Indoor Climate and Productivity

Tool for performance-calculations related to ventilation and thermal comfort in office buildings

Stephanie Carr (s093384) 01/09/2013

The report describes the methodology of an indoor climate tool that quantifies the relative productivity increment due to changes in thermal comfort and ventilation rate. The dose-response relationship between thermal sensation vote and relative productivity, and an estimated distribution of thermal sensation votes in a group form basis for the calculations on thermal comfort. The relationship between ventilation rate and relative productivity lays the foundation for the ventilation calculations. A case-example is included, where an office building is improved in two different ways. The tool shows how one case improves the relative productivity while the other reduces it. The tool delivers the most accurate results with input from IESVE.

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TABLE OF CONTENTS Preface ............................................................................................................................................................... 5

Acknowledgements ........................................................................................................................................... 7

Introduction ....................................................................................................................................................... 9

Theory ............................................................................................................................................................. 11

Indoor environment ................................................................................................................................... 11

Indoor climate and the employer ............................................................................................................. 11

Temperature and relative productivity .................................................................................................... 12

Air quality and relative productivity ....................................................................................................... 13

Methods ........................................................................................................................................................... 15

Ventilation rate .......................................................................................................................................... 15

Calculation type 1; estimation ................................................................................................................. 15

Calculation type 2; simulation ................................................................................................................. 15

Temperature .............................................................................................................................................. 16

PMV/relative productivity-relation ......................................................................................................... 16

Calculation type 1; estimation ................................................................................................................. 17

Calculation type 2; simulation ................................................................................................................. 19

Clo-values ................................................................................................................................................ 19

Combined effect ......................................................................................................................................... 20

Case study example ......................................................................................................................................... 21

Case 1, solar film ......................................................................................................................................... 22

Case 2, mechanical ventilation .................................................................................................................... 24

Result summary ........................................................................................................................................... 26

Discussion ....................................................................................................................................................... 27

Conclusion ....................................................................................................................................................... 28

References ....................................................................................................................................................... 29

Appendix A

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PREFACE This report was developed in relation to a Special Project by Stephanie Carr (s093384), student at DTU (Technical University of Denmark) in collaboration with Grontmij AS, Denmark (CVR 48233511). The supervisor has been Jørn Toftum from DTU, and the support person and project leader in Grontmij has been Jacob Ilsøe. The special project is developed as a part of the student’s Master’s Degree in Sustainable Energy- Energy Savings, and it is weighted to 10 ECTS-points.

This project has been started on incentive from Grontmij, specifically the department for Energy and Sustainability. The report represents the indoor climate-part of a larger LCC (Life Cycle Cost) calculation tool that aims to underpin the argument that investments in early building phases can result in lower total building costs throughout the building life time. The tool has focus on visualizations and its main functionality will be as a design tool for use in the early design phases of buildings or renovations, especially during the decision-making process.

The tool is developed mainly for renovations of- or design of new-build office buildings. The indoor climate part of the LCC tool aims to show that investment in a better indoor climate are beneficial for the building owner/user, as a better indoor climate improves the performance and productivity of the workers.

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ACKNOWLEDGEMENTS I would firstly like to thank the employees in Grontmij for excellent guidance in the process of transforming the theoretical knowledge into practically usable material. I would like to mention especially Dorthe Kragsig Mortensen, for great sparring and support when the development process stalled, and the theory seemed impossible to translate into real-life workable solutions. Daniel Reinert has been most helpful during the design of the example case model in IESVE, and has offered valuable quality control on my work. Jonas Vendel Jensen, Jeppe Lemb Szaimetat and Brian Klejn-Christensen have been readily available for answering the questions I had, which was assuring during the process and frequently needed. The project leader Jacob Ilsøe has been inspirational and a source of positive energy, which helped tremendously during the methodology-development.

I owe my thanks to Lasse Nørregaard, who helped me with the mathematical problems that arose, and added the theoretical explanations needed to underpin my arguments. I have benefitted from Fred Carr’s extensive experience with technical texts in English, and by his hand the linguistic formulations have been checked and verified.

My supervisor on this project, Jørn Toftum, has offered good tips and provided articles and guidebooks that have proved very useful for the development of the methodology used in the project. I am grateful for his willingness to answer questions and provide guidance.

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INTRODUCTION The reason for constructing buildings is to create an indoor climate where the occupants feel comfortable. In office buildings worker productivity is highly influenced by the quality of the indoor climate. Compared to the expenses related to the employees, such as wages, sick leave pay etc., the cost for maintaining a good indoor climate is minimal. However, energy conservation and -efficiency in buildings has for many years been prioritized above a good indoor climate. This is possibly because the effects and consequences of a bad indoor climate have not been considered or else not advocated strongly enough. Today there are many studies pointing towards indoor climate as a central aspect in promotion of people’s well-being, health and productivity. The development and rising popularity of building certification schemes that include criteria regarding indoor climate (such as DGNB and LEED), as well as further deployment of indoor environment criteria in national standards, such as the building regulations, has led to an increasing awareness of the indoor climate and its benefits. Hopefully the development will be towards higher standards for building- and design quality, and that the focus will expand from designing extremely low-energy buildings to include designs that provide the best possible conditions for the people residing in them. Although the evidence is solid, few developers target a good indoor environment as a part of the success-criteria for a building. This might be because the assets of a good indoor climate are difficult to measure and quantify, as the revenue from for example better productivity and health among workers rarely is displayed as savings in Euros and Cents for the developers. The main goal from the development of this indoor climate/productivity calculation tool is to be able to present a visualization of the social/economic benefits gained from a better indoor climate. A tool that can display the economic consequences of cut-backs on investments for indoor climate can provide the argument that tips the scale in favour of a healthier and more productive indoor environment.

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THEORY

INDOOR ENVIRONMENT Employees in an office building spend around 8 hours per day residing indoors at work, where they generate turnover for their employer. To be able to work with a high efficiency the employees are dependent on the working conditions meeting certain standards. The psychological and social conditions are important, such as good colleagues, a good leader and feeling of job security. The physical conditions are important as well, for example the employees possibly need computers to do their work, specific software perhaps and a coffee machine. Also part of the physical conditions is the indoor environment; which has a huge impact on the well-being of the employees, and consequently their performance.

