The Integration of Occupant Comfort, Energy and Daylighting in the Non-Domestic Buildings

The Integration of Occupant comfort, Energy, Daylight in the Non-Domestic Buildings

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The Integration of Occupant Comfort,

Energy and Daylighting in the

Non-Domestic Buildings

By-Anju Pradhan

(Under the supervision of Dr. Mahroo Eftekhari and Dr. John Mardaljevic)


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Abstract

In the quest to improve performance of buildings, without compromising occupant comfort, (both thermal

and visual comfort), the utilization of passive measures in the buildings is a good alternative for energy

efficient non-domestic buildings. By deploying available passive technologies, the energy usage by the

buildings for both space heating and cooling can be reduced significantly. The aim of this research is to

reduce the operational energy use of the building by maximising use of passive measures without

compromising occupant's comfort both thermal and visual comfort. It will also evaluate the effect of

unregulated energy use on the thermal comfort of the occupants inside the building. Furthermore, it will

evaluate the existing gap between the predicted performance of the buildings during design process and

the actual performance of the building after occupied over certain period. In addition to that, it will also

evaluate the underlying correlation between daylight, energy usage and occupant comfort in the life cycle

of buildings.

The actual energy consumption, thermal comfort, daylight autonomy, useful daylight illuminance and

visual discomfort are the key performance indicators analysed for the building performance assessment.

The research will address two methods of data collection and analysis which are termed as qualitative and

quantitative. The qualitative method will use survey process employing short interviews for occupants

and the quantitative method will use a set of questionnaires and spot measurements to gather data from

different location within the space. The thermal comfort indexes Predicted Mean Vote (PMV), and

Predicted Percentage of Dissatisfied (PPD) are calculated. The numerical analysis will be performed

using dynamic simulation tools, EnergyPlus, TRNSYS, DIVA for RHINO. The simulated models will be

validated using the data gathered from the experimental measurements.

Hereby, the research will provide a guideline for the stakeholders and policy makers to improve

occupants comfort in the energy efficient building with the use of passive technologies. Furthermore, it

will suggest on the appropriate control systems, operation and management to improve the financial

implication on the performance improvement of the non- domestic buildings.

Keywords: Passive ventilation, thermal comfort, daylight, visual discomfort, building performance


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Table of Contents

Page No.

Abstract................................................................................................................ 2

Table of content.....................................................................................................3

List of figures........................................................................................................5

List of tables..........................................................................................................6

Chapter 1.0 Introduction ............................................................................................. 7

1.1 Research Aim and Objectives ........................................................................ 11

1.2 Problem Statement ........................................................................................ 12

1.3 Methodology ................................................................................................. 12

1.4 Research Outcome ........................................................................................ 28

1.5 Organisation of the report ............................................................................. 29

Chapter 2.0 Literature Review ................................................................................. 30

2.1 Background .................................................................................................. 30

2.2 Climate data for the building design ............................................................ 35

2.3 Passive Ventilation ....................................................................................... 35

2.3.1 Available passive measures and technologies....................................39

2.3.2 Environmental factors of the building..............................................44

2.3.3 Thermal comfort............................................................................45

2.4 Day Lighting ................................................................................................ 35

2.4.1 Climate effects for daylighting design ................................................. 51

2.4.2 Daylight performance indicators ........................................................ 53

2.4.2.1 Daylight Factor .................................................................................. 54


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2.4.2.2 Daylight Coefficient ................................................................... 56

2.4.2.3 Daylight Autonomy ................................................................... 57

2.4.2.3 Useful Daylight Illuminance ....................................................... 58

2.4.3 Visual discomfort ....................................................................................... 59

2.5 Building Energy Simulation Tools ............................................................... 35

2.5.1 EnergyPlus..............................................................................................63

2.5.2 ESP-r ................................................................................... .......65

2.5.3 TRNSYS ................................................................................. ....66

2.5.4 DIVA for RHINO ................................................................... .......67

2.6 Building Operation and Management .......................................................... 35

2.6.1 Control strategies and systems for building performance improvement.69

2.6.2 Post occupancy evaluation ........................ ......................................70

2.7 Summary ...................................................................................................... 35

Chapter 3.0 Progress/ works done to date ............................................................... 73

3.1 Outstanding work ............................................................................... 74

3.2 Second Year work schedule ......................................................................... 35

References .................................................................................................................. 76


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List of figures Page No.

Figure1.2.1 East-Park Loughborough University Design School Building ..............................11

Figure 1.2.2 UK degree day regions ......................................................................................... 12

Figure 1.2.3 A schematic drawing illustrating the ventilation strategy .....................................13

Figure 1.2.4 Low level air supply below feet in lecture theatre ...............................................14

Figure 1.2.5 The Hobo pendant Temperature/Alarm data logger ............................................15

Figure 1.2.6 Location of sensors on east wall .......................................................................... 15

Figure 1.2.7 Sensor location at middle of lecture theatre ........................................................ 16

Figure 1.2.8 Sensor locations on north wall ........................................................................ ....16

Figure 1.2.9 Sensor location on west wall .............................................................................. .17

Figure 1.2.10 Snapshot view of model of lecture theatre ......................................................... 19

Figure 1.2.11 Snapshot of daylight factor analysis of lecture theatre .......................................19

Figure 1.2.12 Snapshot of illuminance level at lecture theatre ............................................... 20

Figure 1.2.13 Annual glare simulation, Visual comfort without occupant adaptation ........... 20

Figure 1.2.14 Intolerable glare, daylight glare probability-58%...............................................21


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Figure2.2.1 The quarterly sector wise energy usage trend in UK from the year 2010-2013......33

Figure2.2.2 The sector wise energy consumption in 2011 .........................................................33

Figure3.1.1 The different types of vernacular wind catchers .....................................................40

Figure3.1.2 The different forms of modern/ commercial wind catchers ....................................41

Figure3.1.3 Wind catcher tower proposed design for future ......................................................41

Figure3.1.4 The structure and principal of wind catcher system ...............................................42

Figure3.1.5 The salt gradient solar pond configuration..............................................................44

Figure 6.2.1 TM22 'Energy Tree Diagram' illustrating the breakdown of energy use ..............72

List of Tables Page No

Table 1.2.1 Material assumptions in DIVA for Rhino analysis................................................ 18

Table 1.2.2 Result summary from DIVA for Rhino analysis .................................................. 21

Table 6.2.1 Overview of existing POE approaches in UK .......................................................71

Table 7.1.1 Work plan schedule .............................................................................................. 75


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Chapter 1.0 Introduction

In response to the global climate change and future energy security, the current trend in

construction industry is to build buildings using the energy efficient measures. While

implementing energy efficient measures, the occupant’s comfort should not be compromised and

the maximum environment benefits should be in utilized during the life cycle of the building. In

general, building design uses two approaches called active and passive approach. The active

approach require some form of energy inputs to operate for examples heat pumps, air handling

units etc. The passive approach are those features of the buildings which do not require

additional energy inputs, for example insulation, thermal mass, building geometry, daylighting,

airtightness and minimizing thermal bridges etc. (Simm S. et.al., 2011). To improve the

performance of the buildings, passive approach is more emphasised by the designers in the

energy efficient buildings.

However, to enhance the occupant’s comfort utilizing environment factors of the buildings is

very critical task. Since buildings by nature in itself is dynamic and it changes with time. The

building energy performance is influenced by many factors. The basic design parameters such as

building orientation, building form, construction materials, construction details, thermal mass

and daylighting etc. are the main design parameter which influence building performance.

Building form and material affects the airtightness and U- values of the building which in turn

leads to less heat loss / gain from the building to the surroundings. Similarly construction details

will reduce the thermal bridging and leads to less heat gain/loss from the buildings to the

surroundings (Simm S. et.al., 2011). Furthermore, the construction technique used on site also

has a greater influence on the performance of building since it should closely match the design


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intent. Otherwise, there will be more probability of gap on predicted and post occupancy

performance of buildings. Here, the performance of the building implies both energy and the

system performance. Besides that, occupant’s behaviour also has significant influence on the

energy consumption by the buildings. The occupant’s behaviour affects the thermal gain, control

of unregulated power devices, control of ventilation and lighting devices that might work against

the design strategies (Simm S. et. al., 2011). Also the climate variables, such as external air

temperature, solar irradiation and wind speed etc. affect the thermal performance of the

buildings.

In order to improve performance of the energy efficient buildings, in this research , the actual

annual energy consumption, thermal comfort, useful daylight illumination (UDI), daylight

autonomy (DA) and visual comfort will be studied as performance indicators (PIs) of the

building. The relationship between the input parameters and output performance indicators will

be investigated by using sensitivity analysis. The primary sensitivity analysis techniques

available to use with building thermal simulation programs are differential sensitivity analysis

(DSA), Monte Carlo analysis (MCA) and stochastic sensitivity analysis (Simm S. et. al., 2011).

Similarly, there are many methods available to predict the building energy consumption (Zhao

H. and Magoules F., 2012). Such as engineering method, statistical methods, neural networks,

support vector mechanics and grey models. Among these methods, many building simulation

tools have been developed based on engineering methods such as, DOE-2, EnergyPlus, BLAST,

ESP-r etc. The use of detailed engineering method to calculate energy consumption is more

precise, since it addresses step by step calculations of energy consumed by all components of

building with its environmental information such as, external climate condition, building


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construction and operation, utility rate schedule and HVAC equipment (Zhao H. and Magoules

F., 2012). Basically, the energy simulation tools were developed to ease designers in decision

making process for different design alternatives. In the UK, for the non-domestic buildings, the

building energy simulations are performed using two approaches, using the simplified building

energy model (SBEM) and the approved dynamic thermal modelling (EPBD-NCM, 2012).

Although there are numbers of dynamic thermal modelling tools available based on detail

engineering methods, it is difficult to use it in practice, because of its requirement for large

accurate input parameters which are not available during the conceptual design stage.

In practice, it is found that there is a difference in predicted and actual post occupancy

performance of the buildings. The reason for this difference might be due to the assumptions

during design process, construction techniques, changing climate, occupancy behaviours,

management and controls etc. The post occupancy evaluation (POE) tool is very useful to

analyse the reason behind actual building performance and predicted building performance after

occupancy. By definition post occupancy evaluation (POE) is “the process of systematic

evaluation of the building after it’s built and occupancy over a period of time” (Prieser W. F. et.

al, 1988). The systematic evaluation is performed with specific set of performance criteria. The

performance criteria for specific type of building depend on its design intent and functional use

(Prieser W. F., 2001). The post occupancy evaluation (POE) identifies the potential problems

and gives solutions to the problems from the occupant’s feedback, thereby increasing the

effectiveness of the facility management. It helps in cost savings and building process throughout

the lifecycle of the building. In the UK, the energy assessment reporting method (EARM) is one

of the recognized tools to assess the energy and system performance of the buildings. It is


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published as a technical memorandum by CIBSE under CIBSE TM22. It describes a method for

assessing the energy performance of an occupied building based on metered energy use, and

includes a software implementation of the method.

