building performance often deviate significantly. Several studies are now trying to close this knowledge gap by attempting to translate the complexity of human behaviour into behavioural patterns which can then be represented by mathematical algorithms for use in calculations and simulations (Haldi and Robinson 2009; Andersen et al. 2013; Schweiker et al. 2012; Gunay et al. 2014; Peng et al. 2012; Hong and Lin 2012; Fabi et al. 2012). One important

Research Article

Astrid Roetzel ()

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sketch design stage, in which the environmental optimisation potential is largest. Both stages together are referred to in this paper as “early design stages”. In terms of occupant behaviour modelling, early design stages are particularly challenging since modelling software requires input with a higher level of resolution than the design process can provide yet. In order to run the simulation, assumptions have to be made and this paper aims to provide information that can help to improve modelling assumptions in early design stages. Occupant behaviour for this paper is related to cellular offices and focused on occupancy and the use of office equipment, natural ventilation, control of lights and control of blinds. These parameters are analysed with regards to relevance for building simulation, relevance for building designers in early design stages and considerations for occupants in operational stages. The discussion aims to be not limited to the use of particular modelling software but is influenced by the authors experience with EnergyPlus (EnergyPlus 2007). 2 Occupant behaviour modelling in early design stages Building simulation can help optimise environmental building performance significantly, and the optimisation potential is largest in early design stages, when all major decisions concerning the architecture of the building and its use are made. For the same reason, however, it can be very challenging to get the simulation input right, since at the time of modelling major building properties might not have been decided upon or are still subject to change. It is therefore important to choose a level of resolution or complexity of the simulation model that is appropriate to the aim of the investigation (Hensen and Lamberts 2011; Hoes et al. 2009; Hong and Lin 2012). In this context Hoes et al. (2009) suggested three levels of resolution, the first being simple user behaviour (based on averages or minima/maxima), the second considering the interaction between the user and its environment, and the third adding a complex mobility prediction. With regards to EnergyPlus, Hong and Lin (2012) suggested three different methods to model occupant behaviour depending upon the complexity; via the EnergyPlus interface, via the Energy Management System, and the use of a modified code of EnergyPlus. In the context of the different stages of a building design process, it is important to differentiate between early and final design stages. The aim of simulations in final design stages is typically an accurate prediction of the expected performance which allows for minor design adjustments and comparison with performance measurements. Due to the large uncertainty of building design variables (Jin and

Roetzel / Building Simulation / Vol. 8, No. 2

Overend 2014), the aim of building simulation in early design stages is not so much the accurate determination of the buildings future energy consumption, but the ranking of different design and occupant related variables according to their magnitude of influence. An architect’s design process is centred on the establishment of design priorities based on quantitative as well as qualitative parameters. Performance criteria are important, but not the only parameters influencing design decisions. In early design stages simulation results are therefore most useful at the level of resolution of “ballpark figures”, as they will be evaluated in the preliminary design process alongside with other quantitative as well as qualitative parameters. With regards to occupants, the definition of different occupant scenarios can be useful. Hong and Lin (2012) tested three behaviour types for single occupancy cellular offices, 1) austerity—occupants are proactive in saving energy, 2) standard—average occupants, and 3) wasteful—occupants do not care about energy use, and observed a huge impact on related energy consumption. Peng et al. (2012) investigated three typical lifestyles in a residential context in China, 1) energy conscious, 2) habit related and 3) high quality and also reported significant impact on building performance and energy use. These behavioural lifestyles were related to (1) ideology to behavioural principles; (2) human physiological, psychological and economics feelings; (3) impact of human behaviour on building energy use. The concept of lifestyle to explain discrepancies between calculated and measured energy use in residential context was also observed for single family houses (Korjenic and Bednar 2011). The impact of an ideal and worst case scenario for occupant behaviour in offices compared to the impact of building design in different climates showed, that the impact of occupants on resulting energy consumption was larger than the impact of building design (Roetzel et al. 2014). The comparison of different scenarios for occupant behaviour as indicated in the above mentioned literature can be very useful in early design stages. Most of the investigated studies used either two or three scenarios, and in all cases they incorporated two extreme case scenarios such as energy conscious vs. wasteful or ideal vs. worst case. This paper examines the potential of extreme case scenarios (ideal and worst case) as a simplified method for early design stages to predict the impact of occupants on comfort and energy performance in cellular offices. 3 3.1

Parameters of occupant behaviour Occupancy

The schedule for occupancy in buildings has significant impact on the buildings energy consumption and it also