There are many factors of the indoor climate that have potential to obstruct the work progress in an office building. Excessive noise, very high or low temperatures are acute factors that are easily identifiable when present. Other factors are not as easily identifiable, but that does not mean that they do not influence the employees negatively: bad air quality [Pejtersen et al. (2001), Wargocki et al. (2002)], slightly too warm or cold temperatures [Kosonen and Tan, (2004), Seppänen et al., (2004)], lack of daylight [Edwards and Torcellini (2002)] and to some degree; noise in the work area [Lund et al (2012)]. The consequences of these factors include both short-term and long-term effects, and can for instance result in elevated stress-levels, imbalance in the circadian rhythm, more frequent sick leave and a less than optimal productivity level. Indirectly, the reduced quality of working conditions may also reduce the employee state of happiness, which in turn effects how well the employee performs.

In this report the effects of temperatures and air quality will be discussed. The span of the report has been limited to temperatures and air quality for several reasons. The available literature on the two indoor climate factors is extensive, and several investigations have been made on the relationship between worker performance and air supply and thermal comfort. Most consulting engineering companies involved in indoor climate consultancy use software that provides air temperature, relative humidity and mean radiant temperature as output, as well as information on several air flow sources and sizes. Thereby the input for the calculations is readily accessible. The air flow rate and temperature control is furthermore closely related to the energy consumption in a building. Keeping the total energy consumption low might have consequences for the indoor climate, which in turn can cause large economic losses for the employer via lost employee productivity. It is in the constructor’s interest not to compromise on the indoor climate, as it will be easier to sell or lease a property that has a robust indoor climate that promotes high productivity and performance. If a design struggles to meet energy consumption requirements the temperature control and increased air flow rate is often the first to be sacrificed.

The total productivity increment from improving a combination of many indoor environment factors is difficult to assess. Therefore only two factors (temperatures and air flow rate) are investigated.

INDOOR CLIMATE AND THE EMPLOYER The most expensive asset of an office building is by far the employees. Keeping these well provided for in terms of indoor climate will almost certainly be a good investment. The building running-costs are minimal compared to the costs of worker wages, and therefore it is in most cases an economically feasible solution to invest in indoor climate in order to improve the working conditions. The REHVA Guidebook #6 suggests the following ratios for expenses of a normal office building: construction: 1, maintenance and building

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operating costs: 5-9; business operating costs: 200. This means that for each Euro spent on constructing a building 200 Euros will be spent on employee wages, sick leave, etc. Evidently, very small improvements in worker performance can outrank large investments during the construction phase.

Working in a less than optimal environment has consequences for employee satisfaction and well-being. Low job satisfaction can lead to elevated rates of absenteeism [Sagie, 1998]. Rumours about bad indoor environment can cause the employees to become insecure regarding their health, and induce mistrust towards the management [Lahtinen et al, 2002]. A study by Cui et al showed that elevated temperatures greatly influenced the motivation to work among the occupants [Cui et al. 2013]. These are factors whose economic consequences are hard to quantify, but there should be no doubt that they are important for the employees, and influences their daily functionality.

An issue when arguing for investments in indoor climate is that the results are neither directly observable nor measurable, except for the worker satisfaction rate, which possibly goes up because of more comfortable working conditions. However there is strong scientific evidence that a productivity increment is one of the results of a better indoor climate, which is measurable in climate chamber experiments.

TEMPERATURE AND RELATIVE PRODUCTIVITY The operative temperature has influence on the productivity of the workers in a space. The productivity follows a U-shaped curve (see Figure 1), where the productivity decreases both as temperature drops or rises, the optimal temperature placed somewhere between 21 and 26 °C [Seppänen et al. 2004]. The investigations laying basis for the figure do not take into account differences in clothing, metabolic rate nor individual preferences among the individuals in a group. Therefore the thermal sensation vote provides a better understanding of the experienced thermal state of an environment.

Figure 1 - summary of several studies on tempe-ratures and performance, figure from Seppänen et al, 2004

Figure 2 - thermal sensation scale ranging from -3 to 3 where -3 corresponds to "very cold", 3 to "very hot" and 0 to thermally neutral.

The average thermal sensation vote of individuals can be predicted using P.O. Fanger’s PMV-model (Predicted Mean Vote) [Fanger, 1970]. The model is based on statistical data from 1300 participants, and predicts the average thermal comfort of a large group of people. The model uses activity level, clothing level, air temperature, mean radiant temperature, air velocity and relative humidity to calculate a PMV-value, which places the mean thermal sensation on the thermal sensation scale as seen in Figure 2 above.

The thermal sensation vote is connected to the relative performance through the relation seen in Figure 3 below. The best relative performance is at a tsv (thermal sensation vote) of -1 from where it declines in both

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directions. This means that workers perform optimally while feeling slightly cold and less optimal when feeling warmer or colder. The performance decreases more with warm temperatures than cold temperatures [Cui et al, 2013]. The used relationship between tsv and relative productivity (equation 1) is from [K.L. Jensen, 2008]:

(1) 𝑅𝑃 = −0.0029 ∙ 𝑡𝑠𝑣2 − 0.0034 ∙ 𝑡𝑠𝑣 + 0.999

Equation quantifying the effect of thermal sensation on relative productivity, from [K.L. Jensen, 2008]

Figure 3- Left: the dose-response relationship between thermal sensation and mental performance [Toftum et al. 2009]. Right: PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) relation.

Also relevant is the Predicted Percentage Dissatisfied (PPD), which is a measure of how many people who are predicted to feel uncomfortable in the specific thermal environment. It is a function of PMV as seen below in equation 2:

(2) 𝑃𝑃𝐷 = 100 − 95 ∙ 𝑒−0.03353∙𝑃𝑀𝑉4−0.2179∙𝑃𝑀𝑉2

The minimum amount is 5 % because of individual preferences, meaning that all occupants in a large group will never be perfectly happy with the same thermal conditions. The PPD of 5 % is obtained at thermally neutral (PMV 0), and rises in both directions (hotter or colder), see Figure 3, right, above.