In future, with the POE tools, there will be more possibility to reduce the gap between predicted

and actual performance of the building. In the research done by Menezes et.al, 2012,

demonstrated that by using Post-Occupancy Evaluation (POE) data with the energy models for

the lighting, small power and catering equipment in the office building, the accuracy of predicted

energy model is within 3 % of actual electrical energy consumption. Further evidence based

researches are required to benchmark the energy performance of the non-domestic building using

post occupancy evaluations.

The aim of the project is to reduce operational energy usage by the buildings without

compromising occupants comfort both thermal comfort and visual comfort by maximising the

use of passive measures. The operational energy consumption by the non-domestic buildings

includes energy usage for space heating and cooling, water heating, ventilation, lighting and

appliances. In addition to that, it will also focus on the impacts of the non-regulatory energy use

by appliances, ventilation, and lighting etc. on the thermal comfort of occupants. Furthermore,

the gap between the predicted and post occupancy performance both energy and system

performance will also be evaluated. It will also evaluate the suitability of passive ventilation in

providing the thermal comfort during the design life of building in context of the changing UK’s

oceanic temperate climate. Finally the correlation between energy use, daylight and occupant

comfort will be established. The dynamic simulation tools EnergyPlus ,TRNSYS and DIVA for

Rhino will be used to analyse the key performance indicators. The building modelling will be


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done by using DesignBuilder, TRNSYS and DIVA for Rhino for the thermal, system and climate

based dynamic daylight performance assessment. The validation of the model will be done by

using empirical tests method by comparing the measured data from the monitoring process.

1.1 Research Aim and Objectives

The aim of the project is to improve occupant comfort (thermal and visual comfort) with reduce

energy usage in the energy efficient non-domestic buildings. It will also perform the gap analysis

between predicted and post occupancy performance of the buildings.

The objectives of the research are as follows:

To reduce the actual energy consumption for both space heating and cooling by

maximizing use of passive measures.

To evaluate the suitability of passive ventilation in providing the thermal comfort in the

design life of building in context of the changing UK climate.

To evaluate the correlation between energy use, day lighting and the occupant comfort in

the building.

To evaluate the effect of unregulated energy use on the thermal comfort.

To examine the potential of visual discomfort.

To provide the methodology for the efficient operation and management of building

energy system.


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1.2 Problem Statement

This research addresses the following problem:

“How to reduce the operational energy use without compromising the occupant’s comfort in the

energy efficient non-domestic buildings”.

1.3 Methodology

The aim of this research project is to improve the occupant comfort (both thermal and visual)

with improved building performance in the non-domestic buildings. The performance criteria

will depend on the specific building type and the specific uses for which it is built. The occupied

space used for an office purpose will have different performance criteria than the space built for

lecture rooms and auditorium. Therefore, in this project, separate occupied spaces according to

uses, in the non-domestic buildings, will be used to study the underlying relation between the

occupant comfort, energy use and daylighting. Furthermore the reasons of the gap that exists in

the predicted performance and the post occupancy performance of the non-domestic buildings

will be assessed.

The research will be based on the case study approach. Both qualitative and quantitative method

of analysis will be implemented to achieve the objectives. In qualitative method, data will be

collected by survey process employing a set of questionnaires and semi-structured interviews

with the occupants to understand their perception and preference of comfort. The data collected

by this method has more probability of predicting real case scenario, since it provide rich and in-


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detail information on occupant's preference. Moreover, it also provide the prospect to alternative

explanations of the actual query. The attempts will be made to link the response obtained from

the questionnaires and interviews (discrete data) with the continuous quantitative data obtained

from the quantitative method by employing various statistical method e.g. ANOVA. The

discrete and continuous data can be analysed in different methods based on data type. The figure

below shows the general method of analysis of continuous and discrete data.

Y

Discrete Logistic Regression X2- test

Continuous Linear Regression ANOVA,

t-test

Continuous Discrete X

Figure 1.2.1 Methods of data Analysis

In quantitative method, the data will be collected by monitoring process taking spot

measurements with instruments at different location of the spaces to measure the indoor

environment factors. In general, quantitative data can be analysed by simplified approach for

example simple monthly calculation method of BS13790 or dynamic simulations approach using

simulation tools for example EnergyPlus, Trnsys, DesignBuilder etc. The simplified method is

based on simple algebric equations while dynamic simulation considers transient behaviour of

systems and building. The practitioner preference for simplified method is due to the reason that

it has small numbers of inputs, well defined stepwise calculations, correlation between inputs

and outputs can be identified easily. Since the nature of the building control systems and


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components are dynamic in nature, many practitioners prefers to use dynamic simulation tools (

Kim, Y. J. et.al., 2013).

The dynamic simulation approach considers transient simulations within time-varying

parameters, modelling capability compare to simplified approach. It is capable of integrating

performance assessment of energy, occupant comfort, daylighting, etc. and also can evaluate

the innovative and creative design solutions (e.g. double skins with very elaborate control

algorithm for dampers and blinds). However, the dynamic approach requires many inputs, which

may leads to numerous subjective assumptions at the conceptual stage simulations( Kim, Y. J.

et.al., 2013). Hence this might lead to gap in predicted energy consumption and in-use

consumption.

In the second stage of research, the baseline simulations based on the design data of the

individual buildings will be performed. The case study buildings will be modeled using

DesignBuilder, which is integrated with the EnergyPlus simulation engine. For the analysis of

airflow pattern, stratification of air and its effect on the internal temperature, computational fluid

dynamics (CFD) analysis can also be performed using DesignBuilder. If the algorithms inside

the programme are not adequate to interpret the true requirements of the problem, then Matlab

can also be integrated with EnergyPlus for the mathematical interpretation. TRNSYS will be

used to model the temperature variation at different heights and flow rate of an individual stack,

The results obtained from TRNSYS can be linked to EnergyPlus using Matlab/Simulink. The

assessment of climate based dynamic daylight performance indicators, Useful daylight

illuminance (UDI), Daylight autonomy (DA) and Visual discomfort will be performed using the

DIVA for RHINO plug- in and the building model will be generated using Rhinocerous 3d


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programme. The empirical validation of the baseline simulation will be performed using the

measured data collected from the monitoring process. In this way, after the validation of the

model, the simulations will be used to obtain the results for the key performance indicators,

actual annual energy consumption, thermal comfort, daylight autonomy, useful daylight

illuminance and visual comfort. The values thus obtained will be compared with the current

available benchmark data. The comparative analysis of the results obtained from the different

simulations, EnergyPlus, TRNSYS and DIVA for RHINO will also be performed to evaluate the

variations of the performance indicators obtained from different dynamic simulations tools.

The sensitivity of the input variables to the key performance indicators will be studied using the

sensitivity analysis for example differential sensitivity analysis (DSA) or Monte-Carlo analysis

etc. to prioritize the key influential input variables which have marked effect on the key

performance indicators. After scrutinizing the key input variables which will affect the occupant

comfort, the alternative passive measures to improve the comfort level will be suggested. In the

third stage, the dynamic simulation of buildings with the improved passive measures will be

performed to evaluate the occupant's comfort and the performance of the buildings.


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Case study building

To begin with the project, the one of the case study building considered for the assessment is

east-park, Loughborough University design school building. The building is a low carbon

building with inclusion of many sustainability features. The building is built to achieve energy

performance certificate (EPC) of rating B. The district heating is supplied from the

Loughborough university combined heat and power energy centre. All systems within the

buildings are controlled by using intelligent building management system (BMS), a TREND 963

PC BMS. The lighting controls are automatic dimming controls with the occupancy detected

through passive infrared ( PIR) sensors.

Figure 1.3.1: East-park Loughborough University Design School Building

Source: Reproduced from IBMS, 2011


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The building utilized all the energy efficient design measures such as solar shading, thermal

mass, rain water harvesting to improve the building performance. However, the occupants are

complaining about the thermal discomfort during the winter season. the reason behind choosing

this building as one of the case study building is, even though it has employed good energy

efficient measures, whether it will be possible to reduce the operational energy usage without

compromising occupant's comfort. To see how the unregulated energy use of the buildings

effects on the thermal comfort of the building. Henceforth to propose passive technologies in

case of refurbishment and to built similar type of buildings. In this building, different rooms will

be studied based on the activity and uses of the rooms.

The Loughborough is located nearby to the Birmingham weather station. Annual mean wind

speed of the location ranges from 6-7 m/s.(DECC,2013). The climate files for UK degree-day

method, Birmingham station will be used in simulation process.


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Figure 1.3.2: UK degree-day regions

Source: Reproduced from CIBSE, TM41,2006

Primarily, the two spaces, the Lecture theatre and the seminar room is selected for the

evaluations. The ventilation mode for both spaces is based on the natural ventilation concept

using passive stack ventilation. In the lecture theatre, air enters inside the room from the air

inlets, plenum through natural convection and leaves the room through the stack openings at the

ceiling levels. This phenomenon is due to the temperature difference of inlet air and outlet air.

When the seminar room is occupied, the inlet air gains heat from the occupants and thus rise due

through outlets due to stack effect. In the winter season, the set point temperature is set to 18ºC

for occupied periods and 21ºC for unoccupied periods. In winter, a recirculation fan, slab

dampers as well as heater batteries are used to preheat the rooms during unoccupied periods.The

operative temperature of the space was designed to operate at 16° C, however due to occupants


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dissatisfaction about being cold inside the room, the temperature is set to 21ºC for occupied

period. Thereby, increasing the energy usage by the building.

Figure 1.3.3: A schematic drawing illustrating the ventilation strategy

Source: Reproduce from IBMS, 2011.

The Boost fans are also used in the auditorium to maintain the internal temperature during the

summer period when the buoyancy forces are limited and air flow is minimum thus reducing the

stratification. It is ensured that the internal temperature inside the auditorium does not exceed 28°

C for more than 1% of the overall annual occupied hours, overheating criteria. In winter, a

recirculation fan, slab dampers as well as heater batteries are used to preheat the rooms if the

temperature inside the room is less than 21° C before occupancy time.


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Figure 1.3.4: Low-level air supply below feet in lecture theatre

The air temperature sensors which were used to monitor the air temperature inside the lecture

theatre were the Hobo Pendant Temperature/ Alarm Data Loggers 8 & 64K models (UA-001-8

& UA-001-64).

