Final Report of the MSc Graduation Project · 3 Acknowledgements This graduation project is the...

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Final Report of the MSc Graduation Project

Energy performance prediction for heating and cooling under

uncertainties of occupant behaviour: A case study for the Atlas

building on TU/e Campus in connection with the ATES system

Master program: Sustainable Energy Technology- SELECT, EIT InnoEnergy

Department: Built Environment

Research group: Building Performance

Student: YuHsuan Wang

Identity number: 1374087

Thesis supervisor: prof.dr.ir. J.L.M. Hensen

Co-supervisors: dr.ir. P. Hoes

E. Kyrou, PDEng trainee SB&C

Date: Aug, 2019

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Acknowledgements This graduation project is the final work of my 2-year journey in the InnoEnergy SELECT program. The

project could not be completed without others’ support. I would like to thank my first supervisor,

prof.dr.ir. Jan Hensen, who always gave enlightening advice on building performance analysis in each

monthly meeting, and dr.ir. Pieter-Jan Hoes (second supervisor) who gave informative guidance and

suggestions in the early stage of the project, in order to converge the scope of the project. In addition,

I really appreciate all the help from my daily advisor, Evangelos (PDEng trainee SB&C), who is always

supportive and gave me valuable advice on the model development and guide me to move forward in

each discussion.

Returning to academic study after a 3-year work as a mechanical engineer did take me some time to

catch up with the study, especially energy system analysis (in built environment) is a new domain that

I have never studied before. Despite all challenges during the study and long period time of work on

this project, I am glad I made this choice. I also want to thank all the friends I made in Stockholm and

Eindhoven to enrich my study life, and especially thank my family and my best friends who always

support me during the study.

YuHsuan Wang

August 2019

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Table of Contents Acknowledgements ................................................................................................................................. 3

List of Figures ........................................................................................................................................... 6

List of Tables ............................................................................................................................................ 8

Wordlist and abbreviations ..................................................................................................................... 9

Definition of terms .................................................................................................................................. 9

List of Symbols ......................................................................................................................................... 9

Summary ............................................................................................................................................... 10

Keywords ............................................................................................................................................... 10

1 Background of the project ............................................................................................................. 11

Introduction of the Atlas building ......................................................................................... 11

Previous research related to the Atlas building .................................................................... 12

Problem definition and research questions .......................................................................... 12

2 Literature review of occupant behaviour (OB) and occupant behaviour modelling .................... 13

3 Methodology ................................................................................................................................. 15

Key performance indicators .................................................................................................. 16

Determine the complexity of building models ...................................................................... 17

Benchmark model development ........................................................................................... 18

3.3.1 Construction types ........................................................................................................ 19

3.3.2 Infiltration ...................................................................................................................... 19

3.3.3 Representative occupancy schedules............................................................................ 19

3.3.4 Heating, ventilation and air conditioning (HVAC) ......................................................... 20

3.3.5 Internal heat gains ......................................................................................................... 21

3.3.6 Internal mass ................................................................................................................. 21

3.3.7 Zone setup ..................................................................................................................... 21

3.3.8 Weather profiles ............................................................................................................ 23

3.3.9 Other assumptions/settings .......................................................................................... 23

Define scenarios of occupant behaviour ............................................................................... 23

Define control strategies ....................................................................................................... 24

4 Results and discussion ................................................................................................................... 27

Annual heating and cooling demand of the Atlas building ................................................... 27

4.1.1 Benchmark model ......................................................................................................... 27

4.1.2 Comparison to reference values ................................................................................... 28

Sensitivity analysis of occupant behaviour ........................................................................... 29

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Sensitivity analysis of control strategies with OB .................................................................. 32

5 Conclusions and recommendations .............................................................................................. 35

6 Limitations of the project and future work ................................................................................... 37

7 References ..................................................................................................................................... 38

8 Appendix ........................................................................................................................................ 41

A. Detailed floor layouts with furnishings ..................................................................................... 41

B. Classification of zones ............................................................................................................... 44

C. Timeslots of the courses ............................................................................................................ 45

D. Determine a suitable complexity of the departments .............................................................. 45

E. Construction types .................................................................................................................... 49

F. A zone dialog box of a representative thermal zone ................................................................ 51

G. Infiltration of open windows ..................................................................................................... 51

H. Commands and setup of occupant behaviour .......................................................................... 52

I. Commands and setup of control strategies .............................................................................. 56

J. Detailed annual heating and cooling demand of the Atlas building (Benchmark) ................... 59

K. Calculation of on-site energy demand ...................................................................................... 60

L. Detailed annual heating and cooling demand of the Dep. 4S ................................................... 61

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List of Figures Figure 1.1 A captured map of TU/e campus [8] .................................................................................... 12

Figure 3.1 Methodology of the research ............................................................................................... 15

Figure 3.2 The flowchart of model development .................................................................................. 16

Figure 3.3 The Atlas 3-D building model ............................................................................................... 16

Figure 3.4 The flowchart of determining the complexity of building models ....................................... 17

Figure 3.5 Three compartments in a floor (the 4th floor) ...................................................................... 17

Figure 3.6 The layout of the southern double storied department on the 4th floor (left) and 5th floor

(right) ..................................................................................................................................................... 18

Figure 3.7 The layout of the middle department on the 4th floor (top) and 5th floor (bottom) with

determined complexity ......................................................................................................................... 18

Figure 3.8 The representative occupancy schedule .............................................................................. 20

Figure 3.9 The ventilation schedule ...................................................................................................... 20

Figure 3.10 Thermal zone classification of the Atlas building. Same shading colours has similar

characteristics........................................................................................................................................ 22

Figure 3.11 Class B requirements for the indoor operation temperature (with an expected percentage

of dissatisfied people of a maximum 10%), in relation to the running mean outdoor temperature. A

setpoint advice has been given by the dotted blue line in accordance with the recommendations in

NEN-EN-ISO 7730. This figure was adapted from Fig 3.2 of ISSO-74 [24]. ............................................ 25

Figure 4.1 Annual heating and cooling demand of the Atlas building (Benchmark model) ................. 27

Figure 4.2 Annual heating and cooling demand of on the floors 2-11: departments with different

orientation ............................................................................................................................................. 28

Figure 4.3 Annual heating and cooling demand density: departments with different orientation ...... 28

Figure 4.4 Variation of annual cooling demand: adjusting the cooling setpoint (left: in %/ right: in MWh)

............................................................................................................................................................... 31

Figure 4.5 Variation of annual heating demand: adjusting the heating setpoint (left: in %/ right: in

MWh) ..................................................................................................................................................... 31

Figure 4.6 Variation of annual heating and cooling demand: blind operation (left: in %/ right: in MWh)

............................................................................................................................................................... 31

Figure 4.7 Variation of annual heating and cooling demand: window operation (left: in %/ right: in

MWh) ..................................................................................................................................................... 32

Figure 4.8 Annual heating demand of the Atlas building ...................................................................... 32

Figure 4.9 Annual cooling demand of the Atlas building ...................................................................... 33

Figure 4.10 Heating and cooling ratio of the Atlas building .................................................................. 33

Figure 4.11 Excess heating demand of the Atlas building ..................................................................... 33

Figure 8.1 The layout of the Ground floor............................................................................................. 41

Figure 8.2 The layout of the 1st floor ..................................................................................................... 41

Figure 8.3 The layout of the 2nd floor .................................................................................................... 41

Figure 8.4 The layout of the 3rd floor ..................................................................................................... 41

Figure 8.5 The layout of the 4th floor ..................................................................................................... 42





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Figure 8.10 The layout of the 9th floor ................................................................................................... 42

Figure 8.11 The layout of the 10th floor ................................................................................................. 43

Figure 8.12 The layout of the 11th floor ................................................................................................. 43

Figure 8.13 The layouts of the -1th and the -2nd floor ............................................................................ 43

Figure 8.14 The occupancy schedule of classrooms ............................................................................. 44

Figure 8.15 The occupancy schedule of meeting rooms ....................................................................... 44

Figure 8.16 The occupancy schedule of open sitting spaces................................................................. 44

Figure 8.17 The occupancy schedule of offices ..................................................................................... 44

Figure 8.18 Timeslots of the courses in TU/e [41] ................................................................................ 45

Figure 8.19 The layout (the highest resolution) of the southern double storied department on the 4th

floor (left) and 5th floor (right) ............................................................................................................... 45

Figure 8.20 The layout (the lowest resolution) of the southern double storied department on the 4th

floor (left) and 5th floor (right) ............................................................................................................... 46

Figure 8.21 The layout (the highest resolution) of the middle double storied department on the 4th

floor (top) and 5th floor (bottom) .......................................................................................................... 47

Figure 8.22 The layout (the lowest resolution) of the middle double storied department on the 4th

floor (top) and 5th floor (bottom) ......................................................................................................... 47

Figure 8.23 The layout (moderate resolution) of the middle double storied department on the 4th floor

(top) and 5th floor (bottom) ................................................................................................................. 48

Figure 8.24 The window setting ............................................................................................................ 49

Figure 8.25 Details of the floor .............................................................................................................. 49

Figure 8.26 Details of the ceiling ........................................................................................................... 49

Figure 8.27 Details of the partition wall ................................................................................................ 49

Figure 8.28 Details of the facade (underground) .................................................................................. 49

Figure 8.29 Details of the roof ............................................................................................................... 50

Figure 8.30 Details of the floor (Dep 2.S and 2.N) ................................................................................. 50

Figure 8.31 Details of the floor of the ground floor .............................................................................. 50

Figure 8.32 Details of the floor of the basement 1 ............................................................................... 50

Figure 8.33 Details of the ceiling of the basement 1 ............................................................................. 50

Figure 8.34 The zone dialog box of Dep 4.S .......................................................................................... 51

Figure 8.35 The model layout: the reference scenario ......................................................................... 52

Figure 8.36 The model layout: Window operation ............................................................................... 53

Figure 8.37 The model layout: Blind operation (Case A) ....................................................................... 54

Figure 8.38 Commands of SwitchE_Weekday: Blind operation during weekday, eastern side ........... 54

Figure 8.39 The model layout: Blind operation (Case B) ....................................................................... 55

Figure 8.40 The model layout: Strategy of increasing cooling setpoints .............................................. 56

Figure 8.41 The model layout: Natural day cooling .............................................................................. 57

Figure 8.42 The model layout: Night flushing ....................................................................................... 58

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List of Tables Table 2.1 Overview of the most common occupant behaviour modelling approaches according to size,

resolution and complexity [11] ............................................................................................................. 13

Table 3.1 Settings of the HVAC .............................................................................................................. 21

Table 3.2 Assumptions of three types of internal gains with the corresponding data from TRNBuild

library .................................................................................................................................................... 21

Table 3.3 Overview of 11 representative thermal zones ...................................................................... 22

Table 3.4 The defined OB scenarios ...................................................................................................... 23

Table 3.5 Summary of four control strategies ....................................................................................... 24

Table 4.1 Energy density for heating and cooling of the building between different calculation methods

............................................................................................................................................................... 29

Table 4.2 Comparison of on-site energy demand of different buildings .............................................. 29

Table 4.3 H/C and H-C of the 16 defined OB scenarios (Dep 4.S, without control strategies) ............. 30

Table 4.4 Total heating and cooling demand of the building with different control strategies and OB.