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defines the magnitude of internal heat loads, i.e. for how long office equipment is running. Typical occupancy schedules (Rubinstein et al. 2003) cover a period of 10–14 h which is equivalent to 6–8 h with 100% occupancy. However the actual presence and absence of occupants seems to be difficult to predict and several studies report on the randomness of the related behavioural patterns (Wang et al. 2005; Page et al. 2008; Wang et al. 2011). A comparative field study indicated considerable occupancy differences in different buildings, and identified the use type, functions and required working hours as the key parameters to consider (Mahdavi et al. 2008). From a client/tenant point of view, the major influence that defines the occupancy schedule is the task that a person has to perform, and this also defines the required office equipment (power of computer and monitor), as well as the intensity of use of this equipment in on standby or off mode. Some tasks require constant presence in the office, others also require field work outside the office, and others rely on incoming customers which affect the presence of people in the room. As indicated in Table 1, the task is the predominant influence on the occupancy patterns in a cellular office. From a simulation point of view it is desirable to know occupancy patterns as precisely as possible for most accurate modelling. Especially in cellular offices spaces with small numbers of occupants, the presence or absence of a single person can influence the internal heat loads to a larger degree than in larger and more populated spaces. From an architect’s point of view, the individual tasks as well as the presence and absence patterns of occupants are indicators towards an “office lifestyle”, i.e. the attitude of the company towards flexible spatial layouts, work arrangements, luxury level and multimedia integration. These parameters affect the overall floor plan size and layout, identify whether rooms or workplaces can be shared, and the allocation of spaces with special requirements, e.g. with regards to daylighting, natural ventilation, glare protection, representativeness (Heerwagen et al. 2004). If the tenant is already known, this information can be obtained with more detail. Often however, tenants are not yet known in early design stages of commercial buildings and in such cases buildings are designed to be flexible/generic in order to

accommodate a variety of tenants. From an occupant perspective, flexitime, tele-working and job sharing can strongly influence presence and absence patterns and this can vary depending on company policy and the lifestyle of the individual. For occupancy simulation in early design stages the following conclusions can be drawn: – In preliminary design stages, neither architects nor potential occupants are likely to be fully aware of potential occupancy patterns in great detail. Also such patterns are likely to differ between companies/tenants, and a building’s performance should not depend on a particular tenant. – In order to demonstrate the magnitude of influence of different occupancy patterns in early design stages, an ideal scenario with consistently low occupancy and a worst case scenario with consistently high occupancy could be helpful. The energy calculator for PC equipment (EUEnergy-Star 2014) as part of the Energy Star programme of the European Commission to coordinate American and European energy labelling of office equipment, suggests different usage patterns that can be related to occupancy patterns. Figure 1 shows a potential pattern for the “busy office” (on mode for a total of 8 h/day) and Fig. 2 for a “light office” pattern (on mode for a total of 2 h/day). – In order to determine where in the range between ideal and worst case scenario a company’s occupancy pattern is going to be, it can be helpful to translate company’s information about the expected “office lifestyle”, into behavioural patterns, with particular regards to attitudes towards flexible work arrangements, level of IT use and integration as well as expected luxury and privacy levels of the spaces. 3.2

Use of office equipment

As identified in Table 2 the major parameters influencing the internal heat loads by office equipment are the type of equipment and the intensity of use. Both depend strongly on an individual employee’s job. That is, an IT administrator’s job requires a powerful set of office equipment running for the full day and potentially even overnight, while other jobs require very little actual use of office equipment. A study

Table 1 Occupancy patterns and their relevance for simulation models, architects and occupants Occupancy Parameter Individual task/ job description

Sub parameter Presence/absence patterns

Relevance in preliminary design stages from different perspectives Simulation modelling Crucial to determine internal heat loads. Relevance of individual patterns increases the less people in an office

Architectural design Influences floor plan size and layout. General “office lifestyle” is sufficient for architects, the resolution of actual occupancy patterns is not requires

Considerations from an occupant perspective in operational stages “Office lifestyle” and company dependent arrangement such as flexitime, job sharing and tele-working can influence patterns and space layout. Potential impact of after-hours occupancy by cleaning and security staff

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Fig. 1 Occupancy patterns for extreme case scenarios: worst case

Fig. 2 Occupancy patterns for extreme case scenarios: ideal case

(Roetzel 2009) showed, that the wattage for office equipment for an architect can be three times higher than for someone doing an administrative job. From a simulation point of view, the intensity of use (which should be related to occupancy patterns) and the wattage of the equipment are useful indicators to estimate the patterns and magnitude of internal heat gains. In early design stages it can be useful to determine the room with highest internal equipment gains and the strongest exposure to solar heat gains, as this will be the worst case for summer thermal comfort in the building. This then can set a worst case benchmark for the evaluation of other rooms in the building. From an architect’s perspective it is important to know in early design stages, in which spaces high internal heat loads, such as IT centres and server rooms, cannot be avoided. These spaces need to be carefully allocated on the floor plan as they often have special requirements concerning security and cooling (Sun and Lee 2006; Shehabi et al. 2011). Early knowledge of tasks with high intensity of equipment use