Figure 1.3.5: The Hobo Pendant Temperature/ Alarm Data Loggers

Source: Reproduce from Onset, 2013


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The 12 data loggers had been launched successfully, every three data loggers were then mounted

at one location with three different heights. The chosen heights were 0.1, 0.6 & 1.1m which

reflect the ankle, torso & head heights respectively. The sensors are mounted at three different

levels to get an idea about stratification of air forces inside the lecture theatre. . Moreover, the

four sets of data loggers were mounted at 4 locations, on the north wall, west wall, east wall & at

the middle of the lecture theatre. The reason for this is to have an understanding of the indoor

conditions perceived by the occupants sitting at different locations in the lecture theatre.

Figure 1.3.6: Locations of sensors on east wall.


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Figure 1.3.7:. Sensor locations at the middle of lecture theatre

Figure 1.3.8: Sensor locations on north wall


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Figure 1.3.9. Sensor locations on west wall

The room air temperature data are taken for every 30 minutes for a period of one week from the

21st to the 28Th of June. The data from the sensors were then read out using the Hoboware

programme and the graphs of the temperature distribution at for every location were plotted. The

air temperature readings thus obtained will be used for comparison and validation of the outputs

obtained from the dynamic simulation tool EnergyPlus.

Similarly, for the air flow movement within the passive stack, the Transient dynamic simulation

tool TRNSYS will be used for the analysis. The TRNSYS will also be used to analyze the

suitability of control system used in the building.

Daylight performance analysis


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The daylight performance indicators are analysed using DIVA for RHINO simulation programs.

The model for the Lecture Theatre is drawn using Rhinocerous 3d programmes. The following

materials with the reflectance values has been assumed in the analysis.

Ceiling Generic ceiling -80

Door Metal-diffuse

Floor Genericfloor-20

Ground surface Outside ground-20

Wall Generic Interior wall-50

Window Generic Translucent panel-20

No dynamic shading is assumed in the analysis

Lighting Control:

Operation Dimming with occupancy on/off sensors

Lighting setpoint 300 lux

Ballast loss 20%

Standby power 0 W

Table1.3.1 : Material assumptions in DIVA for Rhino analysis


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Figure 1.3.10. Snapshot view of the model of Lecture Theatre.

Figure 1.3.11: Snapshot of daylight factor analysis of the Lecture Theatre


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Figure 1.3.12: Snapshot of illuminance level at Lecture Theatre

Figure 1.3.13: Annual glare simulation, Visual comfort without occupant adaptation


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Figure 1.3.14: Intolerable glare, daylight glare probability-58%

Results

Daylit Area (DA300lux[50%]) 8 % of floor area

Mean Daylight Factor 2.1%

Occupancy 1827 hours per year (10 hours per day)

Predicted annual electric lighting use (kWh) 466.4

Glare 0.0% of occupied hours.

Table1.3.2: Result summary from DIVA for RHINO analysis


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The analysis shows that mean daylight factor is 2.1%. The 6.2% of the floor area has DF

between 5% and 92% of floor area has less than 2%. The mean illuminance values is 1181.34

lux. The 92.9% of area falls below 300 lux. This shows that useful daylight illumination falls

into the category of UDI- supplementary, where occupants will require the additional artificial

lights. The percentage of the space with a UDI <100-2000 lux larger than 50% is 7% for active

occupant behaviour. The mean daylight autonomy is 10% for active occupant behaviour. The

percentage of the space with a daylight autonomy larger than 50% is 8% for active occupant

behaviour. The mean continuous daylight autonomy is 17% for active occupant behaviour. The

percentage of sensors with a DA_MAX larger than 5% is 7% for active occupant behaviour.

Similarly, the same process will be followed for the seminar room. In this way, different case

study buildings will be selected according to its activity and the purpose for which it is built and

the analysis of performance indicators will be done.


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1.4 Research Outcome

In general, building are designed to obtain good energy performance certificate (EPC) rating to

comply with the government mandatory regulation. It is also assumed that the building with

good EPC rating will have better building performance. However, in practice the case is not the

same and the building with lower EPC rating have better performance. It is due to the fact that,

the EPC ratings only measures the theoretical performance or design intent performance and it

does not measure the actual energy consumption by the buildings. The energy consumption by

the building is very much affected by the occupants requirements, occupant's operating hours

and intensity of the energy use. These factors are not considered in the EPC ratings. As a result it

does not give the true indication of the performance of the building. In reality the operational

energy usage by the building is higher than defined by EPCs. Therefore, the research will

analyse the gap existed between predicted and actual performance of the buildings and suggest

the measures to reduce the gap by evaluating similar buildings by its activity and built purpose.

The research will provide the guidelines for the stakeholders and policy makers to improve

occupants comfort in the energy efficient non-domestic building with maximising the use of

passive technologies. Moreover it will evaluate the underlying correlation between the daylight

and the occupants comforts in terms of energy saving potential in the non domestic buildings.

The methodology chosen in this research will help to do the comparative analysis of results

produced from different dynamic simulation tools (DesignBuilder, EnergyPlus, TRNSYS and

DIVA for RHINO and will answer the appropriateness of the algorithms used inside the

simulations tools to assess the performance indicators.


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In addition to that, efficient operation and management of the control systems will reduce the

actual energy consumption, thereby improving the performance and the indoor environment of

the building. Hence, this research project will suggest on the appropriate control systems,

operation and management to improve the financial implication and the performance of the non-

domestic buildings.

1.5 Organisation of the report

The report is organized in different section including introduction, background research aim and

objectives and methodology of the presented research work. In the literature review section,

different available passive technologies which can be employed in the energy efficient non-

domestic buildings is discussed. The climate data variables used in the dynamic simulations in

UK climate is presented. The suitability of passive ventilation in the UK temperate climate and

the environmental factors influencing the occupants comfort ( thermal and visual) in the

buildings is reviewed in chapter 3.0 and chapter 4.0. Similarly, state of art of different dynamic

simulation tools for energy performance and daylighting analysis is presented in chapter 5. In

chapter 6.0, for the proper operation and management of building, the rationale of choosing

appropriate control strategies and system for performance improvement is discussed. Finally,

research methodology is discussed together with the research work schedule.


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2.0 Literature Review

2.1Background

The adverse effect of global climate change is evident in this era. It is due to the emission of

greenhouse gas (GHGs) to the atmosphere. To certain extent, the natural phenomenon is

responsible for the emissions, hugely the phenomenon is because of the anthropogenic (man-

made) contributions. The active use of fossil fuels by the mankind is the major contributory

reason for the greenhouse gases (GHGs) emissions. If the attention is not paid to reduce the

emissions, there is a probability of increasing the global temperature of more than 2ºC compare

to preindustrial times (European Commission, 2007).

To prevent unwanted natural calamities, by acknowledging the fact that the human activities are

substantially responsible to the global climate change, the United Nations reached into

agreement on framework convention for the Climate change (UNFCC, 1992). In subsequent

attempt to formulate a common goal for countries involved in the formulation of the framework

on the climate change, the ‘Kyoto Protocol’ has been defined and implemented on the year 2005.

However under the Kyoto Protocol, the binding countries are limited to developed countries

only. The emission reduction was to achieve five percent less than the 1990s base level and

implementation year was 2008 to 2012 (UNFCC,1997). In the recent development, the second

phase of Kyoto protocol has been extended to the year 2020 (Roger Harrabin, 2012).In the

second phase more vigorous approach for emissions cut down, technologies development and

compensating the countries affected by emissions from the countries generating emissions has

been addressed.


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In parallel to the Kyoto Protocol, the United Kingdom government passed the act on the climate

change (2008) which sets the target to reduce overall GHG emissions to 34 percent by 2020 and

to 80 percent by 2050, below 1990s base line level (UNFCC,1997). Under the framework to

tackle climate change, the UK government initiated three measures. Carbon budgets that is

capping the GHG emissions, climate change agreements (CCA) for the energy intensive

industries to provide discount under climate change levy and carbon reduction commitment

(CRC) energy efficiency schemes to improve energy efficiency and reduce emissions from the

large private and public sector buildings (UNFCC, 1997).The UK government also published

2050 pathways analysis to reach its target level of 80 percent by 2050, which guides the process

to follow to cut emissions. Similarly, the European Unions in the year 2005 introduce EU

emission trading system (EU ETS) policy to reduce the GHG emissions by 8 percent below

1990s base level (DECC,2012).

In the ‘Kyoto Protocol’ mainly six gases are recognized as global warming prone gases which

directly influence the global climate change (UNFCC, 1997). These gases are carbon dioxide

(CO2), methane (CH4), nitrous oxide (NH4), hydroflurocarbons (HFCs), perflurocarbons (PFCs)

and sulphur hexafluoride (SF6). The carbon dioxide (CO2) is one of the major gases in the GHG

emissions. All other gases are expressed in the carbon dioxide (CO2) equivalent metric (shine et

al., 2005,) using global warming potential of 100 years.

In the UK, overall 40 percent of carbon emissions are from building sectors of which 17 percent

of carbon emissions are from the non-domestic building sectors. The government has the

stringent target to make all building zero carbon buildings by 2019. Still, 40 percent of the non-

domestic building which will exist in the year 2050 has not been built yet (UNFCC, 1997).


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Therefore to cut down the emissions from the non-domestic building sector and to come out of

energy dependency, it will be a good practice to deploy passive measures in existing buildings.

From the beginning, at the planning stage of any development, if the proper planning of land use

and energy pattern is planned, the energy use can be reduced efficiently. Also the utilisation of

the renewable sources, proper management, maintenance and operation of building energy

systems are some of the measures that can be considered to reduce the energy consumption by

the buildings. Since 70 percent of the buildings that will be there in 2050 (UNFCC,1997) are

already built, government regulatory scheme building regulations such as CRC energy efficiency

scheme and EU ETS scheme will act as key driver for reducing energy use and emissions from

the energy intensive buildings.

Similarly, the Energy Performance of Buildings Regulations (EPBD) in 2008 introduced the

requirement for Energy Performance Certificates (EPC) for the improvement of the energy

performance of the buildings. It is mandatory requirement for the all constructed, sold and let

buildings. However, the EPCs certificates address only the theoretical energy performance and

design intent, it does not shows the actual energy consumption by the buildings. The variation is

due to the fact that in the EPCs, the energy demand from the occupants which depends on energy

loadings of the space, intensity of energy use, occupant’s operating hours are not considered

(Lassalle, L. J. 2012).