AIR QUALITY AND RELATIVE PRODUCTIVITY The amount of air being introduced into a room has great influence on the well-being and productivity of the occupants. By reducing the time that air remains in a space, the bio-effluents, particles and chemicals in the indoor air is removed faster and is less likely to cause discomfort and Sick Building Syndrome (SBS)-symptoms.

Elevated levels of pollutants in the air reduce the quality of the indoor space. A way of controlling the levels of pollutants is by source control. A climate chamber study by [Wargocki et al, 1999] aimed to measure productivity among office workers with and without a pollution source present in the room (an old carpet). A significant increment in productivity, well-being and health was recorded among the participants. The calculation of pollution emittance from building materials and furniture is a tedious process that requires detailed input on the materials used. Therefore the air flow rate will be the only input for the calculation of productivity increment from ventilation in this calculation tool. The used relationship between ventilation rate and productivity is based upon experiments executed in real office buildings [Seppänen et al. 2006]. Thereby the model indirectly accounts for pollution sources in typical office buildings.

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Basically, the more fresh air being introduced to a room, the less decrement in productivity is seen among the occupants. A relation between the introduced amount of air per person and the relative productivity is available in REHVA Guidebook #6, see Figure 4.

Because airborne disease transmitters are also removed from the space faster with increased air flow rate, the probability of being infected with diseases is smaller with a higher flow rate. This means that worker absence due to illnesses is reduced with higher air flow rates. See Figure 4 from REHVA Guidebook #6. As the sick leave is displayed as prevalence, it is necessary to know the sick leave in the specific space before an improvement of the ventilation system. For instance; if the sick leave is 6 days per employee per year with 5 L/s per person, it would drop to half (from 0,8 to 0,4) if the ventilation rate is increased to 20 L/s per person. This effect is not included in the calculation tool, because it requires previous knowledge on sick leave, and is thus only useful for renovation projects, and only where this information is available. Any productivity increment from reduced sick leave among employees will therefore not be included in the final result for productivity increment, but must be considered as a bonus to the effect that is calculated from increased performance.

Figure 4 - (left) the relation between ventilation rate and relative performance, (right) the relation between sick leave prevalence and ventilation rate, figures from REHVA Guidebook #6

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METHODS

VENTILATION RATE Values from the graph shown in Figure 5 were plotted and a trend line was created in Microsoft Excel. The resulting expression is displayed below:

(3) 𝑅𝑃 = 0.0235 ln(𝑥) + 0.9536 where RP is the relative performance and x is the flow rate of fresh air in l/s per person.

Figure 5- the used ventilation rate and productivity relation based on Figure 4 from REHVA guidebook. The results below the productivity rate of 1 are estimates. This means that flow rates below 7 l/s per person are associated with higher uncertainty.

CALCULATION TYPE 1; ESTIMATION Input for ventilation rate is either l/s per person, air change rate (h-1) or total cubic metres per hour. The input is constant; there is no possibility for variations in flow rates during a day or season. L/s per person is the unit that is used to calculate the productivity in relation to ventilation rate. Therefore input of air change rate [h-1] or total cubic meters of fresh air per hour can be transformed by adding information on number of occupants and area/volume of the space. The relative performance is calculated by using equation 3 above.

The output is the difference the relative performance in the base- and the case simulations (relative performance increase). The percentage improvement in relative performance is multiplied with the average turnover per employee, which is multiplied with the number of occupants in the room. Thereby the total economical consequence for the relevant zone is calculated, regarding ventilation.

CALCULATION TYPE 2; SIMULATION Calculation type 2 is based on output from an IES VE simulation. Information on total air flow of fresh air (l/s) into a space and number of people present is gathered from the simulation in IES VE. The information is used to calculate an air flow rate in l/s per person and hereof productivity rate for each hour of the occupancy time, using the equation displayed above (equation 3). The result is averaged for all hours of occupancy

y = 0,0235ln(x) + 0,9536 R² = 0,9881

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Relative productivity

Ventilation rate, l/s per person

Ventilation rate (l/s per person)

ventilation rate (l/s perperson)Log. (ventilation rate(l/s per person))

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during the workdays. The percentage improvement in relative performance is multiplied with the average turnover per employee, which is multiplied with the number of occupants in the room. Thereby the total economical consequence for the relevant zone is calculated, regarding ventilation.

This calculation type allows for variable air flow calculations as well as calculations on variable occupancy rates. When using IES VE to calculate temperatures, the ventilation calculation type 2 should be used.

TEMPERATURE The temperature calculation can be done as estimation with few inputs but high uncertainties, or with simulation-based input from IES VE. The latter is preferable, because of the higher accuracy of the input, and also because of the possible input types that increases the accuracy of the calculations. The estimation-type is based on a number of hours in specific temperature intervals for a base-situation and a case-situation. The simulation-type is based on output from the simulation program IES VE.

The backbone of both calculation types is a PMV/relative productivity-relation. It is described in the section PMV/ relative productivity-relation, found below.

PMV/RELATIVE PRODUCTIVITY-RELATION The dose-response relationship between thermal sensation and relative productivity can be seen in Figure 3. The relative productivity among a population (for example workers in a landscaped office), is the average of the individual thermal sensation votes is named PMV (Predicted Mean Vote).

Because of individual preferences, and thereby a distribution of thermal sensation votes spanning parts of the scale, the PMV cannot directly be used to predict a relative performance. An expected distribution of votes related to each PMV-value has therefore been created. From the standard DS/EN ISO 7730, page 5, suggested distributions for PMV-values are listed, see Figure 6. Using the criteria in the ISO-standard table distribution suggestions are made for each PMV-value, and used in further calculations of relative productivity. The distributions are displayed in Figure 7. For an analysis on the validity of the distributions and the possibility for several alternative distributions, please see Appendix A. The analysis shows that there are little or no variation possibilities for the distributions while complying with the criteria listed in table 2 in ISO-7730.