H-C variation refers to the difference between (H-C)best and (H-C)worst ................................................ 34

Table 8.1 Characteristics of 4 types of zone .......................................................................................... 44

Table 8.2 Heating and cooling demand of two models (Dep 4.S and Dep 5.S) ..................................... 46

Table 8.3 Heating and cooling demand of three models (Dep 4.M and Dep 5.M) ............................... 48

Table 8.4 Detailed demand of each thermal zone ................................................................................ 59

Table 8.5 Detailed demand of departments with different orientation ............................................... 59

Table 8.6 Annual heating and cooling demand of 16 OB scenarios (Dep 4.S, without control strategies)

............................................................................................................................................................... 61

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Wordlist and abbreviations Word Description

ATES Aquifer thermal energy storage

BPS Building performance simulation

BREEAM Building Research Establishment Environmental Assessment Method

CCISHADE fraction of the solar radiation absorbed by the internal shading element that is transferred to the air node by additional convection between the inner window pane and the internal shading element

COP Coefficient of performance

Dep S The southern side department of the Atlas building

Dep M The department in the middle of the Atlas building

Dep N The northern side department of the Atlas building

FF Fraction of reference floor area

H/C Annual heating/cooling ratio

H-C Excess annual heating demand

HVAC Heating, ventilation and air conditioning

IEA International Energy Agency

IWEC International weather for energy calculations

OB Occupant behaviour

PEF Primary energy factor

KPIs Key performance indicators

RMOT Running mean outdoor temperature

SER Satellite equipment rooms

TU/e Eindhoven University of Technology

Definition of terms Term Definition

Benchmark model The model with the reference OB scenario and without any control strategy

Data centre Technical rooms, SER, ICT rooms in the Atlas building

Reference scenario The scenario without any occupant behaviour from thermostat /window/blind control

List of Symbols Symbol Unit Description

ACH 1/h Air change rate

Atot,od m2 Total area of the double floor open sitting space

Adep m2 Total area of a department

U-value W/m2K Thermal transmittance

R-value m2K/W Thermal resistance

g-value - Solar energy transmittance

n - Efficiency of heat recovery of the mechanical ventilation system

Tamb ℃ Ambient (outdoor) temperature

Tzone ℃ Temperature of thermal zone

Tin ℃ Temperature of supplied air

Tcool,sp ℃ Cooling setpoint

𝜃𝑒𝑑−1 ℃ The average outdoor temperature from yesterday

𝜃𝑒𝑑−2 ℃ The average outdoor temperature from the day before yesterday

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Summary According to the 2020 plan of Eindhoven University of Technology (TU/e), the campus aims to reduce

its energy consumption by 30% by 2020 (compared to 2005). TU/e has installed an aquifer thermal

energy storage (ATES) system and it has connected to around 70% of the buildings on the campus to

achieve this goal. One of the regulations of ATES systems in the Netherlands states that the heat being

injected is obligatory to be the same as the heat being extracted from the subsurface throughout a

year, in order to keep the subsurface thermal balanced. At present, the excess heat of the ATES system

is released from two cooling towers.

The Atlas building, which recently finished its renovation, also connects to the ATES system. The

building has a high BREEAM score (96.01%) and several researchers are working on the control

strategies of the building. Previous studies showed that the Atlas building has potential to improve the

thermal balance of the ATES system. However, the influence caused by uncertainties of occupant

behaviour (OB) has not been considered.

In this project, four control strategies with uncertainties of OB were investigated, in order to achieve

better operational performance of the ATES system. A fit-for-purpose model was developed under the

determined thermal zone complexity and the 16 defined OB scenarios, in regard to thermostat

adjustment, window and blind operation. Key performance indicators (KPIs) are the annual heating

and cooling demand, the heating/cooling ratio (H/C) and the excess annual heating demand (H-C).

Annual heating and cooling demand of the benchmark model is 683 MWh and 215 MWh respectively,

which leads to a H/C of 3.2 and a H-C of 468 MWh. The investigation showed that the defined OB

scenarios cause considerable impact on the heating and cooling demand, especially the cooling

demand. Among the three aspects of OB, the thermostat adjustment has the largest impact on the

heating and cooling demand of the building, while the window operation brings the smallest impact

on the demands.

Introducing control strategies to the building increases both H/C and H-C, and the defined OB scenarios

under different control strategies will lead to different level of variation of KPIs. Based on the final

results of the sensitivity analysis, the thermostats are recommended to be freely adjusted in the range

of ±1℃, the blind operation is suggested to be limited in the summer, and the windows can be freely

operated throughout the year. Based on the robustness of performance to OB, two control strategies

which are increasing cooling setpoints and night flushing are recommended to introduce to the

building, which leads to a H/C of 8.1 and a H-C of 598 MWh.

Keywords Energy performance prediction, ATES systems, occupant behaviour, control strategies, TRNSYS,

sensitivity analysis, annual heating and cooling demand

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1 Background of the project Buildings are responsible for approximately 40% of energy consumption and 36% of CO2 emissions in

the European Union, offering the highest potential for efficiency improvement and savings on energy

bills [1].

Eindhoven University of Technology (TU/e) plans to reduce its energy consumption by 30% by 2020 (in

comparison to 2005). 20% of the savings are expected to come from buildings on the campus. TU/e

has installed an aquifer thermal energy storage (ATES) system, which is one of the largest ATES systems

in Europe. Around 70% of the buildings on TU/e campus are connected to the ATES system. The

Campus 2020 plan aims to fully supply heating and cooling to the buildings by the ATES system, in

combination with heat pumps and low-temperature heating systems to reduce the heating demand

and replace the usage of natural gas. TU/e is expected to save around three million kWh of electricity

consumption and more than 450,000 m³ of gas consumption from the ATES system. [2]

The annual heating and cooling capacity of the ATES system is 15 GWh and 13.5 GWh respectively [3].

One of the regulations of ATES systems in the Netherlands states that it is mandatory that no excess

heat is injected into the subsurface during the summer compared to the heat being extracted from the

subsurface during the winter, in order to protect the subsurface and the environment [4]. Therefore,

the estimation of heating and cooling demand of the buildings is crucial to achieve better operating

performance of the ATES system. At present, two cooling towers are used to balance the ATES system

[5]. The goal of the TU/e is to reduce operational hours of the cooling towers.

Introduction of the Atlas building One of the largest projects to meet the Campus 2020 plan is the renovation of the Atlas building (the

design phase: 2014-2016; the construction phase: 2016-2018), which is also connected to the ATES

system. The building has been officially reopened on the 21st of March 2019.

The design of the building has been awarded the highest BREEAM score in the category for

construction projects in education: 96.01% [2]. The building is equipped with solar panels on the roofs,

which can supply 500 megawatt hours (MWh) per year to cover most of its power requirements.

Natural ventilation is introduced to the Atlas building due to the all-glass façade design, and occupants

can freely adjust the windows. In addition, night flushing will be introduced during summer nights.

Moreover, the glazing of the windows was chosen to be sun-resistant (low g-value) and low U-value

with interior blinds, which could provide thermal and visual comfort for users. The building is also

equipped with smart LED lighting, and the level of lighting could be adjusted by users through an App

[6].

The main building is situated on TU/e main campus (51°26'52.7"N 5°29'10.0"E). Two long sides face to

the east and the west, as shown in Figure 1.1. It has 13 stories with a total useful area of 35,644 m2 [7]

and is composed of different types of zones with single/double floor, such as common sitting spaces,

classrooms, offices, meeting rooms and laboratories. Detailed floor layouts with furnishings are

provided in Appendix A.

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Figure 1.1 A captured map of TU/e campus [8]

Previous research related to the Atlas building The Atlas building is expected to have higher heating demand than cooling demand. Therefore, the

building shows potential to improve the thermal balance of the ATES system. Currently, several

researchers are working on the operational (control) strategies of the building.

One of the recent researches focused on the annual heating and cooling load of the Atlas building by

investigating different thermal control strategies [7]. The results showed that there is an excess heating

demand of the building to contribute to the thermal balance of the ATES system. Ambient cooling

through the window façade will vary the cooling load by up to 11% and ventilation rate based on actual

occupancy could be a major contributor to reducing overall heating demand by up to 9%. However,

the impact caused by uncertainties of occupant behaviour has not been investigated in previous

studies.

Problem definition and research questions Previous studies showed there will be an excess heating demand of the Atlas building, which is able to

contribute to the thermal balance of the ATES system. However, uncertainties of occupant behaviour

will also impact the building performance. The goal of this project is to investigate the defined control

strategies of the Atlas building, taking occupant behaviour into account, in order to achieve better

operational performance of the ATES system. The research questions were developed as follows:

1) Which are suitable control strategies for the Atlas building?

a) What is the impact of occupant behaviour on the heating and cooling demand of the Atlas

building?

i) How do the aspects of occupant behaviour (heating/cooling setpoints, window/blind

operation) influence the heating and cooling demand, and which of them are the most

critical for the Atlas building?

ii) What is the most appropriate thermal zone complexity (level of resolution) for this study?

iii) What is the most appropriate representation of occupant behaviour model to predict

heating and cooling demand for this study?

b) What is the level of uncertainties from occupant behaviour that is suggested for the Atlas

building?

2) How do the defined control strategies for the Atlas building contribute to the thermal balance of

the ATES system on TU/e campus?

Atlas

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2 Literature review of occupant behaviour (OB) and occupant

behaviour modelling Uncertainties (such as occupant behaviour) in building operation phase impact the actual building

performance, causing “performance gap” between design and operational phase. The impact of

occupant behaviour on building performance has become a popular topic in recent studies. Accurate

modelling and predicting occupant behaviour are challenging, due to its stochasticity, diversity, and

complexity.

Occupant behaviour is commonly studied based on the occupancy profiles, thermostat control, plug

load use, lighting control, window operation, shading control, and domestic hot water consumption

[9]–[12]. Occupancy not only contributes to internal heat gains of occupants, it is also a key factor to

other model inputs, such as appliance use and blind/window operation [9], [10]. In the research of

Ahmed, occupancy also influenced the ventilation rate of the case study building [7]. Occupancy

schedules are mainly determined by time of the day and types of buildings/zones. Thermostat control

directly influences the performance of heating/cooling systems and indoor temperature. Occupants in

non-residential spaces usually have less control over the thermostats, compared to the residential

area. Several studies indicate a ±2~3°C of individual control over the default temperature settings in

offices is becoming practice. Control of thermostats is based on time of the day, outdoor temperature,

etc. [9]. In naturally ventilated buildings, window operation is important for indoor air quality and

temperature control [9]. Time of the day, outdoor temperature and indoor CO2 concentration are

common predictors to the window operation of residential housing [13]. Several parameters of

window operation, such as opening duration and window position patterns make the modelling

become much more complicated [14]. Window shading devices help to minimize the visual discomfort

due to glare, reduce solar radiation and provide privacy for occupants [9]. Both visual and thermal

comforts influence how occupants operate the devices.