Similarly, the Energy Performance of Buildings Regulations (EPBD) in 2008 also introduced the

requirement of the Display Energy Certificates (DECs) in the public buildings in England and

Wales. DECs are currently required only in buildings occupied by the non-domestic buildings

over 500m2 areas. The DECs will show the actual energy (metered) consumption and


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operational rating of a building. The operational rating is based on the amount of energy

consumed over a period of 12 months from meter readings, compared to a similar building type

with performance equal to the benchmark. The operational rating is shown on the scale of A to

G. The A being the best performance buildings and the G is the worst performance one. The

energy consumed by the building in any forms are required to combine together to analyse in

terms of single indicator. Therefore, the operational rating (OR) is used as an indicator for

carbon emissions to predict the performance of building. The operational rating (OR) is a

measure of the annual (CO2) emission per unit of area of the building due to energy

consumption, compared to similar building type with performance equal to the benchmark. The

following factors contribute in determining operational energy ratings. These are the building

category, building location, total useful floor area, energy consumption, building’s separable

energy use (for example swimming pool flood lights, bakery oven etc.) and the occupancy (CLG,

2008, pp.27). Similar to the DECs, The energy performance certificates (EPC) are also

introduced for the non-domestic buildings. It shows the design intent or theoretical energy

performance of the non- domestic buildings which is required to meet the benchmark set for the

buildings.


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Figure 2.1.1:The quarterly sector wise energy usage trend in UK from the year 2010-2013

Source: Reproduced from Energy trends,DECC,2013.

Figure 2.1.2:The sector wise energy consumption in 2011

Source: Reproduced from Energy trends,DECC,2013.

Domestic

28.10%

Other final

users

12.40%

Transport

39.90%

Industry

19.80%


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The non-domestic sector currently spends over 85% on electricity, 10% on gas, and around 5%

on oil. The electricity and gas combined accounts for 0.4 percent of the total sector costs. In the

year 2011, non-domestic sector consumed around 30% of total UK electricity use (i.e. 96 TWh)

and 12% of gas used outside the power generation sector (i.e. 59 TWh). If energy consumption

were to remain at current levels, then for an average user covered by the carbon reduction

Committee ( CRC), the energy bill will increase by 25% by 2020.This increase includes a 7%

increase due to the increasing carbon price and a 21% increase due to direct support for low-

carbon generation (i.e. renewables, nuclear etc.), offset by a 2% reduction since the cost of the

carbon reduction committee (CRC) includes power sector decarbonisation (Committee on

Climate Change, 2012). Therefore it is essential to reduce energy usage from the buildings to

improve the energy efficiency in today’s carbon constraint world.

2.2 Climate data for building design.

In the design process, at conceptual design stage, the prediction of the annual energy

consumption is obtained by using building energy simulation programs. Among different input

variables in the building energy simulation programme, the climate data is an important input

variable that affects energy consumption for space heating and cooling of the buildings.

Therefore the climate data relative to future climate change is very crucial factor which should

be incorporated in the buildings energy simulations to predict energy consumption by the

building.


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The world Meteorological Organization defines climate as a 30 year period but for the simulation

modelling engineers use a single year period (Holmes and Hacker, 2007). Therefore it is quite

important that the chosen year (known as test reference year, TRY) depict the weather conditions

of number of year. The method to select the TRY varies between different countries. In the UK,

the TRY contains a set of 12 months weather data which depicts the past weather of number of

years. Since the TRY is based on the past weather scenario, it does not take into account of the

temperature rise that will take place in future. Therefore the building energy performance for

extreme weather based on the current method of TRY weather data is not suitable for

overheating risk analysis and it is only used for the HVAC sizing. (Holmes and Hacker, 2007).

Hence, another method to deal with the extreme weather, design summer year, DSY has been

introduced by CIBSE based on UKCIP02. In the DSY, the average hourly temperature of the

period April–September in a year is obtained from about 20 previous years. The selected year is

the mid-year of the upper quartile. The DSY is mainly used for the assessment of overheating

risk and also for the performance of naturally ventilated buildings. The TRY data is used for the

prediction of the energy consumption by the buildings (Holmes and Hacker, 2007). The

TRY/DSY is available for 14 locations in the UK (CIBSE, 2012). The current TRY/DSY are

derived from the measured UK Met office site data from the year 1983 to 2004. CIBSE weather

data are considered as an industry in the UK and is used for building regulation compliance

testing since TRY data has been used to determine monthly average value SBEM tool.TRY/DSY

weather files are available in different file format by CIBSE to use with different simulation

packages (Jentsch F. M. et.al., 2008). Since the hourly weather data is limited, if diurnal cycles

are to be assessed, because the interpolated values might not reflect true weather scenarios.


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Therefore sub hourly weather data sets have been created by the developers of EnergyPlus and

ESP-r, this format is termed as EnergyPlus/ESP-r Weather (EPW). It uses the file generation

methodology and data available in Typical meteorological year (TMY2) format which was

developed by U.S. National Renewable Energy Laboratory (NREL) in 1990s. Most of the

simulation package use EPW weather file format such as DesignBuilder, EnergyPlus, ESP-r IES,

while TRNSYS use TMY2 format.

Belcher et. al., 2005, proposed the morphing method in which the future climate change is

considered for the building thermal simulations. In the morphing method, present day design

weather data is adjusted to the changes of climate forecast by global circulation models and

regional climate models. It is reliable because it uses the present day weather series and it is

meteorically consistent. However the future prediction of weather data has got the character of

present weather data and the future climate might have different character than the present

weather data. “For example the average temperature will rise in the UK, but the question is

whether the temperature will increase across the whole period or only during the summer period”

(Belcher et. al., 2005). Many researches have been performed based on the new UKCIP09

weather data for more accurate prediction of the future climate which will affect the building

performance in its life cycle.

According to the UKCIP predicted data for 2030, there will be increase of 1ºC ambient

temperature in the UK buildings .The increase in temperature will not be beneficial for the

buildings with cooling loads requirements. Also due to more technologies used in the buildings,

it will add on the casual gains. This will also result into more space cooling requirements in the

buildings. Jenkins and Peacock, 2008 suggested that , it is always desirable to design the


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buildings that require heating loads rather than the cooling loads. In the research work performed

by Hawkins et.al., 2012, using the statistics of DEC data on the annual electricity use and energy

consumption by the institutional buildings, found that electricity use is high and heating fuel use

is low compared to the data provided by CIBSE TM46 for the University campus category. The

electricity use and energy consumption depends on the building activity. Other factors which

affect the electricity use and energy consumption are environment, primary material, heating

fuel, glazing type and ratio, height and aspect ratio. It is noted that the age of the building do not

have significant effect on the energy usage by the buildings.

3.0 Passive ventilation

The low energy buildings are of paramount interest to reduce the carbon emission from the

buildings. With increase awareness of the energy use, passive ventilation has become attractive

method to reduce energy use, operational costs and to provide better indoor environment

conditions compare to the mechanically ventilated buildings. The passive ventilation also known

as the natural ventilation is the use of natural forces to supply and remove air from the buildings.

There are two types of passive ventilation, the buoyancy driven and the wind driven ventilation.

In the buoyancy driven ventilation, air is driven through the building by vertical pressure

differences developed by thermal buoyancy ‘stack effect’. The warm air inside the building is

less dense than cooler air outside, and therefore will exhaust from openings high up in the

buildings. The cooler denser air will enter from the openings at lower level of the buildings. The

stack effect is dependent upon the height of the stack, the difference between the average

temperature of the stack and the outside temperature and the effective area of the openings. In

the wind induced ventilation supply air comes from a positive pressure through openings on the


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windward side of a building and exhausts air to a negative pressure on the leeward side. The

airflow rate depends on the wind speed, wind direction and the size of openings (Baker Nick,

2012, RIBA). The stack effect is favourable during periods of low wind speed and reduces in

summer periods when temperature differences between indoor and outdoor of the buildings are

minimal.

In order to make stack ventilation more effective even during low-indoor outdoor temperature

difference, some advanced stack ventilation strategies utilizing the natural forces available from

both the sun and wind has been developed. For Example the solar induced ventilation, wind-

stack driven ventilation and even fan induced stack ventilation. Some of the examples are solar

chimney, solar roof, double facade, wind catchers, wind cowl, wind towers and hybrid wind

turbine ventilators (BS EN ISO 7730, 2005).

Liu et.al., 2009, has suggested that the external ambient temperature has a larger effect on the

temperature distribution in the atrium space than the thermal load inside the building. The

position of the stack openings can improve the internal thermal environment and the size of the

stack openings also affects the temperature distribution. In the hot and humid climates due to

small temperature difference, a buoyancy-only ventilation strategy is not very effective,

additional methods such as wind-driven ventilation, wind-buoyancy ventilation or mechanically

driven ventilation will be necessary to achieve the desired thermal comfort.

.1 Available passive measures and technologies

There are different approaches to employ passive measures in building design to reduce energy

usage by the buildings. Initially, passive measures can be included as a design features in the


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design process such as by improving thermal mass of the building, choosing passive ventilation,

Insulation, airtightness of the buildings, removing thermal bridging in the building by detailing

the construction joints properly, solar shading, daylighting etc.

In an attempt to reduce carbon emission from the mechanical units of the buildings, the passive

technologies such as wind catchers, wind towers, wind cowls and solar chimney can also be used

to assist passive ventilation and space cooling of the buildings. The Wind catcher is an

architectural feature mounted on the roof of a building to brings in the fresh air from outside.

Wind catchers are available in many types, mainly vernacular wind catchers, modern or

commercial wind catchers and super modern wind catchers ( Saadatian O. et.al., 2012).


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Figure 3.1.1: The different types of vernacular wind catchers

Source: Reproduced from Saadatian O. et.al., 2012

Figure 3.1.2: The different forms of modern/ commercial wind catchers

Figure 3.1.3: Wind catcher tower proposed design for future.

Source: Reproduced from Saadatian O. et.al., 2012


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Figure 3.1.4: The structure and principle of wind catcher system

Source: Reproduced from Li L. and Mak M.C., 2007

In the wind catcher, fresh air is drawn inside the building from the windward side and stale air

is extracted to the leeward side of the building through the passive stack due to pressure and

temperature differences created by stack effects. There are mainly two driving forces acting for

wind catcher system, buoyancy force and external wind for the operation of wind catcher, where

external wind plays significant role compare to buoyancy force( Saadatian O. et.al., 2012 ).The

performance indicator of wind catcher is the rate at which fresh air is drawn to the building and

stale air is removed from the building. Therefore, it is necessary to find the ventilation rates

before choosing the opening sizes of the wind catchers. In general, wind catcher will have

weather proof louvers to protect the buildings and volume control dampers are used to control

the air flow rates. With the aid of night cooling, the wind catcher is more efficient means of

passive technologies to maintain pleasant occupant comfort ( Saadatian O. et.al., 2012 ).