Figure 6 - DS/EN ISO-7730 page 5, table 2; expected distribution of thermal sensation votes for a population with different values of PMV.

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Figure 7 - distributions of thermal sensation votes (tsv) for each PMV (0, +0,5, +1, +2, and mirrored for corresponding distributions of negative PMV-values).

For each thermal sensation vote a relative productivity-value is read from Figure 3, and the distribution is used to calculate a weighed relative productivity for each PMV-value. The results are displayed in Table 1.

PMV Productivity PPD 3 0,963 99,1 2 0,979 76,8 1 0,991 26,1

0,5 0,995 10,2 0 0,997 5,0

-0,5 0,998 10,2 -1 0,997 26,1 -2 0,992 76,8 -3 0,983 99,1

Table 1- resulting relative productivity, PPD and PMV-relation

CALCULATION TYPE 1; ESTIMATION Type 1 calculation is a guesstimate, thus it is designed to be used only as a pointer in situations where a simulation is not possible to complete. Simulations should always be the preferred option when calculating the improvement in performance due to temperatures. Because of the many factors influencing the thermal comfort, many had to be simplified or assumed in order to complete the estimation in simulation type 1 with the input simplicity that was desired.

The input for the estimation is a total number of hours above 27 °C, total number of hours above 25 °C (also including the hours above 27 °C) and the grand total of all relevant hours. For the case-estimation another number of hours in the intervals are defined.

Clothing 0,5 clo Air velocity 0,05 m/s RH 50 % Activity 1,1 met

Table 2- input values for PMV-calculation

The temperatures are run through a PMV macro built on ISO 7730-methodology, by Takahiro Sato, Tanabe laboratory, Waseda University [http://www.tanabe.arch.waseda.ac.jp/, 21.08.2013]. Assuming a clo-value of 0.5 clo, activity level of 1.1 met, air velocity of 0.05 m/s and a relative humidity of 50 % a PMV are

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calculated for each integer from 21 to 29 °C. The air temperature and mean radiant temperature are assumed identical for simplicity.

Clothing value [clo] 0,5 Air velocity [m/s] 0,05

Activity level [met] 1,1 Relative humidity [%] 50 Air temperature [°C] 21 22 23 24 25 26 27 28 29

Mean radiant temperature[°C] 21 22 23 24 25 26 27 28 29 PMV -1,47 -1,13 -0,78 -0,44 -0,09 0,25 0,60 0,94 1,29

Table 3 - PMV macro calculation for relevant temperatures

Using the calculated productivity index for each PMV (see section PMV/relative productivity-relation); the relative productivity for each temperature is calculated. The average of each whole temperature in the input intervals is calculated:

Interval Included temperatures [°C]

< 25 [°C] 21, 22, 23, 24°C 25-27 [°C] 25, 26°C >27 [°C] 27, 28, 29°C

Table 4 - the temperatures that are included in each input interval

< 25 [°C] 25-27 [°C] >27 [°C]

PMV -0,782 0,25 0,94 Relative productivity 0,9977 0,9963 0,9915

Table 5- the resulting PMV and productivity for each interval.

The improvement from base to case is defined in the input as a change in number of hours in the over-temperature-intervals compared to the total sum of hours. The reduced hours in the intervals >27°C and <25°C is assumed to be improved to the temperature interval 20-25°C. The weighed relative productivity for the whole period is calculated for base- and for case-situations, and the difference between these numbers is the final improvement in relative productivity.

There are large uncertainties connected to this type of calculation. It is based only on temperatures, which requires assumptions for all other inputs to the PMV calculation. The intervals are wide, as to reduce the number of inputs required, which also reduces the accuracy of the calculations. Furthermore, it does not take into account yearly variations in clothing, nor air temperature and radiant temperature differences, as it assumes that these are identical. The input for the estimation-type calculation will probably be based upon educated guesses derived from experience, and the output will never be any better than the quality of the initial guess.

Example: In zone X the base-situation includes 200 hours above 27 °C and 350 hours above 25°C, while after an improvement (case) the number of hours is 50 hours above 27°C and 120 hours above 25°C. Zone X is functioning 2400 hours per year.

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Hours >25℃ Hours >27℃ Zone X before 350 200 Zone X after 120 50

Table 6 - input for example zone X

25-27[°C] 27-29[°C] Zone X before 6% 8% Zone X after 3% 2%

Table 7 - Calculated share of hours in each interval. For the interval 25-27 °C please note that the input for hours above 25°C also includes the hours above 27°C. The interval 25-27 °C consists of 350-200=150 hours

for the base-situation and 120-50=70 hours for the case-situation.

The improvement during the total hours is calculated as:

Base: 6 % ∙ 0.9963 + 8 % ∙ 0.9915 + (100 − 6 − 8)% ∙ 0.9977 = 99.71%

Case: 3 % ∙ 0.9963 + 2 % ∙ 0.9915 + (100 − 3 − 2)% ∙ 0.9977 = 99.76%

Improvement total hours: 99,71 % − 99,76 % = 𝟎.𝟎𝟓 %

For the specific hours with a temperature improvement the result is a 0.46 % productivity increase.

CALCULATION TYPE 2; SIMULATION The type 2 calculation of relative performance increment due to temperatures is based on output from IESVE-simulations for the specific building. The simulation output consists of air temperatures [°C], mean radiant temperature [°C] and relative humidity [%], for each hour of one year. The PMV is calculated using previously mentioned PMV-calculation tool with specific clo-values (as described in section Clo-values found below), a metabolic rate of 1.1 met and an air velocity of 0.05 m/s. The relative productivity for each hour is calculated based on the resulting PMV and the PMV/relative productivity- relation described in section PMV/relative productivity-relation. The resulting productivity increment is the difference between the base and case average relative productivity during working hours.

CLO-VALUES The simulation-based calculation provides three options for clothing value input.

With the user defined clo-value the user can define a clo-value that is valid for the whole year. This input does not take into account seasonal variations, and is applicable where there is a dress code, specific uniforms or where the building is a located near equator with a near constant outdoor temperature. The program will calculate a PMV for each hour based on the user-defined clo-value.