can also help to design the floor plan layout in a way that high internal loads are not allocated close to facades with high solar heat gains. From an occupant’s perspective, equipment is usually provided by the company and the level of choice might be limited. In this context the building and its occupant should be treated as an integrated system (Heerwagen 2000). Company policy should aim to use energy efficient equipment, and prefer notebooks over desktop solutions if this does not affect the performance of the task. Individual occupants should be provided with switchable sockets or similar systems, so equipment can be disconnected from the power supply when not needed. Occupants should be discouraged to keep equipment running during longer periods of absence or overnight, and be made aware of their impact on comfort and energy performance (Roetzel 2009). For equipment usage simulation in early design stages the following conclusions can be drawn: – In early design stages it is likely that details on the equipment configurations of individual occupants are not yet known, and they are also likely to change after a tenant change. Different occupant scenarios can help to estimate this impact. In early design stages a worst case and ideal scenario (Roetzel et al. 2011) can help to determine the impact of equipment related internal heat loads. – The ideal scenario can then help to determine the magnitude of optimisation potential related to office equipment alone. Information on the expected “office lifestyle” as mentioned in Section 3.1 can help to determine how, on the range between ideal and worst case scenario, the expected equipment use is likely to perform. – Priority should be given to the assessment of spaces where optimisation of equipment is not possible due to requirements of the task, or high internal heat loads coincide with large solar heat gains, as these are the most challenging spaces in terms of thermal comfort. – The energy calculator for PC equipment (EU-Energy-Star 2014) as part of the Energy Star programme of the European Commission to coordinate energy labelling of office equipment, provides the power of different office equipment devices in on, sleep and off mode. A suggestion for equipment configurations related to the occupancy patterns discussed in Section 3.1 for an ideal and worst case scenario is shown in Table 3.

Table 2 Use of office equipment and its relevance for simulation models, architects and occupants Use of office equipment Parameter Individual task/ job description

Relevance in preliminary design stages from different perspectives

Sub parameter

Simulation modelling

Type of office equipment, and intensity of use

Significance increases with intensity of use and wattage of equipment

Architectural design Relevant for floor plan layout, allocation of server rooms, and balancing of internal vs. solar gains in spaces

Considerations from an occupant perspective in operational stages Occupants should be discouraged to leave equipment on during longer periods of absence or overnight. Switchable sockets can help to disconnect from power supply in off mode

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Roetzel / Building Simulation / Vol. 8, No. 2 Table 3 Ideal and worst case scenario for use of office equipment based on the Energy Ideal scenario, electricity consumption per year: 21.4 kWh/year

Sleep mode

Off mode

Usage pattern

Light office (h)

2

9

13

Equipment

Large notebook 17’’–18” (W)

25.5

1.7

0.9

On

Sleep mode

Off mode

Busy office (h)

8

2

14

Workstation (W)

190

7.4

1.5

Top 27” LCD (W)

103

1.9

0.6

Worst case scenario, electricity consumption per year: 580.3 kWh/year Usage pattern Equipment

3.3

On

Natural ventilation

Natural ventilation controlled by occupants via openable windows is influenced by a large variety of parameters related to the climate, the facade design, and the psychological or social environment (Brager and de Dear 1998; Brager et al. 2004; Ackerly et al. 2011; Rijal et al. 2011; Gunay et al. 2013; Roetzel et al. 2009). Many window opening models have emerged out of field studies, and as pointed out by Ackerly et al. (2011) the literature agrees on the non-deterministic nature of window control. Apart from that, the individual models differ significantly, and models intended for use in building simulation have become increasingly complex. In a review of 10 different window opening models (Roetzel et al. 2009), the adjustment parameters were (in order of occurrence in the models): outdoor temperature, indoor temperature, time of day/previous window opening angle, as well as occupancy, rain, indoor pollution, CO2 concentration, occupant type (active/passive), and wind. It should be noted that most of these models are based on field studies in a moderate climate such as UK, Switzerland and Germany, with the exception of one model based on the Pakistani context. Table 4 illustrates the influences on natural ventilation and their evaluation for early design stages. From a simulation point of view the following can be summarised: – Most recent window opening models agree that outside as well as indoor temperatures are the most crucial parameters (Rijal et al. 2008a; Haldi and Robinson 2008; Rijal et al. 2008b; Page 2007; Martin et al. 1996). These models require weather files with a high level of resolution. – Time dependent models correlate window operation with arrival or departure and often suggest little change throughout the day (Fritsch 1990; Haldi and Robinson 2008; Yun and Steemers 2008; Yun et al. 2008; Herkel et al. 2008). However these models are derived in moderate climates, and observations in hot climates indicate that