Common methods of occupant behaviour modelling are schedules, deterministic, non-probabilistic

and stochastic [11]. The overview of the size, resolution and complexity of the common methods is

listed in Table 2.1. A recent review pointed out that aspects of occupant behaviour in building

simulation tools are conventionally simplified as static schedules [15]. This simplification ignores the

dynamic interaction between the built environment and its users, which may under- or overestimate

occupant behaviour. Several mathematical methodologies for stochastic occupant behaviour

modelling have been developed, such as Markov chains [9]. However, in order to have a realistic OB

profile, sufficient monitoring data are required.

Table 2.1 Overview of the most common occupant behaviour modelling approaches according to size, resolution and complexity [11]

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International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has

established a scientific methodological framework for occupant behaviour research [9]. One of the key

findings is that stochastic models do not always perform better than simplified deterministic/schedules

models. Annex 66 pointed out that one-size-fits-all model is not feasible. Instead, it is suggested to use

a model selection approach, fit-for-purpose approach, to arrive at the most appropriate model to

achieve better efficiency and higher accuracy for different cases. In the study of Gaetani et al. (2016),

the merits of a fit-for-purpose approach were also stressed, and possible parameters that could

influence the choice of modelling technique were presented [11].

Building performance robustness to uncertainties are usually assessed by sensitivity analysis to help to

make decisions. In the study of Kotireddy et al. (2017), robustness assessment based on scenario

analysis was developed to identify robust designs. It was found out that the CO2 emission of the case

study is the most sensitive to heating setpoints [10]. In the research of Wang et al. (2016), it was found

out that for conventional office building energy consumption, occupancy has a smaller impact

compared to lighting and appliance usage, due to the weak interaction between the operation of

building systems and occupancy [16]. Belazi et al. (2018) evaluated the uncertainties of occupant

behaviour and building envelope materials [17]. The results showed that occupant behaviour has a

considerable influence on energy load in hot climates, comparing to parameters related to building

envelope materials. On the other hand, for cold climates, the influence is more significant for

parameters in terms of building envelope than parameters in terms of occupant behaviour. To shortly

summarize, the impact of occupants on different buildings in different locations is different, which will

lead to different building performance based on different performance indicators.

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3 Methodology The research followed five main steps: literature review and background studies, simulation model

preparation, simulation model development, sensitivity analysis, and performance assessment, as

shown in Figure 3.1.

Before developing models, a suitable building performance simulation (BPS) tool, TRNSYS, was

selected and studied, since it is suitable to establish scenarios of occupant behaviour (OB) and control

strategies by using the equation box and the differential controller (type 165). In addition, the building

component, type 56, has a user-friendly visual interface and available libraries of construction and

internal gains. Therefore, it could facilitate the model development. In Ahmed’s work, TRNSYS was also

chosen to investigate the thermal control strategies of the Atlas building [7].

In order to establish reasonable models, design information and data of the Atlas building were

collected from consultants and previous work, and reasonable assumptions about missing parameters

were made. Meanwhile, key performance indicators and representative complexity of the building

models were determined, and the OB scenarios and control strategies were defined. The result from

the developed benchmark model was compared to other reference values, in order to testify the

method of the model development is applicable. Then, sensitivity analysis with defined OB scenarios

and control strategies was conducted. Therefore, the robustness of the KPIs of the Atlas building and

the impact of the building on the ATES system performance could be assessed. Based on the

robustness of the KPIs of the Atlas building, suitable levels of occupant behaviour and control strategies

were suggested.

Figure 3.1 Methodology of the research

Five main items of model development were described in this chapter, which are: key performance

indicators, complexity of the building models, benchmark model development, OB scenarios, and

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control strategies. A workflow was established to develop the models for the whole building, as shown

in Figure 3.2. Firstly, the main zones in the building were classified into four main types, which are:

classrooms, meeting rooms, offices, and open sitting spaces. They have different characteristics

related to occupants. More details are provided in Appendix B. Secondly, the complexity of the 4th and

5th floor was determined and applied to the floors between the 2nd to the 11th, since they have similar

spatial planning. These floors are the main part of the building. The reason to investigate two floors

together is that there are double floor open sitting spaces and classrooms that connect two floors

together. Finally, the rest of the floors, which are the 1st floor, ground floor and the 1st basement, were

investigated. To note that, the 1st basement, ground, the 2nd and the 11th floor have different

constructions and boundary conditions to be considered. An Atlas 3-D building model is shown in

Figure 3.3 to help to understand the workflow of the model development.

Figure 3.2 The flowchart of model development

Figure 3.3 The Atlas 3-D building model

Key performance indicators Based on the developed research questions, key performance indicators (KPIs) were determined,

which are the annual heating and cooling demand, the heating/cooling ratio (H/C) and the excess

annual heating demand (H-C). The definitions are as follows:

• Annual heating demand: total heating demand of a building/thermal zone throughout a year, MWh

• Annual cooling demand: total cooling demand of a building/thermal zone throughout a year, MWh

• Heating/cooling ratio (H/C): annual heating demand /annual cooling demand, no unit

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• Excess annual heating demand (H-C): annual heating demand - annual cooling demand, MWh

The absolute values of the annual heating and cooling demand are essential to determine suitable

complexity of building models, compare variations of heating and cooling demand in different

sensitivity analyses and understand if the differences between different scenarios are significant. H/C

and H-C are the KPIs used to define the best and the worst OB scenarios and further quantify the

contribution of the Atlas building to the ATES system. In this project, the higher H/C and H-C are, the

better the thermal balance of the ATES system will be. In addition, H/C and H-C are the reference of

selections of control strategies.

Determine the complexity of building models A complex model might lead to a more realistic result. However, it requires a larger number of inputs

with uncertainties. There is a trade-off between abstraction modelling error and input uncertainty [18].

Therefore, choosing a suitable complexity to have acceptable and time/cost-efficient results is crucial

at the early stages of the project.

The flowchart of determining the complexity of models for this project is shown in Figure 3.4. The most

complex model is to build up thermal zones for each zone in the building, and the simplest model could

be viewing a whole floor as one thermal zone. A representative thermal zone of the Atlas building

should be as simple as possible, but still gives results accurate enough to extrapolate to the whole

building. In this project, the acceptable deviation between established and the most complex model

was defined to be 10%. Each floor (2nd-11th) in the Atlas building could be seen as three compartments

based on the orientation, which are: the southern side department (Dep S), the department in the

middle (Dep M), and the northern side department (Dep N), as shown in Figure 3.5.

Figure 3.4 The flowchart of determining the complexity of building models

Figure 3.5 Three compartments in a floor (the 4th floor)

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The southern side of the double floor department on the 4th and 5th floor (Dep 4.S/ Dep 5.S) with initial

setup was taken as the test case (Figure 3.6). After investigation, the complexity was determined to be

2 single floor thermal zones for 1 double floor department. Northern side of the double floor

department follows the same complexity since it has similar spatial planning. The details of the

investigation are provided in the Appendix D.1.

Figure 3.6 The layout of the southern double storied department on the 4th floor (left) and 5th floor (right)

For the middle department (Dep 4.M/ Dep 5.M), the complexity was determined to be (2 double floor

classrooms + 2 single floor thermal zones + 4 single floor rooms), as shown in Figure 3.7. The details of

the investigation are provided in the Appendix D.2. Note that traffic area, staircases, elevators, and

technical rooms at the two sides of middle compartments were not taken into consideration.

Figure 3.7 The layout of the middle department on the 4th floor (top) and 5th floor (bottom) with determined complexity

Benchmark model development The settings of the benchmark model (without OB from thermostat/window/blind control and without

control strategies) are described in this section. Building model is the main part of the simulation model.

The main building model was set up in the component Type 56 (TRNBuild). Parameters which collected

from design documents/consultants were input into different elements of the model, which are

19

described in 3.3.1 to 3.3.7. The settings of ventilation rate and occupancy schedules mentioned in this

section are for the thermal zones with defined complexity. A suitable weather profile was chosen for

dynamic simulation.

3.3.1 Construction types

Surface construction of the façade, the floor, the ceiling, the roof, and the partition wall were defined

based on the provided design documents [19]–[22]. The façade above ground is mostly glazing, with

the low U-value around 0.7 W/m2K and the low g-value around 0.25. The dimension of one glazing

module is 1.16 m*2.57 m. Window ID 3500 with similar characteristic was chosen from the TRNBuild

library. The CCISHADE was set to 0.45. Automatic control of the blinds was applied as default. The

shading device closes when the total solar radiation on the window is over 250 W/m2, and it opens

when the total radiation is lower than 200 W/m2. When the shading device is closed, the total U-value

(shading + glazing) is 0.4 W/m2K. The U-value of the frame is 0.8 W/m2K, with the window-frame

fraction of 0.16.

Between the 1st and the 11th floor, there are two different types of floors. The main floor is mainly

composed of concrete and the intermediate floor is mainly composed of metal. Half of the main floor

was set as the floor of the department, and half of the intermediate floor was set as the ceiling of the

department in order not to double consider the layers1. The U-values of the floor and the ceiling of the

department are 0.805 W/m2K and 0.514 W/m2K respectively. As for the rest of the floors and the floor

of the Dep 2.N and Dep 2.S, the composition is different. The roof has the U-value of 0.162 W/m2K2

with the adjusted dimension of layers, due to limitations of TRNSYS.

The partition wall with thickness 0.1 m and total U-value 1.74 W/m2K was applied to all inner walls in

the building. For the external wall underground, it is mainly composed of concrete and the U-value is

0.215 W/m2K.

The convective heat transfer coefficients of internal and external surfaces are set to 11 kJ/hm2K and

64 kJ/hm2K respectively, which are the standard values suggested by TRNSYS. More construction

details are provided in Appendix E.

3.3.2 Infiltration

From the suggestion of consultants, the infiltration rate was taken as 0.72 m3/hm2 (façade area) when

all windows are closed. It is in line with the value of air leakage in 2017 ASHRAE Handbook-

Fundamentals CH16 [23]. By taking the average ACH of the Dep 4.S and Dep 5.S, the ACH value was set

to 0.13, which was applied to the whole building.

3.3.3 Representative occupancy schedules

As mentioned in 3.2, different types of zones have different occupancy schedules. Working hours of

staffs are from 8:00 to 17:30 during weekdays. For the defined complexity, a representative occupancy

schedule modified from the open space schedule was applied, which is shown in Figure 3.8. The

occupancy is 70% during the working hours, and the occupancy is 10% during the 1-hour lunch break

1 The impact of internal constructions does not have a big impact on results, since the identical boundary conditions were set.