Another input possibility is the seasonal clo-value, which differentiates between summer clothing (clo 0.5) and winter clothing (clo 1). The user defines which months that should be included in the summer period. For instance the user can define the period 1. May to 1. October as summer, so that it follows the standard non-heating period in Denmark. The program will calculate a PMV for each hour based on the seasonal clo-value.

The location-based clo-value is calculated based on weather-data from the location of the building. According to a study [De Carli et al, (2006)] the relationship between the clothing value and the outdoor temperature at 06.00 in the morning can be expressed as:

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𝐼𝑐𝑙 = 0,766 − 0,01 ∙ 𝑡𝑜𝑢𝑡𝑑𝑜𝑜𝑟,06.00

This expression does not take into account clothing adjustment during the day. For simplicity, the clothing is therefore assumed to remain the same throughout the day in the calculation. Weather data for locations can be added to the calculation program in a sheet specifically for weather data input. In Figure 8 the clothing values are displayed for three locations as an example; Copenhagen, Amsterdam and Kuala Lumpur. The weather files are the same as the weather data available for simulations in IES VE. The location-based calculation of clo-values provides an annual variation profile for clothing. It expresses the clothing value on basis of the local weather, and will therefore provide a better result than the seasonal clo-value when paired with the IESVE simulation data, which is also based on local weather data. Therefore, if using simulation input created with the use of weather data it is recommended that this is also chosen for the clothing value of the occupants.

Figure 8 - examples of clo-values during a year calculated for three different locations; Copenhagen, Amsterdam and Kuala Lumpur

COMBINED EFFECT The performance increments from ventilation and temperatures cannot simply be added to obtain the combined performance increment. In REHVA Guidebook #6 the following is suggested:

The magnitude of the combined effects is at least the effect of the greater of the single parameters, and not more than the sum of the independent parameters.

This means that if the productivity increment is 1 % due to temperature improvements and 2 % due to higher air flow rates, the combined productivity increment is minimum 2 % and maximum 3 %.

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CASE STUDY EXAMPLE To test the indoor climate tool, a part of Grontmij’s domicile has been modelled in IES VE. One base case and two improvement cases have been investigated: one where a solar film has been added to the windows to improve the thermal conditions, and one where the model is improved by adding mechanical ventilation to the previously naturally ventilated building.

Base Case 1 Case 2 Windows g-value 0,6 0,25 0,6 Infiltration 0,25 h-1 0,25 h-1 0,25 h-1 Ventilation 1,75 h-1

during occupancy if toperative>25°C toutside>14°C

1,75 h-1

during occupancy if toperative>25°C toutside>14°C

1,75 h-1 during occupancy

Cooling None None tin=18°C Internal solar screening, blinds

Deactivated at 100 W/m2 Activated at 200 W/m2

Shading coefficient: 0,25 Short-wave radiation

fraction: 0,25

None

Deactivated at 100 W/m2 Activated at 200 W/m2

Shading coefficient: 0,25 Short-wave radiation

fraction: 0,25 Employees 32 32 32 Average turnover per employee 1.000.000 DKK 1.000.000 DKK 1.000.000 DKK

Clothing After location* After location* After location* Table 8 - The alterations between base simulation and cases (*see section “Clo-values” in chapter

“Methods”)

Figure 9- The model build-up. The red areas in the drawing to the left are the relevant building parts that are included in the simulation, and the red areas in the figure to the right is the area whose results are displayed in the following graphs.

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CASE 1, SOLAR FILM The solar film added to all windows was expected to lower the temperatures in the room, as it lowers the solar heat gain. This is the case, as can be seen in Figure 10, where the case operative temperature is lower than the base operative temperature, especially during the summer weeks where the case peaks are significantly lower than the base peaks. This has consequences for the relative productivity, as the case-productivity is slightly higher than the base-productivity during the summer weeks.

Figure 10 - temperature and productivity comparison base and case 1

As the operative temperature is high during the summer months the PPD is also elevated during this period, where it spikes to about 40 % for base case and 30 % for case 1, see Figure 11. This is of course an improvement. It can also be read from figure that during the winter weeks the case-PPD is higher than the base-PPD, presumably because the temperatures are lower during the heating season as the solar heat gains are reduced.

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Productivity Base Productivity Case

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Figure 11 - temperature and PPD comparison base and case 1

Regarding the ventilation air flows it is observed, see Figure 12, that the amount of fresh air introduced to the space is smaller for the case-simulation than the base-simulation. It suggests that the lower temperature indoors reduces the incentive to open the windows, and thus lowers the ventilation rate. The case-productivity related to ventilation is lower than the base-productivity, and thus offsets the productivity increment from the temperature decline.

Figure 12 - air flow and productivity comparison base and case 1

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PPD Base PPD Case

0,930,940,950,960,970,980,9911,01

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[24]

CASE 2, MECHANICAL VENTILATION The ventilation case includes both an improvement in ventilation, but also in temperatures as a cooling coil is included. The results, which are displayed in Figure 13, show that the temperatures are reduced during summer, but the temperatures are also lower during winter weeks. Compared to case 1 the temperatures are reduced more with a mechanical ventilation system (case 2). As in case 1 the lowered temperatures during summer reduces the maximum dips in productivity. During the winter weeks the lowered temperatures from the mechanical ventilation causes the productivity to decline. The last weeks during the year it can be seen that although the operative temperatures are almost the same, the productivity is lower for case 2 than base. This may be due to other factors influencing the PMV-value, such as the relative humidity or else the weighing between the air temperature and the mean radiant temperature being something other than 0.5 (the average between air temperature and mean radiant temperature is used for the graphs, as operative temperature is not extracted from IES VE).

Figure 13 - temperature and productivity comparison base and case 2

It is observed in Figure 14 that the PPD is reduced from 40 % to 25 % during summer from base to case 2, while the PPD actually increases during the rest of the year. In the base-situation the PPD level is relatively stable between 5 and 15 %, in contrast to case 2 where it varies between 5 and 30 %. This means that most of the time the building is more comfortable in base than case 2. Again it is seen how the operative temperature is not the sole contribution to the PPD-estimation and therefore also PMV and productivity. In this case especially relative humidity differs by being higher for the naturally ventilated building, which has an effect on the comfort of the residents.