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Similarly, another passive technologies that can be implemented is solar pond also termed as

brine pond. The solar pond work as the collection and storage of solar energy in the form of

thermal energy in the same medium for a period of time (Kurt H. et.al., 1999). It has got many

practical advantages such as ease of construction, use of commonly available salt and water,

combined collection and storage of solar energy , easy extraction of heat on demand for practical

uses. In general, the solar pond consists of three layers, the upper convective zone (UCZ), the

non convective zone (NCZ) and the lower convective zone (LCZ). The upper convective zone

mainly consists of fresh water and the salt concentration increases with the depth of pond, high

concentration at the lower convective zone. The NCZ acts as a insulation layer for the LCZ.

If the concentration gradient of the NCZ is high, the convection will not occur in this layer and

the energy absorbed will be stored in the LCZ. Since the water is fluid and it does not transmit

infrared radiation, so only the visible light part, short wave radiation of the solar energy spectrum

reaches the bottom of the pond and is absorbed at the LCZ layer. Because of the poor conductive

capability of water, the nature of infrared radiation and the insulating property of the NCZ, the

stored energy in the LCZ only escape from the pond with conduction. Therefore, the solar ponds

acts as both collector and heat storage device.


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Figure 3.1.5: The salt gradient solar pond configuration

Source: Reproduced from Kurt H. et.al., 1999

The thermal energy stored in the LZC of the solar pond can be extracted in two methods. In the

first method, the heat of the heated salt/brine is removed using external heat exchanger such as

heat pumps. In the second method, a heat exchanger is placed in the lower convective zone of the

solar pond. However this method is comparative harder to implement, as it requires large number

of tubes, difficulty to locate heat exchanger, difficulties for maintenance and corrosion problems

(El-Sebaii A. A., et. al, 2011). In general, the solar pond is used for heating and cooling of the

buildings, industrial processes, power production, desalination etc.

3.2 Environmental factors of the building

The key environmental factors to be considered for indoor environment quality in design process

are, thermal comfort, indoor air quality, acoustics comfort and visual comfort. There exists an

interaction between each factors in building design process ( Sarbu I. and Sebarchievici

C.,2013). It is always recommended to have a good environmental control strategy which is


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capable of maintaining pleasant indoor environment for healthy living which are broadly

satisfied by the occupants occupying the space.

In this research work, specifically two environment factors, thermal comfort and visual comfort

will be evaluated. In reducing the actual operational energy usage of the building, how these

factors will be affected and what design improvements can be implemented to improve indoor

environment factors will be reviewed in detail. These environmental factors are discussed in

details in succeeding topics below.

3.3 Thermal Comfort

In practice, the buildings should be designed to provide adequate occupant comfort and good

indoor environment conditions. Occupant comfort is influenced by the various indoor

environment conditions such as thermal, visual, acoustic and indoor air quality. According to

ASHRAE Standard 55-2004, thermal comfort is "that condition of mind which expresses

satisfaction with the thermal environment." According to EN ISO 7730:2005, “The thermal

sensation of human being is related to the thermal balance of his or her body as a whole”. The

thermal balance of the body is influenced by the six parameters, among which four

environmental parameters are air temperature, mean radiant temperature, air velocity and air

humidity and two personal parameters which are clothing insulation and activity level

(Djongyang N. et.al, 2010) There are two different approaches available to define the thermal

comfort at present, the rational or heat balance approach and the adaptive approach. The heat

balance approach based on Fanger experiment on controlled climate chamber using steady state


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heat transfer model, laboratory experiment and the adaptive approach based on the field studies

of the occupant inside the buildings (De Dear R. and Brager G.S, 1998).

The predicted mean vote (PMV) index has been developed by the Fanger (1970), based on the

assumption that the body must be in thermal equilibrium with its environment i.e. the heat

generation by the body must be equal to the heat loss by the body to the environment. The other

two assumptions are the mean skin temperature and the sweat rate should be within limits of

acceptable comfort range (Awbi H.B,1991).

The thermal sensation by Fanger is based on seven psychological states. These are cold -1, cool

-2, slightly cool -3, neutral 0, slightly warm +1, warm +2 and hot +3. The Fanger correlated the

ratio of the percentage of people who were dissatisfied in the thermal environment with predicted

mean vote. This ratio is called the predicted percentage dissatisfied (PPD). It is an index used to

predict the percentage dissatisfaction by predicting the percentage of people likely to feel too

warm or too cool in a given environment (BS EN ISO 7730, 2005). The PMV is widely used

method to assess the individual differences for thermal comfort. Eventhough, the body may be in

the thermally neutral at certain environment, dissatisfaction may arise because some parts of the

body is cold compare to another parts due to unwanted heating or cooling of the body .This local

thermal discomfort should not arise. The local discomfort occurs due to radiant temperature

asymmetry, draught, vertical air temperature differences and the cold or warm floors

((Djongyang N. et.al, 2010).

The prediction from the heat balance approach is accurate for the human in near sedentary

activity and steady state conditions. The PMV method is good in predicting comfort conditions


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for the buildings with heating ventilation and air-conditioning (HVAC) systems (BS EN ISO

7730, 2005). However, the heat balance approach was conducted in controlled consisted

environment climate chamber and climate chamber might not wholly depict the real environment

which is inconsistent. Moreover, for the Fanger heat balance approach, the parameters for the

accurate measurement of the thermal comfort conditions at every point is difficult to determine,

therefore, the equivalent operative temperature is used for defining the parameters. The operative

temperature is the uniform temperature of an imaginary black enclosure in which an occupant

would exchange the same amount of heat by radiation and convection as in the actual non-

uniform environment (Sourbron M. and Helsen L., 2011).

Therefore, for the simplicity of the thermal comfort assessment adaptive approach has been

developed from the field studies. The fundamental assumption of the adaptive approach is if a

change occurs such as to produce discomfort, people react in ways which tend to restore their

comfort. The Adaptive thermal comfort models assume that people adapt their thermal

requirements because of three different mechanisms, behavioural adaptation (clothing, activity,

opening windows, operating fans etc., physiological adaptation and psychological adaptation. In

the research work done by de Dear and Brager (1997), concluded based on extensive field

studies that in naturally ventilated buildings occupants seem capable of adapting to a broader

range of conditions. The occupants are adaptable to both higher indoor summer temperatures and

lower indoor winter temperatures than predicted by ISO7730. Several attempts have been made

to incorporate this adaptation into thermal comfort standards by relating the indoor operative

temperature to a reference outdoor temperature. For passive ventilated buildings, it is found that


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there exists a linear relationship between indoor operative temperature and outdoor monthly

mean temperature (Humphreys, 1978).

The Moujalled B. et al., 2008, performed the studies on five naturally ventilated offices building

in France using both static and adaptive approach, it has been concluded that the adaptive

algorithms are more reliable to evaluate the thermal comfort in naturally ventilated buildings

than the standard PMV index. While assessing thermal comfort condition using PMV index,

based on ISO7730 standard, it has been noted that the building suffers from overheating during

cold and require air-conditioning during warm season. Hence the PMV index is not appropriate

for naturally ventilated buildings, while adaptive algorithms from EN15251 standards are better

for thermal comfort and energy use, since it accounts for variability of indoor comfort

conditions.

Similarly, McGilligan C.et.al., 2011, proposed the concept of Adaptive Comfort Degree-Day to

quantify the energy savings from Adaptive thermal comfort model in the free running buildings.

The method has been applied to series of climate different locations under different emissions

scenarios in the United Kingdom for the 2020s, 2030s, 2050s and 2080s. The comparison

between European adaptive standard EN15251 and ASHRAE 55 adaptive standard using

Adaptive Comfort Degree-Day method showed that EN15251 standard has more possibility of

energy saving compare to ASHRAE 55 standard.

The debate of which approach to be employed in achieving the accuracy in results will always be

present. In the research performed by Humphrey A. M. and Hancock M., 2007, pointed out that

the desired sensation on the ASHRAE scale is often other than neutral, and that it is dependent


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on person to person. Because the people have different desires and choices for their thermal

sensation. These differing personal desires ranges from slightly cool to hot. Mostly, people's

preference is neutral, followed by slightly warm. In ASHRAE scale, 1 unit is equivalent to 3°C

temperature. In temperate climate like UK, the general assumption is 1° C increase in

temperature results into 10% energy consumption. (Humphrey A. M. and Hancock M., 2007).

hence, we can say that requirements to provide the desired thermal sensation might affect

theoretical estimates of energy consumption, both for winter heating and for summer cooling.

However, there will probably be less effect on the real usage of energy by buildings, since people

normally control their thermal environment to meet comfort instead of achieving thermal

neutrality on the ASHRAE scale.

4.0 Daylighting

The adequate provision of the daylighting is essential factor in view of the occupant comfort and

energy saving potential in the non-domestic buildings. It is essential to provide sufficient

illuminance to perform various tasks and pleasant visual environment for the adequate visual

comfort to the occupants inside the buildings. The rationale behind the daylighting design can be

trace back to the historic buildings. In quest to save energy from the buildings and due to the

findings that the sufficient amount of daylight improves the productivity and health and well-

being of the occupiers, the inclusion of daylighting became essential in the design process.

However in practice, still the daylighting design consideration is still not given the required

importance in the design process.


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“Daylighting in the building is the natural illumination from the sun, experienced by the

occupiers through the openings” (Mardaljevic J. 2012, pp.5). The variation of the daylit quantity

and quality is random, daily or seasonal over a year period. The random variation is due to the

sudden appearance of the cloud on the sky, daily variation is change of sun and sky condition

from the day to night and seasonal variation is due to the changing day length and the weather

pattern. For any sun and the sky condition, the quality and quantity of the daylighting depends on

the size, orientation and nature of the building openings, the geometric shape and aspect of the

building and its surroundings and also on the optical properties (reflectance and transmittance) of

the building facades and its surroundings (Mardaljevic J.2012).

Daylight in other words is the total amount of visible radiation originating from the sky, which in

fact is due to the sun. The sunlight is scattered in the atmosphere due to the presence of air, dust

particles, water vapour which provides the sky as a self-luminous hemispherical light source.

Hence the Daylight comprises of two components the skylight and the sunlight. The sunlight and

the skylight both comprise of two components, the direct and indirect light. The direct light is

due to the direct sun light component and diffuse light is from the indirect sunlight and the both

direct and indirect components of the skylight (Mardaljevic J. 2012).