2 The converted R-value is around 6.17 m2K/W, which is close to the R-value in the design document, 6 m2K/W.

20

(12:00 to 13:00). After 17:30, there are usually some students still present. The occupancy was

assumed to be 10% till 19:30. During the weekend, the occupancy is always considered zero.

Figure 3.8 The representative occupancy schedule

3.3.4 Heating, ventilation and air conditioning (HVAC)

Table 3.1 lists the default setting of the HVAC system. During working hours, the indoor temperature

was set between 21°C and 24.5°C based on ISSO-74 Class B [24]. Outside of working hours, the setback

temperature should not be lower than 17°C or higher than 27°C. Radiant ceiling panels were installed.

The radiative fraction was assumed to be 0.7 according to several sources [25]–[27]. The power of

heating and cooling were set as unlimited in this project, in order to evaluate the actual energy

demands, considering thermal comfort is always met.

The ventilation rate is occupied-based, which was set as 4.5 m3/hm2 (full capacity), based on the

requirement of air change (45 m³/h per occupant suggested by the consultant) and the maximum

occupant density (0.1 occupant/m2 on average)3. Based on the occupancy schedule in Figure 3.8, the

schedule of the ventilation is shown in Figure 3.9. The base ventilation rate was set at 40% of the full

capacity, and during working hours the ventilation rate was assumed as 70% of the full capacity.

Figure 3.9 The ventilation schedule

A heat recovery system is present in the mechanical ventilation system. The extracted air is taken

through a heat exchanger and the heat will transfer to the supply air. Therefore, the heating demand

3 For the thermal zones that did not follow the defined complexity (double floor classrooms, single floor rooms at the two sides of the middle department and several rooms in the underground), their ventilation rate was set as: 45*(number of occupants in the zone) m3/h.

21

could be significantly decreased. It was assumed the supply air bypasses the heat exchanger when the

outdoor ambient temperature is higher than 10℃ [28]. Hence, the cooling demand during summer will

not increase too much. The temperature of supply air (Tin) is:

𝑇𝑖𝑛 = (𝑇𝑧𝑜𝑛𝑒 − 𝑇𝑎𝑚𝑏) ∙ 𝑛 + 𝑇𝑎𝑚𝑏 (℃)

where

Tzone: the thermal zone temperature, ℃

Tamb: the ambient (outdoor) temperature, ℃

n: efficiency of the heat recovery, which is 0.7 based on the design document [29]

Table 3.1 Settings of the HVAC

Temp. lower limit Temp. upper limit Ventilation

Operational hours (8:00-17:30, Mon -

Fri) 21°C 24.5°C Occupancy-based

Setback setting 17°C 27°C 40% of the full capacity

3.3.5 Internal heat gains

The internal gains come from three sources: occupants, equipment and LED lighting. The assumptions

were made as shown in Table 3.2. According to ASHRAE Standard 55-2017 [30], the heat gain from

occupants when they perform moderately active office work at room temperature 24°C is around 130

W, which includes 75 W sensible heat and 55 W latent heat. The heat gain from the equipment was

assumed as 10 W/m2 [31]. In TRNBuild library, the internal heat gains from occupants and equipment

could be found. For the heat gain from LED lighting, the average value of 7 W/m2 was applied to the

model [31]. The radiative and convective fraction was assumed to be 18% and 82%, respectively,

following the assumption from the previous work [7].

Table 3.2 Assumptions of three types of internal gains with the corresponding data from TRNBuild library

3.3.6 Internal mass

The height of the thermal zone was set to 2.7 m (single floor) according to the design document. The

internal mass multiplier between 3 to 6 was recommended for light office rooms [32]. An investigation

showed that there is a small variation by applying internal thermal mass of 1.2 and 6. Therefore, the

capacitance was set to 3*volume in order to take light furnishings and other office equipment into

consideration.

3.3.7 Zone setup

Based on the floor area, orientation, the floor fraction and the average number of occupants, the Atlas

building is mainly composed of 11 different thermal zones, as shown in Figure 3.10. The reference floor

area, numbers of occupants and the floor fraction of each representative thermal zone are listed in

Occupants 130 W/occupant (ASHRAE_130W-Person_AIV_24 ℃)

Equipment 10 W/m2 in average, occupancy-based (SIA_2024_Dev_32office)

LED Lighting 7 W/m2 in average, occupancy-based

22

Table 3.3. The fraction of the reference floor area (FF) for heat gains of equipment and lighting was set

by the following equation, in order not to double consider these two types of internal gains in the

double floor area:

𝐹𝐹 =Atot,od

Adep∙ 0.5 + (1 −

Atot,od

Adep) ∙ 1

where

A tot,od: total area of the double floor open sitting space, m2

Adep: total area of a department, m2

11 S 11 M 11 N

10 S 10 M 10 N

9 S 9 M 9 N

8 S 8 M 8 N

7 S 7 M 7 N

6 S 6 M 6 N

5 S 5 M 5 N

4 S 4 M 4 N

3 S 3 M 3 N

2 S 2 M 2 N

1

Ground -1

Figure 3.10 Thermal zone classification of the Atlas building. Same shading colours has similar characteristics

Table 3.3 Overview of 11 representative thermal zones

Representative thermal

zone Reference floor

area (m2) Number of

similar zones Floor

fraction

Average number of occupants

1 OpenSpace 4.S 800 4 0.7 83.5

2 OpenSpace 4.N 800 5 0.7 83.5

3 OpenSpace 4.M 868.6 9 0.87 87

4 OpenSpace 10.S 800 4 0.84 87.5

5 OpenSpace 11.S 800 1 0.84 87.5

6 OpenSpace 8.N 800 3 0.84 87.5

7 OpenSpace 11.N 800 1 0.84 87.5

8 OpenSpace 2.S 800 1 0.84 87.5

9 OpenSpace 2.N 800 1 0.7 83.5

10 OpenSpace 11.M 1212 1 0.75 121

11 OpenSpace Ground 3988 2 0.84 399

To note that, double floor classrooms, single floor rooms at the two sides of the middle department

and several rooms in the underground were modelled separately and their predicted annual heating

23

and cooling demand were added together with theses 11 types of thermal zones. A zone dialogue box

of a representative thermal zone (Dep. 4S) is shown in Appendix F. Surfaces that are not exposed to

the outdoor environment were set as identical boundary conditions.

3.3.8 Weather profiles

The existing IWEC weather profile from Beek provided by EnergyPlus was applied, which is around 65

km away from the city of Eindhoven. The IWEC data files are suitable for building energy simulation,

which were derived from the year 1982-1999 of hourly weather data originally archived at the U.S.

National Climatic Data Center [33].

3.3.9 Other assumptions/settings

The simulation time step was set as 1 hour, which could already give sufficiently accurate simulation

results. The stack effect was neglected in this project.

Stack effect is often discussed in high-rise buildings. The definition of tall buildings from ASHRAE

Technical Committee 9.12 is buildings higher than 300 feet (around 91.4 m). Since the Atlas building is

around 50 m, the stack effect is much smaller. In addition, the building is relatively long (compared to

its height) in north-south direction, which is around 170 m with almost all-glass façade. The cross

ventilation is dominant in the building when windows are opened.

Define scenarios of occupant behaviour In this project, three aspects of OB during occupied hours were investigated, which are thermostat

adjustment (heating and cooling setpoints), window operation and internal shading (blind) operation.

Two cases were defined for each aspect, as shown in Table 3.4. 16 combined OB scenarios were

defined to assess the impact of OB. The best and the worst scenarios were further determined after

sensitivity analysis, which will be discussed in 4.2. Note that a representative occupancy schedule was

applied in this project as mentioned in 3.3.3.

Table 3.4 The defined OB scenarios

Aspect Reference Case A Case B

Thermostat adjustment

Heating: 21°C Heating: -1°C Heating: +1°C

Cooling: 24.5°C Cooling: -1°C Cooling: +1°C

Window operation

Always closed Always closed Tzone –Tamb >2°C, open (when Tzone > Tcool,sp )

Blind operation (reset at midnight)

Automatic control

Always open Radiation >250 W/m2,

closed and never open again

• Thermostat adjustment

Thermostats were installed in each zone for occupants to increase or decrease the indoor temperature.

From a study of Gunay [34], each time occupants changed the temperature setpoint by 1°C on average.

Therefore, the cases were set as occupants adjust the thermostats for ±1°C.

• Window operation

Occupants also have the freedom to open the windows. However, passive occupants do not operate

the windows, so the windows are always closed (Case A). On the other hand, some occupants operate

24

the windows more actively when the indoor temperature is higher (Case B). The condition from a study

of Gaetani et al. was applied [12]. Occupants open the windows when the zone temperature (Tzone) is

higher than the cooling setpoint (Tcool,sp) and the room temperature is 2°C higher than the ambient

temperature (Tamb). Detailed commands and setup of window operation in TRNSYS are provided in

Appendix H.

The infiltration from window opening is affected by several aspects, such as the overall airtightness of

a building, internal/ external temperature and wind speed [35]. The infiltration rate was set as ACH=2

when windows are open, which is a relatively conservative number compared to the calculated

number only considering wind (ACH=4). An investigation showed the variation of cooling demand with

ACH 2 and 4 is small. Calculation of infiltration of open windows is in Appendix F.

• Blind operation

As for blind operation, the automatic shading device might be overridden by occupants. Some

occupants will always open the blind as long as they arrive at the room (Case A). Some occupants drop

down the blinds when it is too bright for them (assumed to be radiation higher than 250 W/m2), and

they do not open the blinds again (Case B). The shading device always resets to its automatic control

at midnight. To note that, there are no blinds in the northern side of the building. Detailed commands

and setup of blind operation in TRNSYS are provided in Appendix H.

Define control strategies Four strategies for the Atlas building were defined to assist the thermal balance of the ATES system,

which are: increasing heating setpoints, increasing cooling setpoints, natural day cooling and night

flushing. The description is as follows and a summary is listed in Table 3.5. Detailed commands and

setup of control strategies in TRNSYS are provided in Appendix I.

Table 3.5 Summary of four control strategies

Strategy Description

Baseline (without control strategies) Default setting (as described in section 3.3)

Increase heating setpoints 21°C → 22°C

Increase cooling setpoints 24.5°C → max [min(18.8+0.33∙RMOT+2, 26), 24]°C

Natural day cooling (8:00-23:00)

a. 8:00-17:30

Tzone –Tamb >2°C (when Tzone> Tcool,sp-0.5)

b. 17:30-23:00 Tzone-Tamb> 2°C (when Tzone> 24°C)

Night flushing (23:00-7:00) Tzone-Tamb > 3°C (when Tzone> 21°C)

• Increase heating setpoints

If the default heating setpoint is increased by 1°C, the additional heating demand could help with the

thermal balance of the ATES system.