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[25]

Figure 14 - temperature and PPD comparison base and case 2

Due to the constant ventilation in case 2 is also constant, where the natural ventilation in the base-situation causes the ventilation rate per person to rise during summer where the windows are opened more frequently. This results in an elevated productivity during summer weeks. The mechanical ventilation enjoys a constant air change throughout the year, which means that the relative productivity from ventilation is also constant throughout the year, in contrast to the base-situation. As the air flow rate from the mechanical ventilation is the same size as the maximum flow rate from the natural ventilation, the average productivity rate through each week is always higher with mechanical ventilation.

Figure 15 - air flow and productivity comparison base and case 2

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[26]

RESULT SUMMARY The results show that case 1 is not a feasible investment, even if the temperatures drop during the summer, causing a productivity increment of 0.05 %, see Table 9. The drop in temperatures indirectly leads to a lowered ventilation rate, which influences the productivity negatively (-0.2 %, Table 10). The second case also enjoys an improvement in productivity due to temperature control, but where the ventilation is reduced in case 1, the ventilation is kept constantly high throughout the year in case 2. This has positive effects on the relative productivity. The result shows an increment of 3.9 % from ventilation, and 0.05 % from temperature control, Table 10 and Table 9 respectively.

Improvements from temperature (simulation) Building part Improvement Turnover Turnover (name) total hours [%] per employee all employees Case 1 0,05% 521,54 16.689,34 Case 2 0,05% 505,69 12.642,24

Table 9- calculation results for temperature increments in case 1 and case 2 increments (compared to base-scenario).

Improvements from ventilation (simulation) Building part Improvement Turnover Turnover (name) [%] per employee all employees Case 1 -0,2% -2.028 -64.903 Case 2 3,90% 38.983 974.587

Table 10 - calculation results for ventilation increments in case 1 and case 2 increments (compared to base-scenario).

As a consequence of the application of the solar film in case 1 the relative productivity of the office building declines, as the negative effect from ventilation outweighs the positive effects from temperature control, Table 11. When installing mechanical ventilation system that ensures constantly high air flow rate and some temperature control, the productivity of the office workers increase with between 3.9 and 3.95 %. It is seen that this productivity increase is equivalent to about 1 million DKK per year, which means that the ventilation system (if excluding running- and maintenance costs) is equivalent to hiring one extra employee.

Turnover per employee Turnover all employees min max min max min max

Case 1 -0,20% -0,15% -2.028 -1.507 -64.903 -48.214 Case 2 3,90% 3,95% 38.983 39.489 974.587 987.230

Table 11 - combined calculation results from temperature and ventilation productivity increments (minimum and maximum effects for case 1 and 2 compared to base case).

[27]

DISCUSSION The methodology used in this project has in many cases been simplified compared to the literature and theory it is based on. Simplifications have been made for the factors that were not feasible to include in the model. Air velocity input requires CFD-simulations with detailed information on the room build-up, air intakes and outtakes, occupant placement etc. In IES VE it is possible to choose air velocity as an output without running CFD-simulations, but this value is calculated in the centre of a room, and is thus not representative for all the working spaces, as the air velocity can vary throughout a space as a result of window placement, heat source placement, etc. Metabolic rate is also assumed constant. The activity level of occupants would otherwise be hard to estimate, as there is no way one can predict how many times or when occupants get up to get coffee, or activate their height adjustable tables. The tool cannot account for individual preferences, other than relying on statistical observations that have previously been made in science and that are integrated in the models used.

As there are numerous assumptions, models and approximations that lay basis for the calculations used when transforming environmental parameters to performance ratings, the results will inevitably be associated with uncertainty. This has made the transformation from theory to practice difficult. K.L Jensen et al (2009) used a Bayesian Network statistical method to include the uncertainties related to the available theoretical models. By using theoretical models, expert opinions and a database of 12,700 occupants in 124 buildings, probabilities of different combinations of indoor climate factors is calculated. With a certain combination of air temperature, clothing insulation, ventilation principle and air velocity, the distribution of the thermal sensation votes was determined and used to calculate a relative performance using the thermal sensation vote/productivity relationship. The Bayesian Network model is limited to calculations regarding the thermal environment at this time, but could be extended to also include ventilation. Where K.L. Jensen et al’s model uses a probability model to estimate the thermal sensation vote distribution; the indoor climate tool from this project uses a constant distribution (see Figure 7) based on information from ISO 7730. This constant distribution from ISO 7730 is based on the same data set as the BN-model uses, although it differs by not being variable for different conditions. This of course makes the indoor climate tool less accurate, but it is uncertain on what scale it influences the end result. However it is likely that the difference is small. In the study by Toftum et al (2009) several case studies were conducted using the BN-model, where the differences in relative performance between the configurations (a building with mechanical and natural ventilation) were lower than expected, even with large differences in temperatures. For naturally ventilated buildings the BN-model should be more accurate, as the indoor climate tool does not take into account the fact that people are more tolerant to temperature variations in naturally ventilated buildings. But if the variations catered for in the BN-method are of negligible size, this indoor climate tool can provide results of equivalent validity.

One of the very useful assets of the indoor climate tool this report is based upon is the compatibility it has with IES VE-simulation output, so that it is able to calculate the relative productivity each hour for a whole year. A yearly average temperature tells little about the temperature variations through seasonal changes or during especially hot or cold days. An average temperature would thus underestimate the productivity losses. Also in buildings with a variable air change rate or variable occupancy rates, the relative performance from ventilation cannot be calculated accurately from averaged values. It is interesting to observe that in the case study example an addition to the building (solar film) that theoretically should increase the comfort and therefore also productivity actually causes the productivity to decline. It shows that the effect of an apparent improvement is not necessarily intuitively predictable.