window closing is also likely to occur at highest outdoor temperatures, when occupants aim to prevent hot air entering the room (Rijal et al. 2008b; Roetzel 2009) – Most models considering the previous window state (Fritsch 1990; Haldi and Robinson 2008; Yun and Steemers 2008; Yun et al. 2008; Rijal et al. 2008b) assume window types which can have two states, closed and open, such as bottom hung windows which are most common in moderate climates. In warmer climates, window types are more various and often allow for more than two window states, such as sliding windows, side hung windows, and top hung windows. These window types need to be represented by models which can predict the opening and closing events as well as their angle or opening size. Window opening field studies with focus on different window types, sizes and opening angles could help to verify in how far the existing models are universally applicable for all window types. In this context the differentiation between symmetrical and asymmetrical window types as indicated in the literature (von Grabe 2013; von Grabe et al. 2014; Hall 2004) could be important. – Wind, rain (Haldi and Robinson 2008; Martin et al. 1996) and other local climate characteristics have been mentioned as influential on window operation. However this impact is dependent on the window type and the climate, and currently not a major parameter in window operational models. – The indoor air quality as well as CO2 concentration (Page 2007; Martin et al. 1996; Andersen et al. 2013; Fabi et al. 2012; Page et al. 2008) in a room have significant impact on the window operation by occupants. In residential context CO2 concentration has even been found one of the most important variables determining probabilistic window operation. While CO2 concentration can be included in window operational models, indoor air quality based on exhalation of furniture and smells caused by occupants are difficult to model using building simulation. – Night ventilation can significantly contribute to the cooling of a building, but in most cases it depends on the security policy whether or not windows can be left open at night (Roetzel et al. 2009). This is not necessarily known in early design stages. – Window type, size, and placement in the facade (Roetzel et al. 2009; Seifert 2005; Gritzki 2001; Hall 2004; von Grabe 2013; von Grabe et al. 2014) are likely to influence several other parameters such as window control due to wind or rain, and related facade design predefines the air exchange rate, and suggests certain control patterns. – Shading systems can have an impact on ventilation effectiveness. In simulations this should be considered using correction factors for discharge coefficients (Tsangrassoulis 1997).

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Table 4 Use of operable windows and its relevance for simulation models, architects and occupants Occupant control of natural ventilation Parameter climate

Facade design

Relevance in preliminary design stages from different perspectives Simulation modelling

Architectural design

Considerations from an occupant perspective in operational stages

Outside temperature

Major parameter in window opening models. Weather files with high resolution required (i.e. hourly data)

Wind

Currently not considered in window operational models

Rain

Currently not considered in window operational models

Annual and seasonal climate information such as extremes, averages, diurnal range and seasonal patterns are used to assess insulation levels, passive solar heating, shading, weather protection, facade design, window types and bioclimatic responses

Occupants operation of windows is influenced by a large variety of parameters. The hierarchy of these parameters can change on sub hourly basis and is likely to determine the action. Different behaviour is likely for individual and group offices

Inside temperature

Major parameter in window opening models, determined by simulation

Sub parameter

Relies on simulation to get an estimate, feedback can improve design

Window type, size Defines air exchange rates and predefines and placement control patterns. Validity of window operational models for different window types and modelling software could be clearer

Preliminary facade design determines facade type, and approximate glazing area, actual window types and operational details are determined only in later stages

Operation can depend on ease of operation, accessibility of controls, safety of operation, window construction and quality, predefinition of opening angles and opening mechanisms

Can be an obstacle to natural ventilation and strongly influence air exchange rates. Can be modelled via correction factors for discharge coefficients

The more important in early design stages, the more the system is visually present in the facade design

Impact of individual desk location and group dynamics in case of shared offices

Shading system

Indoor environment

Indoor air quality / Major impact, especially for window Interior fit out has influence on CO2 concentration opening. Estimate for CO2 concentration indoor air quality, but is not possible, but indoor air quality is difficult defined in early design stages to model using building simulation

Sensitivity to indoor air quality and smell can vary between individuals

Occupants

Time of the day Considered in window operational (arrival, departure) models, but so far based on moderate climates only

Depends on task / job, flexible work arrangements

Not important for architects, little relevance for design

Previous window state

Considered in window operational models, Window type predefines possible The number of possible window but so far only for window types with the window states opening states will affect the two states: open and closed operation (window type)

Psychosocial influences

Individual preference and expectation Can have strong influence on Strong variation between individuals can be predominant. Difficult to model, design if particular preferences but adds uncertainty to simulation results are communicated to architects

Night ventilation Insurance policy policy

Important parameter can be modelled in most software. Different pattern to daytime occupant controlled ventilation

– With regards to the literature on window opening behaviour none of the authors used the terms “active or passive occupants” to differentiate behavioural patterns related to window opening as clearly as is the case for light and blind switching in Sections 3.4 and 3.5. It is therefore assumed that this differentiation is not of similar importance for ventilation. Further research could investigate whether active/passive window opening behaviour can be differentiated in mixed mode buildings or particularly hot or cold climates. From an architect’s perspective, the following conclusions can be drawn: – Climatic information is required in early design stages predominantly in annual and seasonal terms as the climate