• Increase cooling setpoints

25

There is flexibility to increase the default cooling setpoint to the upper limit of thermal comfort. Based

on ISSO-74 Class B requirements (Figure 3.11) [24], the limit throughout a year was set as:

max [min(18.8 + 0.33 ∙ RMOT + 2, 26), 24] (°C)

RMOT is the running mean outdoor temperature, which considers thermal comfort as time-dependent

(adaptive comfort). The equation of RMOT is as follows:

𝑅𝑀𝑂𝑇 = 0.253 ∙ {𝜃𝑒𝑑−1 + 0.8 ∙ 𝜃𝑒𝑑−2 + 0.82 ∙ 𝜃𝑒𝑑−3 + 0.83 ∙ 𝜃𝑒𝑑−4 + 0.84 ∙

𝜃𝑒𝑑−5 + 0.85 ∙ 𝜃𝑒𝑑−6 + 0.86 ∙ 𝜃𝑒𝑑−7} (°C)

where

𝜃𝑒𝑑−1: the average outdoor temperature from yesterday, °C

𝜃𝑒𝑑−2: the average outdoor temperature from the day before yesterday, °C

etc.

Note that the average temperature of the day is the average of the maximum temperature and

minimum temperature of the day.

Figure 3.11 Class B requirements for the indoor operation temperature (with an expected percentage of dissatisfied people of a maximum 10%), in relation to the running mean outdoor temperature. A setpoint advice has been given by the dotted blue line in accordance with the recommendations in NEN-EN-ISO 7730. This figure

was adapted from Fig 3.2 of ISSO-74 [24].

• Natural day cooling

Windows could be controlled automatically in order to introduce natural day cooling under certain

conditions, where occupants will not have freedom to operate the windows. From 8 AM to 5:30 PM,

the window was set to be opened when Tzone> Tcool,sp-0.5 and Tzone –Tamb >2°C. From 5:30 PM to 11 PM,

there might still be a few students and employees in the building. Therefore, the window is open when

Tzone> 24°C (which is Tcool,sp-0.5 during working hours) and Tzone-Tamb> 2°C. The infiltration was set to be

ACH=2 during natural day cooling.

𝛉𝐨

,𝐢-

op

era

tive t

em

pe

ratu

re in

do

ors

[℃

]

Class B (max. 10% dissatisfaction)

β-upper limit: to be used if there is active cooling and/or there is no provision for effectively usable windows that can be opened

α-upper limit: to be used if there is no active cooling and there are sufficient-effectively usable windows that can be opened

basic heating setpoint during heating season

basic cooling setpoint during cooling season (only relevant with active cooling)

𝛉𝐫𝐦- running mean outdoor temperature [℃]

26

• Night flushing

On summer nights (might also happen in the late spring or early autumn), the windows are opened by

the central control system in order to introduce natural ventilation [6]. The condition of night flushing

operation was modified from Ahmed’s setting [7]. Night flushing is triggered between 11 PM to 7 AM,

when Tzone> 21°C and Tzone-Tamb > 3°C. Since the heating setpoint is 21°C during working hours, the

lowest Tamb to open the window should be no lower than 21°C to avoid overcooling. The condition of

Tzone-Tamb > 3°C can significantly decrease the cooling demand. The infiltration was set to be ACH=2

during night flushing.

27

4 Results and discussion Several aspects of sensitivity analyses were compared. In section 4.1, the annual heating and cooling

demand of the Atlas building (benchmark model) was compared to other reference values. Section 4.2

shows the sensitivity analysis of occupant behaviour, taking Dep 4.S for demonstration. In section 4.3,

the result of the sensitivity analysis of different control strategies with the best/worst OB scenarios

was discussed.

Annual heating and cooling demand of the Atlas building

4.1.1 Benchmark model

Annual heating and cooling demand of the building is shown in Figure 4.1, which is around 900 MWh,

with the annual heating demand of 683 MWh and the annual cooling demand of 215 MWh. The floors

2-11 (with the same spatial planning) account for 75% of the total heating demand and 89% of the

total cooling demand. H/C is around 3.2 and H-C is around 468 MWh. Detailed heating and cooling

demand of each thermal zone are provided in Appendix J.

Figure 4.1 Annual heating and cooling demand of the Atlas building (Benchmark model)

Annual heating and cooling demand of departments with different orientations on the floors 2-11 are

compared, as shown in Figure 4.2. Dep M accounts for the largest percentage of the annual heating

and cooling demand, which is 45% and 39% respectively. It is due to the fact that Dep M has the largest

floor area compared to the other two. Dep S and Dep N have similar energy demand since they have

the same floor area, but Dep S requires lower heating demand and higher cooling demand, because it

receives more sunlight throughout the day.

In terms of demand density, as shown in Figure 4.3, Dep M has the highest heating demand (20.5

kWh/m2a) and the lowest cooling demand (6.5 kWh/m2a), which results in the largest H/C, 3.2. It is

due to the double floor classrooms in the Dep M has higher occupant density (0.4 occupant/m2) which

leads to higher ventilation rate. Dep S has the lowest H/C, which is 2.2.

Heating Cooling

Floor -1 63 3

Floor 0/1 105 21

Floor 2-11 514 191

0

100

200

300

400

500

600

700

800

MW

h

28

Figure 4.2 Annual heating and cooling demand of on the floors 2-11: departments with different orientation

Figure 4.3 Annual heating and cooling demand density: departments with different orientation

4.1.2 Comparison to reference values

Since the Atlas building has only been operated for a few months, the result from the developed model

cannot be compared to actual measured data. Therefore, a typical model validation could not be

performed in this research. In this section, the heating and cooling demand of the benchmark model

was compared to the results from an EPC calculation and the energy demand of a Dutch office building

with similar heating and cooling systems.

In this project, a dynamic simulation approach has been applied. Annual heating and cooling demand

density of the benchmark model is 18.5 kWh/m2a and 5.8 kWh/m2a. The predicted heating density is

around 35% lower and the cooling density is around 14% higher, compared to the EPC calculation of

29

the Atlas building [29]. A comparison of energy performance prediction between the two approaches

is shown in Table 4.1.

Table 4.1 Energy density for heating and cooling of the building between different calculation methods

Calculation method Annual heating density (kWh/m2a)

Annual cooling density (kWh/m2a)

Dynamic simulation 18.5 5.8

EPC calculation 28.4 5.1

The heating and cooling demand of the Atlas building are also compared with the demand of a

sustainable office building, Netherlands Institute of Ecology (NIOO-KNAW). NIOO-KNAW is connected

to a heat pump and an ATES system, which is similar to the system of the Atlas building. It has a primary

energy demand of 45.3 kWh/m2a with 18% of heating demand and 7% of cooling demand, which is far

below the primary energy demand of the average Dutch building, 250 kWh/m2a. [36]

Taking COP=10 for cooling systems and COP=4 for heating systems4 [36], together with PEF of 2.6 [37],

the on-site heating and cooling demand of NIOO-KNAW were calculated and compared with the Atlas

building, which is shown in Table 4.2. Detailed calculation is provided in Appendix K. The predicted on-

site heating and cooling demand of the Atlas building are 24.3 kWh/m2a in total, which is close to the

total on-site heating and cooling demand of the NIOO-KNAW. Note that technical rooms, satellite

equipment rooms (SER), and ICT rooms were not considered in this project. Therefore, the predicted

on-site cooling demand of the Atlas building is underestimated. The actual on-site energy demand for

cooling is expected to be higher than 5.8 kWh/m2a.

Table 4.2 Comparison of on-site energy demand of different buildings

Buildings On-site energy demand,

heating (kWh/m2a) On-site energy demand,

cooling (kWh/m2a) On-site heating+cooling

demand (kWh/m2a)

Atlas building 18.5 5.85 24.3

NIOO-KNAW 12.5 12.2 24.7

Sensitivity analysis of occupant behaviour The impact of the 16 defined scenarios of occupant behaviour was investigated for the Dep 4.S. S0 is

the reference scenario without any occupant behaviour from thermostat/window/blind control, and

it is the basis to compare the heating and cooling variation with other scenarios. The annual heating

and cooling demand of the Dep 4.S under the reference scenario are 12.5 MWh and 5.7 MWh

respectively.

Based on the heating/cooling ratio (H/C) and the excess annual heating demand (H-C), which are

shown in Table 4.3, the best and the worst OB scenario were defined. The worst OB scenario is S1,

which is the scenario when occupants decrease both heating and cooling temperature setpoint by 1℃,

do not operate the window, and keep the blinds always open during occupied hours. The best OB

scenario is S16, which is the scenario when occupants increase both heating and cooling temperature

4 Heating and cooling are only supplied by an ATES system coupled with a heat pump 5 Without cooling demand from technical rooms, SER and ICT rooms

30

setpoint by 1℃, actively operate the windows, and always drop down the blinds once the radiation is

above 250 W/m2 (and keep it closed thereafter). The two extreme OB scenarios are used to quantify

the robustness of the building performance to uncertainties coming from OB. Detailed heating and

cooling demand of Dep 4.S are provided in Appendix L.

Table 4.3 H/C and H-C of the 16 defined OB scenarios (Dep 4.S, without control strategies)

The result shows that adjusting thermostats for ±1℃ has significant impact on the heating and cooling

demand. From the result (Figure 4.4 and Figure 4.5), it appears that the annual cooling demand varies

around 52%~68% (which is roughly 3 MWh~4 MWh) and the annual heating demand varies around

36% (which is 4.5 MWh). If occupants override the automatic shading device, the annual cooling

demand can vary up to 30%~42% (which is roughly 1.7 MWh~2.4 MWh) and the annual heating

demand variation is around 4% (which is 0.5 MWh), as shown in Figure 4.6. On the other hand, window

operation has a smaller impact on the cooling demand, which is around 1~12% annually (which is

roughly 0 MWh~0.7 MWh), as shown in Figure 4.7. When blinds are closed, and the cooling setpoint is

adjusted by 1℃ higher, window operation has almost no impact on the annual cooling demand (which

is the cases S11-S15 and S12-S16). In the defined window operation scenario, windows are only opened

to prevent overheating. Therefore, the annual heating demand remains almost constant.

In summary, the cooling demand is much more sensitive to occupant behaviour from

thermostat/blind/window control. The heating and cooling demand of the building is highly sensitive

to the thermostat adjustment, while they are less sensitive to the window operation.