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As IES VE is a widely used building simulation tool it is obvious that the indoor climate/productivity tool should be compatible. IES VE-simulations are often performed for a project in any case, so a further calculation of the productivity using the developed tool is an option that is uncomplicated and rapid. This means that an extra dimension of consultancy can be added to the simulations without spending extra time (and thus money).

Humans and the interaction with their physical environment have been studied for a long time, and the first studies were conducted in 1930’s, for example with Yanglou stating that ventilation could be used in order to remove odour and achieve thermal comfort [Yanglou, C.P., 1937]. Even with a long history, the models available today are associated with uncertainties, as individual preferences are still difficult to predict. The potential for a model that can predict performance in relation to indoor climate is valuable for employers, but a method for more certain estimations of productivity increment will be difficult to obtain. More statistics, with an even greater knowledge on conditions, such as ventilation rate, temperatures, air velocities, are needed. Also, it would be interesting to investigate and obtain a database on other institutions than office buildings, for example schools or universities. The performance increment from an improved indoor climate has been demonstrated even greater in learning institutions than they are in routine-work office buildings [Cui et al. 2013].

CONCLUSION The tool provides quantifications on the relative productivity in relation to ventilation rate and thermal comfort combined. Its accuracy is profoundly better with input from IES VE than estimation input.

Combining the productivity increment from thermal comfort and ventilation rate can provide an argument for improving the indoor climate in buildings.

The tool has proven useful for revealing non-intuitive effects of changes in the indoor climate, as exemplified with Case 1 in the case-examples.

By including the Bayesian statistics methodology the results could have been more accurate, but it is uncertain how large difference the varying distributions would actually make to the end result. This should be investigated further in order to quantify the difference accurately.

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REFERENCES Cui, W., Cao, G., Park, J.H, Ouyang, Q., Zhu, Y., 2013, Influence of indoor air temperature on human thermal comfort, motivation and performance, Building and environment 68, 114-122

De Carli, M., Olesen, B., Zarella, A., Zecchin, R., 2006, People’s clothing behaviour according to external weather and indoor environment, Building and Environment 42, 3965-3973 Edwards, L., Torcellini, P., 2002, A literature review of the effects of natural light on building occupants, National Renewable Energy Laboratory Fanger, P.O., 1970, Thermal comfort, Danish Technical Press, Copenhagen, Denmark

ISO 7730:2006

Jensen, K.L., 2008, Development of a model to calculate the economic implications of improving the indoor climate, Ph.D., Technical University of Denmark, Denmark

Kosonen, R., Tan, F., 2004; Assessment of productivity loss in air-conditioned buildings using PMV index, Energy and buildings 36, 987-993

Lahtinen, M., Huuhtanen, P., Kähkönen, E., Reijula, K., 2002, Psychosocial dimensions of solving an indoor air problem, Indoor Air 2002, 12; 33-46

Lund, S.P., Kristiansen, J., Persson, R., Shibuya, H., Toftum, J., Clausen, G., 2012, Cognitive test performance following exposure to noise in an open-office simulation study, Joint Baltic-Nordic Acoustics Meeting, BNAM2012

Pejtersen, J., Brohus, H., Hyldgaard, C. E., Nielsen, J.B, Valbjørn, O., Hauschildt, P.,Kjærgaard, S.K., Wolkoff, P., 2001,Effect of renovating an office building on occupants’ comfort and health, Indoor Air 2001; 11;10-25

REHVA Guidebook #6, Wargocki, P., Seppänen, O., Andersson, J., Boerstra, A., Clements-Croome, D., Fitzner, K.,Hanssen, S.O., 2006, Indoor Climate and Productivity in Offices, REHVA, Federation of European Heating and Air-conditioning Associations

Sagie, A., 1998, Employee absenteeism, organizational commitment and job satisfaction: another look, Journal of vocational behavior 52, 156-171

Seppänen O., Fisk, W.J., Faulkner, D., 2004, Control of temperature for health and productivity in offices, Lawrence Berkeley National Laboratory

Seppänen, O., Fisk, W.J., Lei, Q.H., 2006, Ventilation and performance in office work, Indoor Air 2006; 16; 28-36

Toftum, J., Andersen, R.V., Jensen, K.L., 2009, Occupant performance and building energy consumption with different philosophies of determining acceptable thermal conditions, Building and environment 44, 2009-2016

Wargocki, P., Sundell, J., Bischof, W., Brundrett, G., Fanger, P.O., Gyntelberg, F., Hansen, S.O., Harrison, P., Pickering, A., Seppänen, O., Wouters, P., 2002, Ventilation and health in non-industrial indoor environments: report from European Multidisciplinary Scientific Consensus Meeting (EUROVEN), Indoor Air 2002; 12; 113-128

Yanglou, C.P., Witheridge, W.N., 1937, Ventilation requirements, part 2: heating, piping and air conditioning, ASHRAE J., 43:1-4.

APPENDIX A An evaluation is made of the predicted PMV distribution, primarily to determine if the values set in the model of this report can be significantly altered if the distribution changes.

The model

The average of the thermal sensation votes should equal the actual PMV. As such, the PMV follows a distribution of productivity according to this equation

−3𝑥 − 2𝑦 − 𝑧 + 0𝑠 + 𝑎 + 2𝑏 + 3𝑐𝑥 + 𝑦 + 𝑧 + 𝑠 + 𝑎 + 𝑏 + 𝑐

= 𝑃𝑀𝑉

with x, y, z, s, a, b, and c equaling the percentage predicted to vote for thermal sensations of -3, -2, -1, 0, 1, 2, and 3 respectively.

For each PMV, certain conditions are predestined, seen in the table below from ISO 7730:

Notice that the parameters serve as a constant. Furthermore, the percentage predicted to vote is always a number between 0 and 100.

0 ≤

⎩⎪⎪⎨

⎪⎪⎧

𝑥𝑦𝑧𝑠𝑎𝑏𝑐

⎭⎪⎪⎬

⎪⎪⎫

≤ 100

A distribution for each real PMV

Baring this, for each real PMV the distribution above can be reduced to a linear system of five equations. One equation containing the parameters for percentages of the negative thermal sensation votes (x, y and z), one equation containing the parameters for percentages of the positive thermal sensation votes (a, b and c) and three equations represent the relation between the three paired parameters for the vote percentages (a and z, b and y, and c and x).