Requires burglary safe ventilation Weather forecast can have an openings which need to be influence on occupant controlled considered in design night ventilation

is only one of many parameters influencing the design. Extremes and averages, diurnal ranges, seasonal and annual patterns are useful to assess insulation levels, potentials for passive solar heating, requirements for shading, weather protection and to integrate those influences into the building envelope. While buildings are designed predominantly based on long term experience with annual and seasonal climate patterns, they are assessed (simulation) and operated (occupants) at a much finer resolution, i.e. one weather file for a specific year. This discrepancy should be taken into account when evaluating simulation results. – While window size and placement is likely to be determined in early design stages as part of the visual

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appearance of the facade, window types are mostly defined only in later development stages. Since they have only minor influence on the aesthetics of the facade, feedback and recommendations about the impact of window types on the performance of the building can be very helpful for architects in early design stages. The same applies for the interaction of windows and shading systems. A flow chart facilitating the selection of window types is proposed in Fig. 3. – The responsibility of architects with regards to indoor air quality is mainly related to the floor plan layout (i.e. the possibility for kitchen smells to travel through the building) and the choice of internal surface materials and furniture (exhalation). – In case that the future building occupants are known, it is possible that clients communicate particular psychosocial preferences and experiences to architects as part of the brief. These can relate to preferences in brightness, thermal comfort, privacy, transparency etc. – If night ventilation is desired in a building this should be communicated to architects in early design stages, as it requires the design of burglary safe ventilation openings, which may or may not be windows. Building occupants in operational stages are influenced in their control of windows by a large variety of parameters. As a result occupants operate windows individually based on their personal hierarchy of all these influencing parameters. Similarities between individuals can be translated into

patterns for simulation. Buildings should be robust enough to tolerate different behavioural patterns, and recommendations about the impact of window opening behaviour on the buildings performance should be better communicated to occupants. For the simulation of cellular offices in early design stages it can be concluded that occupant control on natural ventilation is an important parameter to consider. While it does not seem necessary for daytime natural ventilation, for night ventilation the differentiation between an ideal (night ventilation=yes) and worst case (night ventilation not possible) scenario seems useful. Ideally, a behavioural model for window operation would consider as many of the parameters indicated in Table 4 as possible. In practice, this currently depends on the capabilities and limitations of simulation software. The major recommendations for further research derived from this section are: – Window opening types could be a selection parameter on user interfaces for building simulation. This could improve the accuracy in modelling of natural ventilation and raise the awareness of architects to the importance of window type, size and placement in a facade. Further research on air exchange rates and discharge coefficients of different window types might be required. – In practice shading systems often limit the effectiveness of natural ventilation, and this impact is often not considered in simulation models. Additional research and recommendations for architects on the impact of

Fig. 3 Window opening type selection criteria related to climate and building design

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shading systems on ventilation effectiveness would be required. – There is a current mismatch between the low accuracy of commonly available weather files to represent the climatic variability, extremes and trends at a location, and the high level of accuracy applied when evaluating simulation results produced with such a weather file. That is, a few overheating hours are taken very seriously in comfort assessment, but much less is known regarding the number of hours where the weather file is actually representative for the climate characteristics. Further research to increase the accuracy of weather files could be useful. – The impact of psychosocial parameters and indoor air quality on the effectiveness of natural ventilation can be significant, but is very difficult to model in building simulation. An approach for the translation of qualitative into quantitative information would be required. 3.4 Artificial lighting control Occupant controlled light switching is influenced by a large variety of parameters (Bordass et al. 1993; Bourgeois 2005; Reinhart and Voss 2003; Galasiu and Veitch 2006; Moore et al. 2003). The most influential parameters on lighting control as determined in Table 5 are related to either the individual occupants or the building.

From a simulation point of view, the following issues can be identified: – The task is useful as a vehicle to estimate the required illuminance levels. Although occupant’s preferred illuminance levels varied significantly in field studies, a tendency towards preferences of about 500 lx for reading and writing tasks and for about 300 lx for work on a computer screen could be observed (Hunt 1979; Velds 2000; Escuyer and Fontoynont 2001; Moore et al. 2002; Mahdavi et al. 2008). – The differentiation between different user types i.e. active/passive or standard vs. energy conscious can give a hint on the frequency of the use of controls (Reinhart and Voss 2003; Moore et al. 2003; Bourgeois 2005). Active occupants adjust the lighting throughout the working day, whereas passive occupants tend not to change the lighting conditions during the day. However in early design stages, this requires detailed knowledge about the future occupants of the building, which is often not available. – With regards to the building, orientation and context are of major importance. Especially in dense urban environments the orientations can be less influential than overshadowing by surrounding buildings, and the latter should be included in the simulation model to obtain realistic illuminance values. – The type of the lighting system, i.e. room related or task

Table 5 Use of artificial lighting and its relevance for simulation models, architects and occupants Occupant controlled light switching Parameter Individual influences