Scenario S0 S1 S2 S3 S4

DescriptionDefault temp

Windows closed

Automatic control

Heating -1, Cooling -1

Window always closed

Blinds always up

Heating +1, Cooling -1


Blinds always up

Heating -1, Cooling +1


Blinds always up

Heating +1, Cooling +1


Blinds always up

H/C 2.2 1.0 1.4 1.6 2.3

H-C (MWh) 6.8 -0.2 4.1 3.5 7.8

S5 S6 S7 S8Heating -1, Cooling -1

Window open (Case B)

Blinds always up



Blinds always up



Blinds always up



Blinds always up

1.0 1.5 1.6 2.4

0.5 4.8 3.7 8.1



>250 W/m2, Blind down










1.3 1.9 2.4 3.4

2.6 7.0 5.8 10.2













1.4 2.0 2.4 3.5

2.9 7.3 5.8 10.3H-C (MWh)

H/C

H/C

H-C (MWh)

Scenario

Description

Scenario

Description

H/C

H-C (MWh)

Scenario

Description

31

Figure 4.4 Variation of annual cooling demand6: adjusting the cooling setpoint (left: in %/ right: in MWh)

Figure 4.5 Variation of annual heating demand: adjusting the heating setpoint (left: in %/ right: in MWh)

Figure 4.6 Variation of annual heating and cooling demand: blind operation (left: in %/ right: in MWh)

6 The variation of cooling demand from the cooling setpoint adjustment is derived from two scenarios with different cooling setpoints and other OB conditions are the same. For example, S3-S1 represents the difference of cooling deviation between S3 and S1. The same method is applied to derive variation of the heating/cooling demand from other aspects of OB.

32

Figure 4.7 Variation of annual heating and cooling demand: window operation (left: in %/ right: in MWh)

Sensitivity analysis of control strategies with OB Annual heating and cooling demand of the whole building without control strategies was compared

with other control strategies with the worst/best case OB scenarios. The results of sensitivity analysis

are shown from Figure 4.8 to Figure 4.11. Results show that introducing control strategies to the

building increases both H/C and H-C, which means a more positive contribution to the thermal balance

of the connected ATES system. It is due to a higher heating demand (the strategy of increasing heating

setpoints) or a lower cooling demand (the strategy of increasing cooling setpoints/natural day

cooling/night flushing). If the default heating setpoint increases, the H/C of the reference scenario

increases from 3.2 to 3.7. It can reach up to 4.8, if the default cooling setpoint increases. The H-C of

the reference scenario increases from 467 MWh to 530 MWh, if the night flushing strategy is applied.

It can reach up to 596 MWh if the strategy of increasing heating setpoints is applied.

In terms of the robustness to the uncertainties coming from OB, all control strategies lead to a larger

variation of H/C. On the other hand, the robustness of H-C to OB depends on the control strategies.

The variation of H-C of the Atlas building without control strategies is 368.5 MWh. Table 4.4 shows

that the strategy of increasing heating setpoints leads to the variation of H-C of 417.8 MWh, which is

a 13% increase in the variation of H-C, while the other three control strategies have a smaller variation

of H-C.

Figure 4.8 Annual heating demand of the Atlas building

33

Figure 4.9 Annual cooling demand of the Atlas building

Figure 4.10 Heating and cooling ratio of the Atlas building

Figure 4.11 Excess heating demand of the Atlas building

34

Table 4.4 Total heating and cooling demand of the building with different control strategies and OB. H-C variation refers to the difference between (H-C)best and (H-C)worst

Control strategy OB Total annual demand Variation H/C H-C H-C variation

Unit MWh MWh % - MWh MWh %

No control strategies

Reference Heating 683

898

3.2 467

368.5 -

Cooling 215

OB worst Heating 574

924 -15.9

1.6 224 Cooling 350 62.5

OB best Heating 770

948 12.8

4.3 593 Cooling 178 -17.5

Increase heating sp.


1034 19.4

3.7 596

417.8 13.4

Cooling 219 1.5


1038 -15.9

1.9 333 Cooling 352 61.2

OB best Heating 918

1084 12.6

5.5 751 Cooling 167 -23.7

Increase cooling sp.


822 -0.3

4.8 540

338.0 -8.3

Cooling 141 -34.8


823 -15.9

2.3 322 Cooling 251 78.2

OB best Heating 768

876 12.8

7.1 660 Cooling 108 -23.1

Natural day cooling


837 0.3

4.5 533

325.4 -11.7

Cooling 152 -29.3


826 -15.3

2.4 334 Cooling 246 61.6

OB best Heating 772

885 12.8

6.8 659 Cooling 113 -25.8

Night flushing


838 0.1

4.4 530

358.7 -2.7

Cooling 154 -28.6


839 -15.9

2.2 311 Cooling 264 71.3

OB best Heating 775

880 13.3

7.4 670 Cooling 105 -31.8

35

5 Conclusions and recommendations Annual energy demand for heating and cooling of the Atlas building were predicted in this project. The

benchmark model with OB reference scenario and without control strategies results in 683 MWh of

annual heating demand and 215 MWh of annual cooling demand (which is 18.5 kWh/m2 and 5.8

kWh/m2 respectively), which leads to a heating/cooling ratio of 3.2 and an excess annual heating

demand of approximately 468 MWh. The excess annual heating demand could help the thermal

balance of the connected ATES system. A representative complexity was developed for the main part

of the building to reduce simulation work. Each floor is mainly composed of three open space thermal

zones (2nd to 11th floor).

Sixteen scenarios of combined occupant behaviour regarding thermostat adjustment, window

operation and internal blind operation were defined. The sensitivity analysis showed that the defined

OB scenarios have significant impact on the annual heating and cooling demands, and the annual

cooling demand is much more sensitive to occupant behaviour. Comparing the three aspects of OB,

the annual heating and cooling demand of the building are highly sensitive to the thermostat

adjustment, while the demands are less sensitive to the window operation. In terms of heating/cooling

ratio (H/C) and excess annual heating demand (H-C), the best and the worst OB scenarios were defined

and introduced to different control strategies.

Introducing control strategies to the building can increase both H/C and H-C. In terms of the robustness

of performance to OB, four defined control strategies all lead to a larger variation of H/C. However,

the robustness of performance to OB, in regard to H-C, depends on the control strategies.

Based on the results of sensitivity analyses, recommendations for the level of occupant behaviour and

selection of control strategies are given as follows:

• Suggestions regarding the level of occupant behaviour

Based on the results in section 4.2, allowing occupants to adjust thermostats for ±1℃ results in a

significant impact on annual heating and cooling demand (36% and 52%~68% of variation respectively).

Therefore, the level of freedom of thermostat operation should be limited within ±1℃ . If it is

necessary, the central system could take over the thermostats to limit the thermostat operation.

Two extreme cases of the blind operation (always open the blinds from 8:00 AM/ drop down blinds

once the radiation is over 250 W/m2) lead to high variation of cooling demand, which is roughly

30%~42%. Therefore, if the cooling variation is to be limited, the override by occupants needs to be

avoided, especially the case when blinds are always kept open in the summer, which would lead to

significant increase of the cooling demand.

On the other hand, window operation has a smaller impact on the cooling demand and has almost no

impact on the heating demand in the simulation model. In reality, some occupants might open the

window in the winter (when Tzone is not larger than Tcool,sp), which leads to higher excess annual heating

demand. Therefore, it is suggested to have total freedom of window operation, in terms of the thermal

balance of the ATES system.

36

• Suggestions regarding the selection of control strategies

Based on the results in section 4.3, the strategy of increasing heating setpoints leads to the largest

excess annual heating demand. However, this strategy is much more sensitive to OB and introducing

the strategy will significantly increase annual heating demand. Hence, it is not suggested to apply to

the Atlas building.

The natural day cooling strategy might be the most suitable strategy to help to increase excess annual

heating demand by decreasing annual cooling demand, since this strategy is less sensitive to OB.

However, occupants will lose the freedom of window operation. Combining two strategies of

increasing cooling setpoints and night flushing is recommended. The annual heating and cooling

demand are 682 MWh and 84 MWh respectively (the reference OB scenario). Therefore, an excess

heating demand of 589 MWh could help to balance the ATES system. A larger variation of excess

heating demand due to OB of night flushing strategy can be mitigated by increasing the default cooling

setpoint. However, increasing cooling setpoints to the upper limit of thermal comfort leads to high risk

of overheating.

37

6 Limitations of the project and future work The limitations of this project are listed as below, and suggestions are given to improve the energy

prediction of the Atlas building in future work:

• Implement actual power of the heating and cooling system

The power of the heating and cooling systems was set as unlimited in this project. The actual heating

and cooling systems have limited capacity, which needs to be implemented to investigate how control

strategies and occupant behaviour impact thermal comfort.

• Investigate the energy demand for technical rooms, SER, ICT rooms (data centre)

The data centre was not considered in this project. The cooling system of a data centre alone may

account for up to 40% of the total energy demands [38]. The actual loads of IT and UPS equipment in

the Atlas building were not given. Therefore, it is hard to make a reasonable prediction of cooling

demand for these rooms. A design document denotes that the actual cooling power of the data centre

is 179 kW [39]. Information about data centre could be further collected to supplement the cooling

demand of the building.

• Investigate climate change scenarios

In this project, the uncertainty of climate change was not assessed. Climate change leads to an

increasing number of record high temperature events, while the number of record low temperature

events has been decreasing [40]. Therefore, it is also a factor to influence the annual heating and

cooling demand of the Atlas building. Sensitivity analysis of the performance of the control strategies

in relation to climate change scenarios could be investigated.

38

7 References [1] E. Commission, “Energy Performance of Buildings,” 2019. [Online]. Available:

https://ec.europa.eu/energy/en/topics/energy-efficiency/energy-performance-of-buildings.

[2] TU/e, “Sustainability.” [Online]. Available: https://www.tue.nl/en/our-university/about-the-university/sustainability/campus-and-operational-management/energy/.

[3] M. van Aarssen, “Heat from under our feet: Geothermal energy in the Netherlands,” 2017.

[4] Dutch ATES, “DUTCH POLICY ON ATES SYSTEMS,” 2006.

[5] T. J. G. Spruijt, “Supporting the Eindhoven University of Technology to Reach Thermal Energy Balance at the Campus 2020,” 2015.

[6] TU/e, “Atlas The pre-eminent icon of a sustainable TU/e Campus,” 2018.

[7] M. Samir, B. Performance, V. Simonis, and M. Program, “Investigating Thermal Energy Control Strategies of TU / e Main Building ‘ Atlas ’ in Connection with ATES System Air Changes per Hour,” 2018.

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[9] International Energy Agency, “International Energy Agency , EBC Annex 66 Definition and Simulation of Occupant Behavior in Buildings Annex 66 Final Report,” 2018.

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[11] I. Gaetani, P. J. Hoes, and J. L. M. Hensen, “Occupant behavior in building energy simulation: Towards a fit-for-purpose modeling strategy,” Energy Build., vol. 121, pp. 188–204, 2016.

[12] I. Gaetani, P. J. Hoes, and J. L. M. Hensen, “On the sensitivity to different aspects of occupant behaviour for selecting the appropriate modelling complexity in building performance predictions,” J. Build. Perform. Simul., vol. 10, no. 5–6, pp. 601–611, 2017.

[13] D. Calì, R. K. Andersen, D. Müller, and B. W. Olesen, “Analysis of occupants’ behavior related to the use of windows in German households,” Build. Environ., vol. 103, pp. 54–69, 2016.

[14] S. D’Oca and T. Hong, “A data-mining approach to discover patterns of window opening and closing behavior in offices,” Build. Environ., vol. 82, pp. 726–739, 2014.