For the sustainable building design, it is required to understand the current practice and design

tools available in the construction industry that are used by the design practitioner to integrate

the daylighting design from the conceptual design phase of the design process. It is also

beneficial to supplement new information on the top of the design guides used in the current

practice. The proper daylighting planning is essential in all phases of the design process. From

the conceptual to the detailed phase and actual construction phase to the commissioning and


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operation phase, the daylighting design require to be prioritised for the better building

performance requirements.

In the conceptual design phase of design process, the accurate prediction of the daylighting for

any spaces is crucial for energy savings and to provide the better indoor environment in the

building. By integrating daylighting in design process, energy consumption for space heating,

cooling and lighting in the buildings can be substantially reduced. Generally the cooling load is

typically high for the non-domestic buildings due to the internal heat gains from the various

equipments and the occupancy schedule inside the building. It is known that the building

designed with daylighting approach will require less cooling loads in compare to the one without

daylighting design consideration. The reason is luminous efficacy which is the ratio of the

luminous flux to the power (lm/w) is higher for the outdoor rather than electric system used

inside the building (Johnson R et. al, 2013). However it is true under the some specific design

considerations. Since, the solar gain resulting from the improper daylighting design might negate

the overall energy savings from the daylighting. Therefore, for the daylighting design, attention

should be paid on the fenestration system, lighting system, proper management of the lighting

and shading controls and architecture of the space. So that the benefits of the energy savings

from electrical lighting requirements, cooling loads and lower peak electric demand will be

achievable in the buildings.

4.1 Climate effects for daylighting design

The climate of the locale has a greater influence in the design of buildings; it affects the building

loads, sizing of heating ventilation and air conditioning (HVAC) system performance and


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building energy consumption. The fundamental of the design process is to take advantage from

the external climate variables for example temperature, humidity and solar radiation and design

the suitable indoor environment. In the design process, it is essential to define climate

characteristic specific to the site. In daylight design, generally, the sky conditions are categorised

into five groups. The designers choose the appropriate sky conditions relevant to the climate

profile of that locale (Gregg D.A.2002).

• Uniform sky

• Overcast sky

• Clear sky

• Partly cloudy sky

• Direct sunlight

Uniform sky: The uniform sky condition provides equal amount of illuminance in all directions.

The hand calculation or artificial sky simulator is best suited to test the daylight performance of

the scale model under uniform sky conditions.

Overcast sky: In the overcast sky condition, water particles diffusely refract and reflect all

wavelengths of sunlight. As a result, the zenith is three times brighter than the horizon.

Clear sky: The light is diffuse because it is refracted and reflected as the sunlight passes through

the atmosphere. Thus the sky is brighter along the horizon than the zenith.


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Partly cloudy sky: The illuminance level varies depending on the cloud position relative to the

sun. Higher horizontal illuminance may result under a partly cloudy sky than under a clear sky.

Direct sunlight: Direct sunlight is perpendicular to the surface. Generally direct sunlight is too

intense for the task illumination.

In the following methods daylight can be analysed inside the buildings.

1. Hand calculation methods

Lumen method: It uses coefficient of utilization which is defined as the ratio of light

incident on a reference point to the light entering to the space.

2. Computer programs

Radiance

DOE2

3. Physical modelling

Scale model

4.2 Daylight Performance Indicators

With the integration of daylighting in the building design, there is higher probability of reducing

the use of artificial electric lighting inside the building. Also the heat gains from the electric

lighting can be substantially reduced in the building. Furthermore, it enhances the energy


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performance of the building by reducing total electricity load and peak demand. The daylight

performance of the building is mostly critical for the office buildings which utilize significant

amount of lighting energy consumption.

4.2.1Daylight Factor

The simplest quantitative measure for the daylight in the building used in practice is the daylight

factor (DF). The daylight factor is the ratio of the internal illuminance (E in) to the unobstructed

horizontal illumination (E out) under the CIE overcast sky and expressed as a percentage. Under

the CIE overcast sky condition, the luminance of sky is rotationally symmetric about its vertical

axis and also the luminance from the sun is not considered. Hence the daylight factor is not

affected by the building orientation and the climatic condition of the locale and it is the static

measure of the daylight. Therefore, due to its simplicity, it is used as the designer’s preferable

quantitative measure in the conceptual design phase. The DF is divided into three components;

Sky Component (SC), External Reflected Component (ERC) and Internal Reflected Component

(IRC). The total of these three quantities gives the daylight factor. (Kota S. and. Haberl S.J.,

1982).It was first proposed as the indicator of daylight performance by the Alexander Pelham

Trotter in the year 1895.

The average daylight factor (DFavg) is often used to approximate the available daylighting in the

space. The readings of the available daylight in the space at light levels and obstructed space

outside are taken on the grids, so that map can be drawn to average daylight factor. The optimum

average daylight factor according to the BS 8206-2:2008 is 2-5%. If the daylight factor is less


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than 2%, then additional artificial lighting is required, and if more than 5% there will be lesser

use of artificial lighting for performing tasks.

The daylight factors can be measured in various different methods.

• Physical modelling for example scale model

• Graphical, tabular and analytical method

• Computer simulation

In the physical modelling, for example scale model is used to predict the daylight factor, in the

graphical modelling for example Waldram diagram is used, and for the tabular modelling, BRS

table is used. In the analytical method, Lynes equation is used and for the computer simulations

for example radiance is used (Mardaljevic J., 2012) to measure the daylight factors.

The magnitude and distribution of daylight factor is depend on the size, distribution, location and

transmission properties of openings, the size and the configuration of the space, the reflective

properties of the internal and the external surfaces of the space and the degree of obstruction of

the sky view by the external structures (Mardaljevic J., 2012).

The major drawback of the daylight factor approach is that it is based on the standard overcast

sky conditions, whereas for real sky condition where the non-overcast sky conditions exists, the

metric is not appropriate one to measure quantity of daylight for variable sky conditions.

Additionally it does not account for the illumination from the sun. It does not account for the


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seasonal variation of the climate, time of the day it is measured, building orientation and building

location. Also it cannot indicate the probability of glare problem (Reinhart F.C et.al., 2006)

However, since it is based on the CIE standard overcast sky condition, which is the worst sky

condition scenario, the practitioner prefer to quantify the daylight level using daylight factor

metric. Eventhough there is common understanding of the fact that it is not appropriate method

due to its drawback; still there is no single alternatives indicator for replacement of daylight

factor.

4.2.2 Daylight coefficient

The daylight coefficient (DC) is defines as the sensitivity of the internal illuminance to the

changes in the brightness of the sky element (Tregenza P.R. and Waters M. I., 1983). It has been

found that the ratio of internal to external luminance varies greatly under real skies, since the

luminance of real sky is always changing. The concept of the daylight coefficient has been

proposed to consider the variance of the luminance intensity of real sky. The daylight coefficient

divides the sky into number of discrete elements (145 patches) and then considers illumination

due to each element at the reference point in the space. In daylight coefficient, it requires

expensive simulation to account for the indirect component of both sky and sun, and finer

discretisation to account for the direct component of sun for accuracy. In compare to daylight

factor, an advantage of the daylight coefficient is that it is possible to find out the illumination

levels at a reference point for a wide variety of luminance from the skies and the illuminance due

to sunlight (Kota S. and. Haberl S.J., 1982).


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4.2.3 Daylight Autonomy (DA)

The illumination level inside the space can only be accurately determined when luminance from

both sun and sky are considered across full year for that specific locale. It is due to the fact that

daylighting being dynamic in nature. Previously defined two metrics only address the static

nature of the daylight. The dynamic daylight performance metrics are based on time series of

illuminances or luminances within a building. These time series extend across the whole year

and it is based on external, annual solar radiation data for the building site. In compare to static

performance metrics, dynamic performance metrics consider the quantity and character of daily

and seasonal variations of daylight for a building site together with irregular meteorological

events (Reinhart F.C. et.al., 2006).It has been widely recognized that static performance metrics

required to be replaced by the dynamic performance metric which is based on absolute value

from luminous output of both sun and sky condition across a year derived from the standardised

climate files (Mardaljevic J. et. al., 2011). The daylight autonomy is one of the simplest climate

based dynamic performance metric.

Daylight Autonomy (DA) is a defined as the percentage of the year when a minimum

illuminance threshold is met by daylit alone (Mardaljevic J, 2012). The drawback of the daylight

autonomy in predicting the performance building is it fails to address the lower value below than

the threshold value which is necessary to analyse in occupant’s comfort prospect. Also the lower

value may also have potentiality to replace the electric lighting loads. Similarly, it does not


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consider the amount by which the threshold illuminance is exceeded at any instance, which gives

information regarding visual and thermal discomfort (Kota S. and. Haberl S.J., 1982).

4.2.4 Useful Daylight Illumination (UDI)

The Useful Daylight Illuminance (UDI), proposed by Nabil A and Mardaljevic J., 2004 is

another climate based dynamic daylight performance metric. The metric is based on useful

illumination level inside the building which considers human response to lighting levels to

perform tasks at the work plane. It defines the ranges from lower (100lx) to upper limit (3000 lx)

with absolute values which aid the designers to assess the daylight performance of the buildings.

In compare to daylight autonomy, the UDI also address the illuminance level lower than the

minimum lower limit, that gives the indication to the designers that necessary UDI is not met

The illumination levels lower than 100 lx is termed as UDI fell short (UDI-f) and in this case the

occupant’s tendency to use the additional artificial lighting is high. The illumination level

between 100lx to 300lx is termed as UDI supplementary (UDI-s), where individual occupant’s

necessity dominates the switching on/off of the additional artificial lighting. The illumination

level between 300 lx to 3000lx is termed as UDI autonomous (UDI-a) and in this case the

occupant’s will not prefer additional artificial lighting to perform tasks. The illumination level

above 3000lx is termed as UDI exceeded (UDI-e ) and it this case occupant’s comfort will

come in question. The occupants may prefer to completely to shade the daylight using blinds to

prevent glare perform tasks with artificial lights. Again some might prefer only to half shade the

openings and use additional artificial lightings.


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In comparison to daylight autonomy, the UDI performance gives the designer information

regarding visual and thermal discomfort, glare probability and unwanted solar gains .With the

use of useful daylight illumination metric ( UDI), designers have more flexibility in choosing

alternative design options to enhance the daylit environment in the building with more focus on

occupant’s satisfaction. Recently, Garcia-hansen V. et al. (2012) studied the daylighting

performance of the multi residential tower in compliance with ASHRAE and Green Star, it is

noted that climate based metrics have more potentiality to be used in conceptual design stage of

the design process.