Building related influences

Sub parameter



Considerations from an occupant perspective in operational stages

Task

Indicator for required illuminance levels. Lower levels for computer based task, higher for other tasks

Relevant for floor plan layout with regards to privacy, representativeness

Lighting is adjusted based on available control options in order to perform the task

Active or passive

Indicator for likelihood of light switching

Not directly relevant for architects in early design stages

Group dynamics and hierarchies can influence who decides on operation of controls

Psychosocial influences

Large variation based on individual Can have strong influence on design if Strong variations between preference and expectation, difficult particular preferences are communicated individuals to model to architects

Orientation/building context

In urban environments overshadowing Expected variability of lighting levels Potential light switching as a and reflections by other buildings can can have impact on the use of controls/ consequence of blind switching be important dimming in later design stages

Office type, number The larger the number of people, of occupants in room the less likely individual action for room related lighting

Influences decision for task area or room related lighting

Group dynamics and hierarchies can influence who decides on operation of controls

Type of lighting system Essential for the definition of the wattage of the system

Depends on intended luxury levels, project budget, implementation of double floors and suspended ceilings

Location of controls

Impact depends on room size, can indicate likelihood of controls

Not determined in early design stages

Operation of task related lighting is possible from desk, for room related lighting occupants have to get up

Distance of occupant from window

Impact on interior lighting levels

Depends on floor plan layout and furnishing

Indirect influence on light switching in terms of daylight illuminance levels, glare and view

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area lighting, the location of controls as well as the office type, size and number of people in the room are all interrelated and useful to determine related internal heat loads (Galasiu and Veitch 2006; Reinhart 2004; Reinhart and Voss 2003; Bordass et al. 1993). However, details related to the interior fit out of a building are not a deliverable in sketch design stage of building and therefore difficult to obtain in early design stages. – Early floor plan sketches can indicate the allocation of work places and allow for a preliminary estimate of bright vs. dark spaces and related impact on lighting control. From an architect’s perspective most of the parameters investigated in Table 5 are relevant in later stages of the project as the interior fit out is not part of a sketch design proposal. – The task is useful to know as it gives indication towards required levels of privacy, representativeness and access to daylight/location on floor plan. – Psychosocial variables and the building owner’s attitude can be influential on the design, i.e. a glazed facade is often desired as a symbol to express transparency of a business. – Office type, approximate size and number of people in the room are likely to be known in early design stages, but the selection of the lighting system and the location of their controls are not part of the sketch design process. In order to obtain early estimates, the budget as well as the intended quality/luxury level of the project under consideration can be helpful. A low budget as well as low luxury levels can hint towards systems with low initial costs such as simple room related lighting systems with basic controls. These systems often utilise more energy in operation. Higher budgets and luxury levels will allow for more accentuated, atmospheric lighting and more sophisticated controls. These often come with higher initial costs, whereas operational energy consumption and costs can vary significantly depending on whether lighting is only supplied to workplaces or also used to light features or exhibits (Illuminating Engineering Society 2003). From an occupant’s perspective, control of an artificial lighting system depends significantly on the task and the required illuminance levels. The lighting system and the location of controls are often interrelated with centrally located switches for room related- and individual switches for task area related lighting. Especially in offices with more than one occupant, a central switch can require occupants to get up in order to make adjustments which may first require consensus by all occupants in the room. It can be summarized that in early design stages, the future occupants of a building and related individual influences on light switching are often not yet known. Also building related influences will depend on the characteristics

of the project, and are still likely to change. Therefore, the modelling of any specific configuration is very difficult during early design stages. An ideal and a worst case scenario for light switching could therefore be useful in early design stages, since it can reflect an active and passive behaviour, and the magnitude between both scenarios illustrates the uncertainty in occupant behaviour due to parameters which are not yet known in early design stages such as number of occupants and the type of lighting system. An ideal scenario could assume light switching according to daylight availability, i.e. with an illuminance threshold of 500 lx (or 300 for computer tasks) on the work plane. And a worst case scenario would assume that the light is switched on throughout the working day. 3.5 Blind control Occupant controlled blind switching is another important parameter that affects a building’s comfort and energy consumption (Inkarojrit 2005; Rea 1984; Boubekri and Boyer 1992; Newsham 1994; Bülow-Hübe 2000; Foster and Oreszczyn 2001; Osterhaus 2008; Sutter et al. 2006; Galasiu and Veitch 2006; Tuaycharoen and Tregenza 2007; Reinhart and Voss 2003). Since it influences the levels of daylight in the room it indirectly affects the artificial light switching patterns, too. Difference between active and passive users have been observed, with passive occupants keeping the blinds closed all day, and active occupants adjusting the blinds according to glare and/or overheating. As can be summarized from the literature (Table 6), the most important blind switching criteria are related to glare, overheating or privacy. From a simulation point of view occupant controlled blind switching is difficult to model, due to a large variety of parameters, not all of which can be expressed numerically. Especially glare is a very complex blind switching criterion and some of the major influences are listed below: – Orientation and sunlight penetration together with the distance of occupants from windows determine the relationship of the sun angle within the field of vision of occupants (Rea 1984; Inkarojrit 2005; Foster and Oreszczyn 2001; Mahdavi et al. 2006). – Window area of the facade and window arrangement, which impacts the luminance differences within the field of view, but also depends on climate and external obstruction (Bülow-Hübe 2000; Foster and Oreszczyn 2001; Osterhaus 2008; Boubekri and Boyer 1992). – Type of shading system, which predefines the balance of shading vs. daylighting. – Task and quality of a computer screen, which can make a slight difference to the glare sensitivity of occupants if the task involves work on a computer (Sutter et al. 2006). This parameter is difficult to consider in a simulation model.