[15] D. Yan et al., “Occupant behavior modeling for building performance simulation: Current state and future challenges,” Energy Build., vol. 107, pp. 264–278, 2015.

[16] Q. Wang, G. Augenbroe, J. H. Kim, and L. Gu, “Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings,” Appl. Energy, vol. 174, pp. 166–180, 2016.

[17] W. Belazi, S. E. Ouldboukhitine, A. Chateauneuf, and A. Bouchair, “Uncertainty analysis of occupant behavior and building envelope materials in office building performance simulation,” J. Build. Eng., vol. 19, no. May, pp. 434–448, 2018.

[18] E. E. E. Enk, “Guidelines for selecting the ‘fit-for-purpose’ model complexity regarding building energy performance prediction,” 2016.

39

[19] Team RSVP, “03_doorsneden (11D03-03-00.01),” 2016.

[20] Team RSVP, “04 Details Hoogbouw (11D04-02-H-01),” 2016.

[21] Team RSVP, “Details Laagbouw (11D04-02-L-01),” 2016.

[22] Team RSVP, “TU/e Hoofdgebouw Ontwerp-toelichting DO PCV 100%,” 2016.

[23] ASHRAE, CHAPTER 16- VENTILATION AND INFILTRATION. 2017.

[24] A. Boerstra, J. van Hoof, and H. van Weele, ISSO Publicatie 74- Thermische behaaglijkheid. 2013.

[25] Tim Dwyer, “Module 5: Radiant heating panels,” CIBSE J., 2009.

[26] Zehnder Group UK Ltd, “9 Mythbusters - Radiant Ceiling Panels | Zehnder Group UK.” [Online]. Available: https://www.zehnder.co.uk/service/blog/9-mythbusters-radiant-ceiling-panels. [Accessed: 28-Jun-2019].

[27] S. Okamoto, H. Kitora, H. Yamaguchi, and T. Oka, “A simplified calculation method for estimating heat flux from ceiling radiant panels,” Energy Build., vol. 42, no. 1, pp. 29–33, 2010.

[28] P.-J. Hoes, “Computational performance prediction of the potential of hybrid adaptable thermal storage concepts for lightweight low-energy houses,” 2014.

[29] Valstar Simonis, “2015-11-18 Hoofdgebouw TUe v1.6 DO100 DEF plus 4300 m2 PV,” 2015.

[30] ASHRAE 55-2017 Thermal Environmental Conditions for Human Occupancy. ASHRAE, 2017.

[31] Valstar Simonis, “TUe Atlas -Verwarming en koeling.”

[32] S. H. Lee and T. Hong, “Leveraging Zone Air Temperature Data to Improve Physics-Based Energy Simulation of Existing Buildings,” 15th IBPSA Conf., pp. 528–535, 2017.

[33] EnergyPlus, “Weather Data | EnergyPlus.” [Online]. Available: https://energyplus.net/weather. [Accessed: 28-May-2019].

[34] H. B. Gunay, W. O’Brien, I. Beausoleil-Morrison, and J. Bursill, “Development and implementation of a thermostat learning algorithm,” Sci. Technol. Built Environ., vol. 24, no. 1, pp. 43–56, 2018.

[35] M. Liddament, “Air Filtration Calculation Techniques: An Application Guide,” 1999.

[36] D. Bont et al., “nZEB design in the Netherlands : an overview of recent projects nZEB design in the Netherlands: an overview of recent projects,” no. 2016, 2016.

[37] E. Molenbroek, E. Stricker, and T. Boermans, “Primary energy factors for electricity in buildings Toward a flexible electricity supply,” p. 52, 2011.

[38] M. Avgerinou, P. Bertoldi, and L. Castellazzi, “Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency,” Energies, vol. 10, no. 10, 2017.

[39] Unica building projects, “GKW, MER & SER Principeschema,” 2018.

[40] NASA, “Global Climate Change: Vital Signs of the Planet”.” [Online]. Available: https://climate.nasa.gov/evidence/.

40

[41] TU/e, “Timeslots.” [Online]. Available: https://educationguide.tue.nl/studying/planning/timeslots/. [Accessed: 23-Apr-2019].

[42] Meteoblue, “Climate (modelled) Eindhoven.” [Online]. Available: https://www.meteoblue.com/en/weather/historyclimate/climatemodelled/eindhoven_netherlands_2756253. [Accessed: 01-Jul-2019].

[43] World Weather Online, “Eindhoven, North Brabant, Netherlands Weather Averages.” [Online]. Available: https://www.worldweatheronline.com/eindhoven-weather-averages/north-brabant/nl.aspx. [Accessed: 01-Jul-2019].

41

8 Appendix

A. Detailed floor layouts with furnishings Figure 8.1 to Figure 8.13 show the floor layouts of the Atlas building. The grey/dotted line area

indicates the double storied area.

Figure 8.3 The layout of the 2nd floor

Figure 8.4 The layout of the 3rd floor

Figure 8.2 The layout of the 1st floor

Figure 8.1 The layout of the Ground floor

42

Figure 8.5 The layout of the 4th floor






43



Figure 8.13 The layouts of the -1th and the -2nd floor

44

B. Classification of zones The zones could be classified as four main types, which are: classrooms, meeting rooms, offices, and

open sitting spaces. They have different characteristics related to OB, which are shown in Table 8.1.

Since different patterns of lighting and equipment use give relatively small impact on the annual

heating and cooling demand, the average value per area which suggested by the consultant is applied

to simplify the model. Occupancy schedules of each type of zone are shown in Figure 8.14-17.

Table 8.1 Characteristics of 4 types of zone

Classroom Meeting room Open sitting

space Office

Occupancy schedules7

8:45-19:158 8:00-17:30 8:00-19:30 8:00-17:30

Occupancy (%) 50 70 60 70

Equipment use

Laptops, projectors,

screens, etc.

Laptops and screens

Laptops or desktops

Laptops, desktops,

printers, etc.

10 W/m2 on average, occupancy-based

Lighting use Usually adjusted

by lecturers Depend on the type of occupants

(active/passive) More actively

7 W/m2 on average, occupancy-based

Figure 8.14 The occupancy schedule of classrooms

Figure 8.15 The occupancy schedule of meeting

rooms

Figure 8.16 The occupancy schedule of open sitting

spaces

Figure 8.17 The occupancy schedule of offices

7 Occupancy schedules are modified from Ahmed’s work [3]. 8 The classroom schedule is based on TU/e timetables (Appendix C).

45

C. Timeslots of the courses

Figure 8.18 Timeslots of the courses in TU/e [41]

D. Determine a suitable complexity of the departments

D.1 The southern departments

The building model with highest resolution is shown in Figure 8.19. The southern department on the

4th floor was divided into 7 zones, which are 2 double storied open spaces (OW 4.1 and OE 4.1), 1 single

storied open space (OE 4.2), 3 meeting rooms (RME 4.1, RME 4.2, and RME 4.3), and 1 office (ROE 4.1).

The department on the 5th floor is mainly composed of 2 meeting rooms (RM 5.1 and RM 5.2), 9 office

rooms (ROE 5.1~ROE 5.9) and 2 double storied open spaces connecting with the open spaces from the

4th floor.

Figure 8.19 The layout (the highest resolution) of the southern double storied department on the 4th floor (left) and 5th floor (right)

O: Open sitting space / RO: Office / RM: Meeting room/ E: East / W: West

46

The building model with the simplest layout is shown in Figure 8.20. Since the area of open spaces is

much more than the area of rooms in the Atlas building (which is around 8:1 for the Dep 4.S; 3:1 for

the Dep 5.S), Dep 4.S and Dep 5.S could be seen as two open spaces, which are O4.S and O5.S.

Figure 8.20 The layout (the lowest resolution) of the southern double storied department on the 4th floor (left) and 5th floor (right)

The simulation result of the simplest and most complex models is shown in Table 8.2. The deviation of

the heating and cooling demand is less than 1% for two test cases. Therefore, the complexity of a

southern double floor department was determined as two single floor open spaces.

Table 8.2 Heating and cooling demand of two models (Dep 4.S and Dep 5.S)

The simplest model (Floor 4+5) T09 T110

Total Heating (MWh) 75.1 70.3

Total Cooling (MWh) 13.8 13.7

The complex model (Floor 4+5)

Total Heating (MWh) 75.8 71.7

Total Cooling (MWh) 13.8 13.7

Heating Deviation (%) -0.81 -0.02

Cooling Deviation (%) 0.16 0.00

D.2 The middle departments

The same investigation method as the southern departments was applied. Firstly, a complex model

was built and examined, as shown in Figure 8.2111. It composes of double storied open spaces (areas

with purple colour), single storied open spaces (areas with green colour), meeting rooms (areas with

light blue colour), and offices (areas with yellow colour). In addition, there are 2 double floor

classrooms at the western and the eastern side of the middle department (area with orange colour).

9 T0: The heating setpoint: 21℃, the cooling setpoint: max [min (18.8+0.33∙RMOT+2, 26°C), 24°C]-1℃, no shades, and windows are always closed 10 T1: The heating setpoint: 20℃, the cooling setpoint: max [min (18.8+0.33∙RMOT+2, 26°C), 24°C]-1℃, no shades, and windows are always closed 11 Zones next to traffic areas weren’t taken into consideration when determine the complexity

O: Open sitting space / S: South

47

Figure 8.21 The layout (the highest resolution) of the middle double storied department on the 4th floor (top) and 5th floor (bottom)

The model with the simplest layout is shown in Figure 8.22. However, since there are 2 double floor

classrooms in the middle department, the deviation of the simplest and the complex models is large.

Therefore, the complexity of the model was adjusted to (2 single floor open spaces + 2 double floor

classrooms), as shown in Figure 8.23, which has acceptable heating and cooling deviation. The

simulation results of three models with different complexity are shown in Table 8.3.

Figure 8.22 The layout (the lowest resolution) of the middle double storied department on the 4th floor (top) and 5th floor (bottom)

48

Figure 8.23 The layout (moderate resolution) of the middle double storied department on the 4th floor (top) and 5th floor (bottom)

Table 8.3 Heating and cooling demand of three models (Dep 4.M and Dep 5.M)

Complex model (Floor 4+5) T0

Total Heating (MWh) 175.1

Total Cooling (MWh) 14.9

Simplest model (Floor 4+5)



Heating Deviation (%) 1.8

Cooling Deviation (%) -13.9

Moderate model (Floor 4+5)



Heating Deviation (%) -2.7

Cooling Deviation (%) -6.7

49

E. Construction types

Figure 8.24 The window setting

Figure 8.25 Details of the floor

Figure 8.26 Details of the ceiling

Figure 8.27 Details of the partition wall

Figure 8.28 Details of the facade (underground)

50

Figure 8.29 Details of the roof

Figure 8.30 Details of the floor (Dep 2.S and 2.N)

Figure 8.31 Details of the floor of the ground floor

Figure 8.32 Details of the floor of the basement 1

Figure 8.33 Details of the ceiling of the basement 1

51

F. A zone dialog box of a representative thermal zone

Figure 8.34 The zone dialog box of Dep 4.S

G. Infiltration of open windows Based on 2017 ASHRAE Handbook- Fundamentals CH16 [23], airflow caused by wind could be

calculated by following equation:

Q = 𝐶𝑣𝐴𝑈

where

Q: airflow rate, m3/s

Cv: effectiveness of openings (Cv was assumed to be 0.5 to 0.6 for perpendicular winds and 0.25

to 0.35 for diagonal winds)

A: free area of inlet openings, m2

U: wind speed, m/s

Based on several sources, the annual average wind speed (U) in Eindhoven is around 4 m/s, and the

prevailing wind is from South-West [42], [43]. Therefore, Cv was taken as 0.25. Total effective area of

opening (A) for one single floor department is around 2.6 m2 12 . Applying these numbers to the

equation leads to an air flow of 2.6 m3/s. Therefore, the ACH is around 4 by applying the zone volume

of Dep 4.S (2160 m3).