PMV = 2

The distribution of thermal sensation votes can be reduced to the following system of equations:

−6𝑥 − 4𝑦 − 2𝑧 = 0 𝑎𝑛𝑑 2𝑎 + 4𝑏 + 6𝑐 = 400

Real PMV

Percentage predicted to vote s a + z b + y c + x

2 5 20 45 30 1 30 45 20 5

0,5 55 35 7,5 2,5 0 60 35 5 0

-0,5 55 35 7,5 2,5 -1 30 45 20 5 -2 5 20 45 30

𝑎 = 20 − 𝑧 𝑎𝑛𝑑 𝑏 = 45 − 𝑦 𝑎𝑛𝑑 𝑐 = 30 − 𝑥

Since none of the parameters are allowed to be below 0, there exists only one solution to this system: x = 0, y = 0, z = 0, a = 20, b = 45 and c = 30.

PMV = 1

The distribution of thermal sensation votes can be reduced to the following system of equations:

−6𝑥 − 4𝑦 − 2𝑧 = 0 𝑎𝑛𝑑 2𝑎 + 4𝑏 + 6𝑐 = 200

𝑎 = 45 − 𝑧 𝑎𝑛𝑑 𝑏 = 20 − 𝑦 𝑎𝑛𝑑 𝑐 = 5 − 𝑥

Again, since none of the parameters are allowed to be below 0, there exists only one solution to this system: x = 0, y = 0, z = 0, a = 45, b = 20 and c = 5.

PMV = 0,5

The distribution of thermal sensation votes can be reduced to the following system of equations:

−6𝑥 − 4𝑦 − 2𝑧 = −7 𝑎𝑛𝑑 2𝑎 + 4𝑏 + 6𝑐 = 108

𝑎 = 35 − 𝑧 𝑎𝑛𝑑 𝑏 = 7,5 − 𝑦 𝑎𝑛𝑑 𝑐 = 2,5 − 𝑥

For this distribution there exist an infinite number of solutions. However, the solutions lie within a relatively narrow field, since each of the variables is withheld within a certain range:

0 ≤ �𝑎𝑧� ≤ 35 𝑎𝑛𝑑 0 ≤ �𝑏𝑦� ≤ 7,5 𝑎𝑛𝑑 0 ≤ �𝑐𝑥� ≤ 2,5

PMV = 0

The distribution of thermal sensation votes can be reduced to the following system of equations:

−4𝑦 − 2𝑧 = −45 𝑎𝑛𝑑 2𝑎 + 4𝑏 = 45

𝑎 = 35 − 𝑧 𝑎𝑛𝑑 𝑏 = 5 − 𝑦 𝑎𝑛𝑑 𝑐 = 0 − 𝑥

It should be noted that c and x both are negligible in this distribution. An infinite number of solutions exist for this distribution, although, as with PMV = 0,5, it lies within a narrow field, withheld by the range its variables can span:

0 ≤ �𝑎𝑧� ≤ 35 𝑎𝑛𝑑 0 ≤ �𝑏𝑦� ≤ 5

PMV = -0,5

The distribution of thermal sensation votes can be reduced to the following system of equations:

−6𝑥 − 4𝑦 − 2𝑧 = −108 𝑎𝑛𝑑 2𝑎 + 4𝑏 + 6𝑐 = 7

𝑎 = 35 − 𝑧 𝑎𝑛𝑑 𝑏 = 7,5 − 𝑦 𝑎𝑛𝑑 𝑐 = 2,5 − 𝑥

This distribution mirrors the distribution of PMV = 0,5. As such, it has an infinite number of solutions, although all within a narrow field since each variable is withheld within a certain range:

0 ≤ �𝑎𝑧� ≤ 35 𝑎𝑛𝑑 0 ≤ �𝑏𝑦� ≤ 7,5 𝑎𝑛𝑑 0 ≤ �𝑐𝑥� ≤ 2,5

PMV = -1

The distribution of thermal sensation votes can be reduced to the following system of equations:

−6𝑥 − 4𝑦 − 2𝑧 = −200 𝑎𝑛𝑑 2𝑎 + 4𝑏 + 6𝑐 = 0

𝑎 = 45 − 𝑧 𝑎𝑛𝑑 𝑏 = 20 − 𝑦 𝑎𝑛𝑑 𝑐 = 5 − 𝑥

The opposite of the PMV = 1 distribution, there is only one solution: x = 5, y = 20, z = 45, a = 0, b = 0, c = 0.

PMV = -2

The distribution of thermal sensation votes can be reduced to the following system of equations:

−6𝑥 − 4𝑦 − 2𝑧 = −400 𝑎𝑛𝑑 2𝑎 + 4𝑏 + 6𝑐 = 0

𝑎 = 20 − 𝑧 𝑎𝑛𝑑 𝑏 = 45 − 𝑦 𝑎𝑛𝑑 𝑐 = 30 − 𝑥

The opposite of the PMV = 2 distribution, there is only one solution: x = 30, y = 45, z = 20, a = 0, b = 0, c = 0.

Final notes:

Four out of the seven distributions have a single solution. Of the other three distributions, two are mirrors of each other. The last one is the PMV = 0 distribution. For this distribution, a very good case can be made for distributing the value of the parameters equally on either side of the s vote (which is set), i.e. z being equal to a and y being equal to b (meaning that a = z = 17,5 and b = y = 2,5).

For the PMV = -0,5 and PMV = 0,5 distributions a little more fiddling can be done with the parameters and still give a reasonable distribution. However, it should be noted that the values will move very little. For example; giving these distributions if the real PMV is 0,5, the maximum predicted voting percentage for a thermal sensation vote of -1 (parameter a) will be only 3,5% and this leaves no percentages capable of voting for -2 or -3. The distribution of PMV = 0,5 is thus heavily weighted towards prediction of positive thermal sensation votes. The opposite is of course the case with the PMV = -0,5 distribution.