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Table 6 Occupant controlled blind switching and its relevance for simulation models, architects and occupants Occupant controlled blind switching Parameter Glare

Sub parameter


Considerations from an occupant perspective in operational stages Glare perception can be influenced by the quality and quantity of the view

Orientation, sunlight penetration

Influences luminance contrasts

Direct/diffuse light influences atmosphere of the space, different shading types might be required for different orientations

Window area of facade and window arrangements

Influences luminance contrasts as an indicator for glare

Facade design is a compromise between visual appearance from the outside and visual comfort inside

Distance of occupant from window

View angles of the facade influence Allocation of workplaces in the room can perception of luminance contrasts influence view angles of the facade and resulting glare

Type of shading system Simultaneous impact on glare, heat protection and daylighting

Occupants closer to the facade are more likely to experience glare, but have better access to daylight

Only determined in early design stages if Ease and accessibility of control influential on design. Glare and heat varies with the system protection can be one or different systems High quality screen can reduce the perception of glare

Quality of computer screen

Stronger impact on discomfort as Choice of equipment is not made by opposed to disability glare. Difficult architects to consider in simulation

Task

Helps to determine amount of time Relevant for floor plan layout with regards spent on computer screen with to privacy, representativeness higher sensitivity towards glare

Aesthetic interior qualities of the room

Impact of surface reflectance

Only considered in early design stages for Blind switching/daylighting can representative or key spaces be used to cheer up a dark space

Active or passive

Affects frequency of switching

Not relevant in preliminary design

View quality

Indirect impact on glare tolerance, May influence the choice of shading difficult to consider in simulation system

Can have impact on tolerance of discomfort glare, not so much on disability glare

Threshold for overheating control Feedback from simulation can be is crucial important for optimisation in early design stages

Occupant control can differ for glare or overheating prevention

Overheating Indoor/outdoor temperature Privacy


Energy conscious occupants are more likely to maximise use of daylight

Parameter considered in floor plan layout Individual variability likely Exposition of the room Parameter can superpose, glare and overheating. Can be estimated based on task and floor plan layout

– Visual and aesthetic interior qualities of the room, which has an impact on luminance contrasts and indirectly affects glare tolerance (Boubekri and Boyer 1992). These parameters are not yet defined in early design stages. – View quality, i.e. attractive views result in a higher tolerance to glare than less attractive views (Galasiu and Veitch 2006; Bülow-Hübe 2000; Boubekri and Boyer 1992; Tuaycharoen and Tregenza 2007). This is difficult to consider in building simulation. – One simplified index for glare evaluation is the Discomfort Glare Index (DGI) (Hopkinson 1970 and 1972) which is based on the luminance difference between the window and the surrounding background as seen from a reference point. It does not reflect the complexity of the perception of glare in real buildings; however in its simplicity it might be suitable for early design stages and it is available in simulation software such as EnergyPlus.

Overheating is related to the difference between indoor and outside temperature, and only at uncomfortably high indoor temperatures this is a likely reason for blind switching. – Blind switching for overheating protection is likely to occur when the sun is shining on the facade, and the room temperatures are likely to exceed the comfort limits. In a study in France, the threshold 200 W/m2 for solar radiation on the facade and a room temperature of 26 degrees Celsius was suggested (Sutter et al. 2006). Another study in Austria indicates a relationship between blind operation and incident irradiance on the facade; however acknowledges that for a reliable prediction additional parameters have to be considered (Mahdavi et al. 2008). These thresholds might need to be adjusted for different climates depending on local specifications. Although further validation would be necessary, the comfort temperatures according to the adaptive thermal comfort standard