4.3 Visual discomfort

The improper daylight design leads to unpleasant indoor environment for the occupants inside

the buildings. Excessive daylight results into visual discomfort as well as thermal discomfort to

the occupants. The visual discomfort to the occupants mainly arises from the glare from the

openings, the veil reflections and the reflection from the surfaces where the difference in varying

degree of darkness and brightness exists, Mainly the buildings with large glazed openings have

to be designed to avoid the discomfort from glare. According to CIBSE Code of Lighting, glare

is defined as “the condition of vision in which there is discomfort or a reduction in the ability to

see details or objects caused by an unsuitable or range of luminance or extreme contrast”.

The glare is divided into two types, disability glare and the discomfort glare (Osterhaus K.E.W,

2005). Disability glare is the effect of stray light in the eye which results into poor visibility and

visual performance. Discomfort glare is the glare that produces discomfort and is merely the

psychological condition than the physiological condition of disability glare. With disability glare


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immediate reduction in the work performance will be noticed whereas in the case of discomfort

glare immediate work performance will not be hampered but occupants might suffer from

headaches, blurry vision etc. due to discomfort.

Many methods have been developed so far to address the degree of discomfort glare related to

luminous position and the size of glare source. Most of the metrics are derived on the basis of

artificial light source rather than the daylight source i.e not from the direct sunlight. Still single

metric to adopt internationally for the prediction of discomfort glare from the daylight has not

been derived and the standard practical method for monitoring the process is not available

(Nazzal A.A. and Chutarat, A., 2000). It is difficult to correlate the daylight discomfort glare

using test chamber scenario with the real scenario, since in practical scenario the daylight is non-

uniform source. While in the test chamber scenario, the window act as an artificial source and the

daylight thus produced will be from uniform source. Also in the test chamber scenario, the

psychological factors will influence the visual contents of the field of view of the occupants. The

proper daylight design inside the building require to consider sky conditions, lighting intensity

and distribution, colours and radiant energy which vary over a time in a given day (Nazzal A.A.

and Chutarat, A., 2000).

The Hopkinson and Bradley (1926) proposed British glare index, similarly CIE glare index is

prosed by CIE(1993). Sorensen developed the unified glare rating (UGR) which was adapted by

the CIE in 1995 in its publication on Discomfort Glare in Interior Lighting (Osterhaus E. K.,

2005). The glare indices used to predict discomfort glare from artificial lighting source cannot be

used in daylight situation. It is because the size of the openings exceeds the solid angles of 0.01

steradian which is maximum limitation for the solid angles used in assessment of discomfort


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glare ( for example: UGR) for artificial light source. With large solid angles, there is a

probability that the potential glare source may cover large part of visual field of the occupants.

Therefor it increases the adaptation of the eye and reduces the potential glare sensation and

contrast effect. Whereas in the case of artificial lighting conditions, the occupant’s adaptation

level is independent of small glare source.

The Hopkinson (1970) proposed the daylight glare index known as ‘Cornell formula’ based on

large uniform glare source to assess discomfort glare from daylight source. The Cornell formula

takes into consideration of the source illumination and the background illumination. The cornell

formula is modified to take into consideration of window luminance by Chauvel. However the

formula is based on the uniform glare source, it doesn't consider non uniform glare sources. The

visible luminance from the openings cannot be considered as uniform daylight source due to its

dynamic nature. Both Hopkinson and Chauvel did not presented the actual monitoring process of

parameters in practice. In the New Daylight Glare Index (DGIN) proposed by Nazzal A.A.,

(2000) for the assessment of the daylight system performance, the method of determination of

sources of luminance and solid angles is different compare to Cornell Formula. In this method

the effect of observation position (position of the measuring equipment) instead of solid angles

and the adaptation luminance instead of background luminance has been used. It has been

suggested that New Daylight Glare Index (DGIN) predicts sensible discomfort glare sensation.

Similarly, to develop new metric to quantify discomfort glare, Wienold J. and Christofferson J.

(2006), proposed new metric daylight glare probability which is based on vertical eye

illumination, glare source luminance, solid angle and its position index. In this research, the

impact of luminance distribution on glare is analysed using CCD camera based luminance


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mapping technology. The study strongly provided the practical relationship of measured

daylighting conditions with the user response. However the experiment was based in laboratory,

thus more studies are required to understand the difference of laboratory and real daylight

conditions. In addition to that, long term studies required to understand the effects seasonal

variability of the climate on the glare perception of occupants (Painter B. et.al., 2010).

Furthermore, Painter B. et. al., (2010) relates the physical daylighting conditions data using

luminous maps derived from high dynamic range (HDR) images with the user perception of

glare at their work station in order to get both quantitative and qualitative measures of

daylighting conditions. It was found in the research the current available metrics are not suitable

for assessing discomfort glare from the daylight sources.

Even though there are many metrics for the discomfort glare, till today there is no effective

method to predict discomfort glare in the open plan offices. In the research performed by Hirning

B.M. et.al, (2012) on green buildings in Brisbane found that almost 50 percent of the occupant

experienced discomfort from both artificial and daylight sources at their workplace. In this

research, post occupancy evaluation (POE) survey is used to assess discomfort glare and

luminance maps were extracted from high dynamic range (HDR) images to capture luminous

environment of the occupants. The study shows that there is no physical evidence between the

different glare metric for daylight within open plan spaces, including the daylight glare

probability (DGP) (Hirning B. M. et,al.2012). Also, there is a knowledge gap in the discomfort

glare studies about how the age and the eyesight factor affects the perception of discomfort glare

by the occupants. In addition to that ,to achieve the maximum environment benefits for the


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occupants from daylight, more studies required to be done on specific building types and uses of

the day-lit space.

In practice, buildings can have maximum benefits from daylighting, provided it is prioritized at

the conceptual stage of design process. Two methods are commonly used to prevent glare from

openings, use of blinds and light selves from the daylight sources. Besides that, the proper

planning of indoor artificial light also requires to be considered to provide pleasant environment

and improve energy saving potentials. With the inclusion of daylighting in building design, there

is high probability of improving overall energy performance of the buildings.

5.0 Building Energy simulation tools

The following dynamic building simulation tools are available for the assessment of the building

performance.

5.1 EnergyPlus

It is an integrated modular simulation engine which predicts the whole building energy

performance. The program is originated from the BLAST and DOE-2.1E with more advance

features compare to its parent programs. The important feature of the EnergyPlus is the

simultaneous integration of loads and the systems simulation for the accurate predictions of

temperatures and thermal comfort. EnergyPlus program has got three basic components, a

simulation manager, a heat and mass balance simulation module and a building systems

simulation module. The simulation manager controls the interaction between all simulation

processes. The heat and mass balance module simulates the integrated building thermal loads and


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HVAC systems. The building system simulation modules handles the interaction between heat

and mass balance engine and various HVAC modules and loops , boilers, chillers, fans, pumps

etc.(US DOE, 2012).

In the program, the loads are calculated by heat and mass balance engine at a user defined time

step, and feed into the building system simulation module at the same time step. The building

system simulation module then calculates the response of the plant and electrical systems at

variable time steps down to seconds. If the loads are not met by the systems, it will be reflected

on the next adjusted temperatures ((US DOE, EnergyPlus Simulation Software, 2013).

In the heat balance calculation, simultaneous calculation of radiation and convection processes in

each time step is done. The feature of automatic calculation of wind pressure coefficient is

partially implemented and the feature for calculation of natural ventilation by pressure or

buoyancy driven is available. Also the features for calculation of solar gain, day lighting and

human comfort model based on activity, inside drybulb, humidity and radiation is available.

EnergyPlus is a stand-alone simulation program and requires a graphical interface program.

EnergyPlus reads input and writes output as text files. It is an open source program and due to its

modular structure many programs can be integrated with EnergyPlus (US DOE, EnergyPlus

Simulation Software, 2013). Among all other graphical interface program, DesignBuilder is one

of the graphical interface programs which can run the latest EnergyPlus simulations. The features

available in the Design Builder program are building energy simulation, visualisation, CO2

emissions, solar shading, natural ventilation, daylighting, comfort studies, CFD, HVAC

simulation, building energy code compliance checking, hourly weather data, heating and cooling

equipment sizing (USDOE, 2012) .


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The EnergyPlus can perform analysis of simple energy and demand charges evaluation as well as

complex energy tariffs including fixed charges and block charges etc. It also has the features to

schedule the variation in all rate components and user selectable billing dates are also available

(Crwaley D. B et.al., 2006).

Although EnergyPlus has many excellent features, it also has some shortcomings. For example,

there is no easy way to construct a complex system other than to use the template systems

provided by the software. Sometimes, we have to add related modules in a text IDF file, which is

time-consuming and difficult due to the complex interconnections of various components and

loops. In addition, a complex EnergyPlus model with many thermal zones normally requires a

much longer simulation time (Clarke J., 1998)

5.2 ESP-r

The Environmental Systems Performance research (ESP-r) dynamic simulation tool is an

integrated energy modelling tool for the simulation of the thermal, visual and acoustic

performance of buildings the assessment of the energy use and gaseous emissions associated

with the environmental control systems and constructional materials (Hien W.N. et. al, 2000).

The program has been developed by the Joe Clarke in 1974 and is available as open source

program under GNU license for research from the University of Strathclyde.

It is based on a finite-volume (or finite difference) discretisation approach specified in terms of

geometry, construction, operation and leakage distribution etc. and it transforms into a set of

conservation equation of energy, mass and power etc. (USDOE, 2012) The architecture of the

ESP-r comprises a central Project Manager (PM) with support databases, a simulator and


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performance assessment and reporting tools. The PM's co-ordinate design process by receiving

and giving information from the data model to the support applications. ESP-r's results analysis

modules are used to view the simulation results and undertake various analyses (Energy System

Research Unit, 2012).

However The program is not user friendly and is hard to operate without proper training

(Crwaley D. B et.al., 2006). In the ESP-r, for the user defined coefficients, constants and

equation or correlation, require the domain expertise. Also for the hybrid, natural and mechanical

ventilation analysis, inputs are very difficult to obtain. For the economic evaluation purpose,

only simple energy and demand charges analysis can be obtained from this program.

5.3 TRNSYS

TRNSYS is a graphical program with a modular structure for transient systems simulations

based on component approach. It consists of two modules. The first is an engine, kernel which

processes the input file, solves the system, determines convergence, and plots system variables.

The kernel also provides utilities that determine thermophysical properties, invert matrices,

perform linear regressions, and interpolate external data files. The second module is library of

components for the component of the systems. The standard library includes approximately 150

models ranging from pumps to multizone buildings, wind turbines to electrolyzers, weather data

processors to economics routines, and basic HVAC equipment etc. Due to the modular structure

of TRNSYS, it also facilitates the addition of mathematical models not included in the standard

TRNSYS library to the program (University of Wisconsin/TRNSYS, 2013).