12 Occupants can open the window to a net opening of 30 mm. For one glazing module, the effective opening area is (2.57*2)*0.03=0.154 m2. At Dep 4.S and 5.S, the total number of glazing that could be opened is 34.

52

H. Commands and setup of occupant behaviour I.1 The reference scenario

The layout of the reference scenario is shown in Figure 8.35. The main components are: the building

model, the weather data and an equation box of the heat recovery for the input.

Figure 8.35 The model layout: the reference scenario

I.2 Window operation: windows open if Tzone –Tamb >2°C and Tzone > Tcool,sp

The layout of window operation is shown in Figure 8.36. The command of window operation is as

follows:

Window_open = Switch1*Switch2*OccupiedHour

Two Type 165 controllers were used to set the two conditions of window operation, which are Switch1

and Switch2 respectively. Occupied hours are 8:00-19:30 for open spaces.

53

Figure 8.36 The model layout: Window operation

I.3 Blind operation

Case A: blinds always open from 8 AM

The layout of the case A is shown in Figure 8.37. Blind operation was classified into two groups:

Weekday and Weekend. It was assumed that no occupants in the building during the weekends, so the

shading device will always be automatic control. During weekdays, the blinds are opened by occupants

from 8 AM and the device resets to automatic control at midnight, which means there is no blind signal

(0) during the period. Therefore, an open schedule was imported. Type 165 controllers were used to

determine whether to open the blinds or not, according to the conditions of automatic control. The

upper inputs (Th) in the Type 165 controllers were imported from the equation box. Taking

SwitchE_Weekday as an example (Figure 8.38):

Weekday = LT(MOD(Day-1,7),5)

Th= Radiation_E_Weekday = Radiation_E13*Weekday

The commends of SwitchE_Weekend:

Weekend = GE(MOD(Day-1,7),5)

Th=Radiation_E_Weekend = Radiation_E*Weekend

The commands of the overall blind operation (BlindE) were imported into the building model.

BlindE = Day_E*OpenSchedule+End_E (Day_E= SwitchE_Weekday; End_E= SwitchE_Weekend)

13 Radiation_E was imported from the weather profile.

54

Figure 8.37 The model layout: Blind operation (Case A)

Figure 8.38 Commands of SwitchE_Weekday: Blind operation during weekday, eastern side

OpenSchedule

55

Case B: blind closed once radiation >250 W/m2 and never open again

The layout of the case B is shown in Figure 8.39. Blind operation was also classified into two situations:

Weekday and Weekend. On the weekends, shading device is always automatic control. The settings of

all the controllers in the weekday are similar to the case A, but with different lower inputs (TI) and

lower dead bands (dT). A reset schedule was imported to TI at midnight, in order to open the blinds

again. The commands of the overall blind operation were imported into the building model, which are:

BlindE = Day_E+End_E

BlindW = Day_W+End_W

BlindS = Day_S+End_S

Figure 8.39 The model layout: Blind operation (Case B)

56

I. Commands and setup of control strategies J.1 Increasing cooling setpoints

The model layout is shown in Figure 8.40. t1~t7 were imported from the component of type 9e, which

contains the average outdoor temperature of the past 7 days. The working hours are 8:00-17:30 during

weekdays. The setback temperature is 27℃. The cooling setpoint (T_cooling) was then imported into

the building models. The commands are as follows:

A = 0.8

RMOT = 0.253*(t_1+A*t_2+A*A*t_3+A*A*A*t_4+A*A*A*A*t_5+A*A*A*A*A+t_6+A*A*A*A*A*A*t_7)

T_Cooling = (MAX(MIN(18.8+0.33*RMOT+2,26),24))*EQL(WorkingHr,1)+27*EQL(WorkingHr,0)

Figure 8.40 The model layout: Strategy of increasing cooling setpoints

J.2 Natural day cooling

The layout is shown in Figure 8.41. The commands of window operation are as follows:

Window_open_1 = Switch1*Switch2*Operation1

Window_open_2 = Switch2*Switch3*Operation2

Window_open = Window_open_1+Window_open_2

Type 165 controllers were used to set the three conditions of window operation, which are:

Switch1: Tzone> Tcool,sp-0.5

57

Switch2: Tzone –Tamb >2°C

Switch3: Tzone> 24°C

Two schedules were set:

Operation1: 8:00-17:30

Operation2: 17:30-23:00

Figure 8.41 The model layout: Natural day cooling

J.3 Night flushing

The layout of night flushing is shown in Figure 8.42. The command of window operation is as follows:

Window_open = Switch1*Switch2*Schedule_NightFlush

Type 165 controllers were used to set the two conditions of window operation:

Switch1: Tzone> 21°C

Switch2: Tzone-Tamb > 3°C

The operational hours of night flushing are between 23:00 and 7:00.

58

Figure 8.42 The model layout: Night flushing

59

J. Detailed annual heating and cooling demand of the Atlas building

(Benchmark)

Table 8.4 Detailed demand of each thermal zone

Floor Type of thermal zone Area (m2) Heating (MWh) Cooling (MWh)

2~11

OpenSpace 4.S*4 3200 50.1 22.9

OpenSpace 4.N*5 4000 69.4 27.4

OpenSpace 4.M*9 7817 103.3 48.9

OpenSpace 10.S*4 3200 43.1 27.1

OpenSpace 11.S 800 18.1 5.2

OpenSpace 8.N*3 2400 36.1 19.4

OpenSpace 11.N 800 19.6 5.0

OpenSpace 2.S 800 20.3 5.6

OpenSpace 2.N 800 23.9 4.7

OpenSpace 11.M 1212 30.3 5.1

Other single rooms 1361 43.1 10.8

Double floor classroom 1013 56.5 9.0

Total (MWh) 513.9 191.1

% 75.3 88.7

0/1

OpenSpace*2 7976 80.5 17.4

Double floor classroom 448 25.0 4.0

Total (MWh) 105.5 21.4

% 15.4 9.9

-1 Total (MWh) 1068 63.4 3.0

% 9.3 1.4

Whole building MWh 682.9 215.5

Investigating area m2 36894

Density kWh/m2a 18.5 5.8

Table 8.5 Detailed demand of departments with different orientation

Area (m2)

Heating (MWh)

Cooling (MWh)

Heating (kWh/m2a)

Cooling (kWh/m2a)

H/C H-C

(MWh)

Dep S 8000 131.7 60.8 16.5 7.6 2.2 70.9

Dep N 8000 149.0 56.5 18.6 7.1 2.6 92.5

Dep M 11403 233.2 73.8 20.5 6.5 3.2 159.5

60

K. Calculation of on-site energy demand The on-site heating demand of NIOO-KNAW is:

(PE)𝑁𝐼𝑂𝑂−𝐾𝑁𝐴𝑊 ∙ 𝑃ℎ𝑒𝑎𝑡𝑖𝑛𝑔 ∙𝐶𝑂𝑃ℎ𝑒𝑎𝑡𝑖𝑛𝑔

𝑃𝐸𝐹= 12.5

The on-site cooling demand of NIOO-KNAW is:

(PE)𝑁𝐼𝑂𝑂−𝐾𝑁𝐴𝑊 ∙ 𝑃𝑐𝑜𝑜𝑙𝑖𝑛𝑔 ∙𝐶𝑂𝑃𝑐𝑜𝑜𝑙𝑖𝑛𝑔

𝑃𝐸𝐹= 12.2

where

(PE)NIOO-KNAW: primary energy demand of NIOO-KNAW, 45.3 kWh/m2a

Pheating: percentage of the heating demand (NIOO-KNAW), 18%

Pcooling: percentage of the cooling demand (NIOO-KNAW), 7%

COPheating: the coefficient of performance (heating), 4

COPcooling: the coefficient of performance (cooling), 10

PEF: the primary energy factor, 2.6

61

L. Detailed annual heating and cooling demand of the Dep. 4S Heating and cooling deviations are compared with S0.

Table 8.6 Annual heating and cooling demand of 16 OB scenarios (Dep 4.S, without control strategies)

Scenario DescriptionHeating

(MWh)

Cooling

(MWh)H/C ratio

H-C

(MWh)

Heating deviation

(%)

Cooling

deviation

(%)

Heating

density

(kWh/m2)

Cooling

density

(kWh/m2)

S0

Default temp

Windows closed

Automatic control

12.5 5.7 2.2 6.8 15.7 7.2

S1



Blinds always up

9.6 9.9 1.0 -0.2 -23.1 72.6 12.0 12.3

S2



Blinds always up

14.1 10.0 1.4 4.1 12.9 75.7 17.7 12.6

S3



Blinds always up

9.6 6.1 1.6 3.5 -23.3 7.4 12.0 7.7

S4



Blinds always up

14.0 6.2 2.3 7.8 11.8 7.8 17.5 7.7

S5



Blinds always up

9.6 9.2 1.0 0.5 -23.1 60.6 12.0 11.5

S6



Blinds always up

14.2 9.4 1.5 4.8 13.0 63.6 17.7 11.7

S7



Blinds always up

9.6 5.9 1.6 3.7 -23.3 3.2 12.0 7.4

S8



Blinds always up

14.0 5.9 2.4 8.1 11.8 3.5 17.5 7.4

S9




10.0 7.5 1.3 2.6 -19.8 31.0 12.6 9.4

S10




14.8 7.8 1.9 7.0 18.0 35.8 18.5 9.7

S11




10.0 4.3 2.4 5.8 -20.1 -25.6 12.5 5.3

S12




14.5 4.3 3.4 10.2 16.0 -24.9 18.2 5.4

S13




10.0 7.2 1.4 2.9 -19.8 25.6 12.6 9.0

S14




14.8 7.5 2.0 7.3 18.0 30.4 18.5 9.3

S15




10.0 4.2 2.4 5.8 -20.1 -27.1 12.5 5.2

S16




14.5 4.2 3.5 10.3 16.0 -26.4 18.2 5.3

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