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ASHRAE Standard 55 (ASHRAE 2010) could be a climate specific threshold for blind switching as overheating control. – Privacy as a reason for blind control is likely to lead to closure of blinds throughout the whole working day, i.e. in rooms that are particularly exposed so that occupants feel observed by others (Foster and Oreszczyn 2001). – While in an existing building such behaviour might be observed in a field study, in early design stages the floor plan layout can help to estimate which rooms in a building could have privacy issues. From an architect’s perspective, occupant controlled blind switching is not one of the major issues to consider in the sketch design stage. The choice of the shading system is important if the shading system affects the visual appearance of the facade. It is also a cost factor in the budget of the project as internal shading systems tend to be less expensive however less efficient compared to external systems. Facade design offers the opportunity to separate systems for heat and glare protection in order to improve daylighting; however this would normally be a decision in later design stages. From an occupant perspective, a variety of influences can affect the control of blinds and the variability between behaviour of different individuals can be large. Disability glare (which prevents occupants from performing their task) is likely to cause an immediate action, whereas the reaction to discomfort glare (affecting but not preventing the performance of the task) can depend on other parameters as well, such as view quality and quantity, control type and ease of use, sensitivity towards privacy issues, quality of the space, group dynamics and hierarchies (Sutter et al. 2006; Tuaycharoen and Tregenza 2007; Galasiu and Veitch 2006; Bülow-Hübe 2000; Osterhaus 2008). In early design stages, most of the influences on blind switching are not yet known, and for simulations it is important to acknowledge that any assumption made to facilitate simulations can lead to inaccuracies. Especially the difficulty to model glare makes occupant controlled blind switching a very challenging parameter to consider in building simulation. A simplified approach based on an ideal and worst case scenario can be a simplified way to model blind control in early design stages. The worst case scenario would in most cases assume that blinds are closed throughout the working day, e.g. for privacy reasons or due to passive users. From a comfort and energy consumption point of view, this requires the artificial lighting system to be switched on for long periods of time which increases the energy consumption as well as the internal heat loads. However if more information is available, the worst case can be adjusted depending on the project specific interplay (Roetzel et al. 2010) of the type of the shading system and whether it allows for daylighting

adjustments when activated, the wattage and luminous efficacy of the lighting system, as well as the Coefficient of Performance of a cooling system. And an ideal scenario would assume that blind switching is depending on a location specific definition of heat or glare, i.e. “glare (DGI threshold)” or “solar radiation on the facade + room temperatures above a defined threshold”. 4 Conclusions and recommendations The focus of this paper is to highlight the different levels of resolution required for building simulation input as opposed to the information available in early design stages of a building. It addresses the difficulty to translate the predominantly qualitative architectural information in early design stages into quantitative data to run the simulations. The sections above describe an ideal and worst case scenario approach to the modelling of occupant behaviour in early design stages. In the sense of the three levels of resolution suggested by Hoes et al. (2009), this approach refers to the first and most simplified level. Benefits of the extreme case / ideal and worst case scenario approach in early design stages are: – It matches the level of resolution of available architectural information in early design stages. This low level of resolution is not enough for predictions of actual building performance in a final design stage; however it is sufficient for architects in early design stages to compare the impact of occupant behaviour with other parameters in order to inform design decisions. – Results are less misleading to architects in early design stages than predictions based on one specific behavioural profile, because a “range of impact” reflects the low resolution of input better than a seemingly accurate single performance value. The major disadvantage of an ideal and worst case scenario approach is that it does not make a clear prediction how the building is likely to perform. However the discussion in this paper suggests, that due to the lack of accurate information, accurate predictions are hardly possible in early design stages. Instead, it might be helpful to review available qualitative and quantitative information in early design stages in order to detect indicators towards the ideal or worst case scenario. A suggestion for potential indicators is provided in Table 7. From this table it can be summarized that a building design focused on low initial costs and basic quality of office space provides a higher likelihood towards the worst case scenario, and a priority for low running costs, moderate to high quality of office space and energy conscious occupants/building operation are indicators towards the ideal scenario. More flexibility in the project’s budget and a higher intended quality of office spaces tends to allow for

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Table 7 Indicators towards the ideal and worst case occupant scenarios Ideal scenario

Worst case scenario

Office equipment (Table 3)

Task requires only basic equipment, notebooks can be used, energy conscious company attitude

IT centre or server room or other tasks that require high end desktop equipment

Occupancy (Figs. 1 and 2)

Task requires little presence in the office

Task requires all day presence in the office

Night ventilation

Burglary safe ventilation openings, context of location considered safe, room not easily accessible from exterior

Room easily accessible from exterior (e.g. ground floor), occupants deal with sensitive data, location perceived unsafe

Lighting control

Single occupancy, higher luxury levels, focus on low running costs, energy conscious occupants

Multiple occupants, focus on low initial building costs or high luxury level with significant decorative lighting

Blind control

No privacy issues, shading system allows for adjustment of daylighting when activated, energy conscious occupants, higher luxury levels

Room provides little privacy, shading system does not allow for adjustment of daylighting when activated, low initial costs

Summary

Energy conscious occupants and building operation, focus on low running rather than low initial costs, moderate to high quality of office space with more sophisticated systems and controls

Focus on low initial rather than low running costs, very basic quality of office space, with basic systems and controls

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