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The main inputs for the simulation are the building’s construction elements, the building’s

geometric elements, meteorological parameters such as ambient temperature, relative humidity,

diffuse and global radiation, wind speed and direction, the building’s internal gains, the

building’s infiltration, ventilation, etc. The main outputs of TRNSYS simulation are the indoor

air temperature at each building’s thermal zone and the heating and cooling load

(Assimakopoulos M.N. et. al, 2007). TRNSYS is based on transfer function method (TFM).In

order to predict solar irradiance on tilted surfaces, it use the Perez model along with three solar

irradiance components, direct normal, global horizontal and diffuse (Buratti C. et.al., 2013). The

TRNSYS simulation is used for HVAC analysis and sizing, multi-zone airflow analyses, electric

power simulation, solar design, building thermal performance, analysis of control schemes, etc.

5.4 DIVA for RHINO

DIVA which stands for design, iterate, validate and adapt is an optimised environmental analysis

plug-in for Rhinoceros 3d Nurbs modelling programme (Jakubiec A.J. and Reinhart F.C,2011).

The plug-in allows to perform various environmental performance evaluations in the building

such as radiation maps, climate based daylighting metrics, annual and individual time step glare

analysis and single zone energy and load calculation etc. In radiance maps, it can generate

climate specific annual surface irradiation images to calculate annual irradiation at node location.

By comparing summer and winter period irradiation results, the optimization of shading devices

can be achieved to maximize winter gain and minimizing summer exposures. it uses radiance

module GenCumulative sky to create a continuous cumulative sky radiance distribution and uses

radiance backward ray tracing method for simulation. It can also analyze the daylight glare

probability (DGP) for visual comfort evaluation by considering brightness of view, position of


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glare source and visual contrast. The simulation uses evalglarev1.0 to calculate daylight glare

probability (DGP) from the luminance maps based on total vertical eye illuminance and contrast.

Similarly, annual glare calculation is performed for each hour in the year by using annual

DAYSIM prediction to calculate eye illuminance. The dynamic climate based daylight metrics

such as daylight autonomy (DA), continuous daylight autonomy, daylight availability, useful

daylight illuminance, (UDI) is performed for annual calculation. It also allows the modelling of

single-zone thermal models using EnergyPlus engine. These models are automatically linked,

through the software interface, with detailed lighting and shading schedules generated by

DIVA/DAYSIM. Therefore, the effect of different daylighting and controls strategies on the

energy consumption of day-lit space can be analysed (DIVA for RHINO, 2013).

6.0 Building Operation and Management systems

The key question currently facing by industry is how to tackle the gap between design

performance and post occupancy performance of the buildings. Furthermore, since the existing

building are dominant compare to the new built buildings, aspect of post occupancy performance

in respect to potential energy savings, occupant's comfort, and proper operation and management

of buildings are critical issues which require proper attention. To improve the building

performance. existing control systems, energy usage and proper operation and management of

control systems considering passive measures than active measures technologies should be

implemented.

The operation and management of buildings are two different issues, but require to be considered

simultaneously to improve performance. The reason being, there might be existing buildings

http://diva4rhino.com/user-guide/rhino/lighting-controls

http://diva4rhino.com/user-guide/rhino/advanced-shading


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which are not designed to meet the performance, but using less energy due to proper

management of the control systems and equipments. While some buildings designed to meet the

performance but due to lack of proper operations for example faulty operational issues leads to

high energy consumption.

The design process of the building goes through, conceptual design stage, detailed design stage,

construction stage and commissioning stage. The practitioner always try to match the energy

usage by the building after commissioning to the design intent. However, in practice it does not

occurs due to many problems, for example due to assumptions made during design process,

faulty equipments, control system not operating as designed, etc. Hence it is better to compare

benchmark building that has good building performance with the similar types of buildings with

similar activities and uses to evaluate the performance. It this way, the reason behind the

buildings not performing to the design intent and questionable operation system of building

control system will be easily distinguished. Basically, the quantification of potential energy

savings from the improvement of building operation and control system will be possible, (Treado

S. and Chen Y, 2013).

6.1 Control Strategies and systems for building performance improvement

The control systems for the buildings should be design to optimize energy usage, smooth

operation of system's components and proper management of buildings with inclusion of

occupant comfort and the cost effectiveness of the whole system. Keeping all these things in

mind, the control system of the building is directly related to the performance of buildings. For

example if a building is designed efficiently but the operation of control systems is not


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functioning properly then the building performance will not be up to the mark. On the other

hand, if the operation of the control system is efficient but the building is not designed properly

to meet the energy efficiency benchmark, the buildings will not also perform well. Hence the

control system of building is a primary unit that determines overall performance of the whole

building system.

The basic control system consists of sensor which measures variable inputs such as temperatures,

airflow etc., controller which process the variables according to the defined logic and controlled

device which will transmit the output variables. The control system should be designed and

operated to suit the specific building type and purposes. In general, conventional control system,

computational intelligent system and agent based intelligent system are used in the building

design ( Dounis I.A. and Caraiscos C.,2008) .

6.2 Post occupancy evaluation

The post occupation evaluation (POE) is the structured process of evaluating the performance of

a building after it has been built and occupied for certain period. The process undergoes through

systematic data collection, analysis and comparison with set of performance criteria. The POE

helps the designer to understand regarding the real in-use performance of the design and also

helps to point out about differences if exists in the predicted and actual performance of buildings

(Preiser W. et.al., 1987).


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The post occupancy evaluation can be scoped into three interlinked strands (Cooper I., 2010).

Feedback: as a management aid method for measuring building performance, mostly in

terms of organizational productivity and efficiency.

Feed-forward: as a design aid method for improving building procurement using the data

gathered as a feedback from the users to the design team during briefings.

Benchmarking: as an measuring aid to measure progress from sustainable production and

consumption of the built environment.

POE can be performed in various approaches by gathering technological data to socio-

physiological interests where subjective parameters are used to assess the building

performance. Hence there are many POE techniques available worldwide (Menezes A. C.

et.al., 2012). The various available POE method in the UK are listed below in Table.

Method Format/techniques used

Focus

De Montfort method - Forum

- walkthrough

Broadly covers the

process review and

functional

performance.

CIC Design Quality Indicators -Questionnaire Functionality/

building

quality/impact

Overall Liking Score -Questionnaire

(hardcopy/web- based)

Occupant survey;

sectors incl.

educational


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-7 point scale Diagnostic tool

PROBE -Questionnaire/-Focus

group/-visual surveys

-Energy assessment

-Evaluation of

performance of system

User satisfaction/

occupant survey

Systems

performance

Benchmark

developed

BUS Occupant Survey -Building walk-through

-Questionnaire backed

up by focus groups

Occupant

satisfaction

productivity

Energy Assessment and Reporting Methodology -Energy use survey

-Data collection e.g.

from energy bills

Energy use and

potential savings

Learning from experience -Facilitated group

discussions or

interviews

Team learning

from its experience

Table 6.2.1: Overview of existing POE approaches in UK

Source: Reproduced from Riley M. et.al., 2012

The energy assessment reporting methodology (EARM), is widely used for the performance

assessment in the UK, It is published as a technical memorandum (CIBSE TM22) by CIBSE.

It describes the method for assessing energy performance of an occupied building based on

metered energy use and it also includes a software implementation of the method. the figure

below shows the breakdown of the energy use by the buildings.


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Figure 6.2.1: TM22 'Energy Tree Diagram' illustrating the breakdown of energy use

Source: Reproduced from Menezes A. C. et.al., 2012

The existence of the gap in the predicted performance and actual performance of the buildings is

due to many critical factors such as poor design assumption, inbuilt algorithms of the modelling

tool not giving the accurate predictions, inadequate controls and management systems of the

building which leads to inefficient operation of building services, occupant behaviour and the

built quality of the construction. As a result, the predicted performance tends to be relatively low

while in reality the actual performance is very high (Menezes A. C. et.al., 2012).

7.0 Conclusions

To begin with the research, in detail literature review relevant to the research has been

performed. The importance of climate variables in the design process and the type of climate

data file appropriate for different dynamic simulation tools such as DesignBuilder, EnergyPlus,


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TRNSYS and DIVA for RHINO has been reviewed. The suitability of passive ventilation for the

non-domestic buildings for the oceanic temperate climate such as the UK, has been explored. It

is noted that the buoyancy driven passive ventilation can be adopted for the UK temperate in the

non - domestic buildings. The passive stack ventilation is dependent on stack height, the

difference between the average temperature of the stack and the outside temperature and

effective opening areas. The position of the stack openings improves the internal thermal

environment and the size of the stack openings affects the temperature distribution in the space.

To improve the performance of building, the wind catchers and solar pond is reviewed as an

passive technologies for energy efficient non-domestic buildings. The actual energy

consumption, thermal comfort, useful daylight illuminance (UDI), daylight autonomy (DA) and

visual discomfort are considered as an key performance indicators to measure the performance of

the building. For the passive ventilated buildings, adaptive approach proposed by de Dear and

Brager is more suitable compare to the fanger's model of heat balance method for measuring

thermal comfort indices, PMV and PPD, since the occupants are capable of adjusting to its

required thermal environment in passive ventilated buildings. However, it is noted that in many

moderate climate countries, both PMV/PPD model and the adaptive models are giving similar

outcomes, therefore many scientific and practice based studies are required to establish the

approach appropriate to predict the occupants preference of their thermal environment. In the

evaluation of visual discomfort, it is found that more evidence based research is required to

predict the discomfort glare in open planned offices. There is a research gap on the knowledge

on the discomfort glare studies about the effect of age and eyesight factors in the perception of

discomfort glare by the occupants.


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7.1 Research progress and future work

In the beginning of the research work, the detail literature review on the passive ventilation,

daylighting, thermal comfort and passive technologies has been done. The problem statement,

research aim and objectives has been set out. The experimental data has been collected from the

Lecture theatre of the east-park Loughborough University design school for the summer period

of a week from 21st June to 28 th June. The winter period experimental data is required to log

for a week period for the same room. Similarly, data for different occupied spaces of the building

depending on its activity and uses is also required to record in next stage. The set of

questionnaire required to develop for the data collection for next stage. In similar way, the

experimental data from the other case-study buildings is required to collect for the

simulations.The following table gives the work schedule for the continuation of the project in

brief.


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Table7.1.1: work plan schedule.

After the completion of the data collection, in next phase the work will proceed for simulations

and evaluation of the given results.


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The Integration of Occupant Comfort, Energy and Daylighting in the Non-Domestic Buildings

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