Towards the Future Generation of Adaptive Glazing Facades

Towards the future generation of

adaptive glazing facades

Adaptive Façade Conference 2014

European Façade Network Conference – 28th November 2014

PhD student: Fabio Favoino

Supervisor: Dr Mauro Overend

Contents

2 Fabio Favoino, EFN Adaptive Façade Conference,

Luzern, Nov 2014

1. State-Of-The-Art of adaptive glazing

2. Future generation of adaptive glazing

3. Description of the method and the tool designed

4. Energy saving potentials

5. Optimal thermo-optical properties

SOTA Method -Tool Objectives Results

Energy in office buildings: building envelope design

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E W

Helsinki, FI HDD 3902 London, UK HDD 1828 Rome, IT HDD 1415

Ref: Jin et al. (2014), Sensitivity of façade performance to early-stage design variables, Energy and Buildings 77, 457-466

Transparent Building Envelope properties [U-value, g-value, Tvis] have the highest

impact on energy consumption of a building in different climates (office buildings)

SOTA Method -Tool Objectives Case study

Fabio Favoino, EFN Adaptive Façade Conference,

Luzern, Nov 2014

Glazing Technologies: State-of-the-art

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Glazing Adaptive Technologies: State-of-the-art

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Glazing Adaptive Technologies: the future?

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Define the characteristics of future generation adaptive glazing :

- Energy saving potential (overall and break up in heating, cooling and lighting, climate and

orientation)

- Thermo-optical properties (modulation ranges, relationships, control)

Definition of a method and design of BS tool

- Method to define an optimal adaptive glazing based on its performance

- Tool overcoming the limitations of current energy simulation software (variable

properties, receding horizon control, state update and integration with lighting system);

- Application of the method to a representative case study (cellular office in different

climates and orientations)


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Inverse model for adaptive facades

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T vi

s [-

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SHGC [-]

Static products

t v

is

g-value

COMFORT

Desired ouput (comfort, energy

consumption)

Occupation

Input (External

Climate)

Façade properties at time t

Time


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Inverse model for adaptive facades

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• Ye et al. (2012): theoretically derived for two climate extremes (glazing)

• Zeng et al. (2012): SQL applied to RC model – modelling limitations and optimisation of just one

property at the time (themal mass)

• Kasinalis et al. (2014): long term adaptiveness only (general, any property)

• Erikson (thermal mass) (2013), De Forest (IR glazing properties) (2013), Martinez (opaque

envelope properties) and Goia (WWR) (2014): simulation of static material properties and post-

processing to approximate adaptive material

• Loonen et al. (2014): approach of Kasinalis extended to shorter adaptiveness (general, any property).

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T vi

s [-

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SHGC [-]

Static products

t v

is

g-value

COMFORT

Desired ouput (comfort, energy

consumption)

Occupation

Input (External

Climate)

Façade properties at time t

Time


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Architecture of the tool

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What can we simulate?

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Building envelope (Active/Passive) and RES:

1.Construction state

2.Surface Heat Transfer Coefficient

3.Material Surface properties

4.Schedules

5.Airflow Network Opening

6. RES Generation and Storage


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What can we simulate?

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Building envelope (Active/Passive) and RES:

1.Construction state

2.Surface Heat Transfer Coefficient

3.Material Surface properties

4.Schedules

5.Airflow Network Opening

6. RES Generation and Storage

Objective functions:

a) Total Primary Energy

b) Net Primary Energy (N-ZEB)

c) Thermal Comfort

d) Visual Comfort

e) IEQ


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Potential applications

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Concept design

System design

and control

Material design

and optimisation


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U-value [0.20-5.14]

1. g-value [0.00-0.84]

Tvis [0.00-0.98]

g-value [-]

Case study: future generation adaptive glazing

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Aim: pinpoint ideal adaptive glazing properties in order to evaluate the energy saving

potential of future generation adaptive glazing facades

Tvis

[-]

U-value [W/m2K]


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Optimal adaptive glazing - Variable properties limits

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1 2 3 4

OUT IN

1 2 1 2 3 4 5 6

OUT OUT IN IN

DGU SGU TGU

1. Possible technologies

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2. Variable surface properties



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1 2 3 4

OUT IN

1 2 1 2 3 4 5 6

OUT OUT IN IN

DGU SGU TGU

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2. Variable surface properties

3. Variable surface and cavity properties

g-value [-]

Uglazing [W/m2K]

tvis = g-value/0.423

tvis [-]



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1 2 3 4

OUT IN

1 2 1 2 3 4 5 6

OUT OUT IN IN

DGU SGU TGU


Representative case study and optimisation problem

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3 m 5 m

3.5 m

WWR = 40% T heating = 20° C [12 ° C setback] T cooling = 26° C [40 ° C setback] Ventilation = 1.4 l/sm2

Equipment PD = 13.46 W/m2 (Office profile) Lighting PD = 12.00 W/m2 (Office profile) Lighting control = 5 step dimming [500 lux on working plane threshold]

h Heating = 0.85 SEER Cooling = 3.50 Fuel factors according to national context

Typical Office Room

Climate and Orientation

Helsinki London Rome

Environmental conditions and HVAC

N

E S

W

Optimisation:

Global (PSO) +

Local (GPSHJ)

algorithm


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Results – Climate comparison (South)

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R Y M D R Y M D R Y M D

5% 11%

21%

12% 22%

36% 34% 47%

57%

Helsinki London Rome



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Results – Sub-hourly ideal glazing adaptive properties

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Dimension of the problem

50 possible states ^ 40

variables = 50^40 possible

states

Climate – Rome South Oriented – 19 Jul Wed to 22 Jul Sat

Control

Energy

Performance

Up to 60% energy saving

compared to reference, 45%

to best static and 15%

compared to daily adaptive for

the same days

3.5 hrs * 4days = 14 hrs =

25000 evaluations* 4days

Issues

Complexity of solution

Speed


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Results – Ideal glazing adaptive properties

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Y - Ideal static glazing

properties

M – Monthly ideal adaptive

glazing properties

D – Daily ideal adaptive

glazing properties


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Results – Ideal glazing adaptive properties

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Cumulative time frequency

Ideal performance (energy saved)

compared to optimized static

Which thermo-optical properties values are the most frequent?

Which thermo-optical properties values are the most effective?


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Results – Ideal glazing adaptive properties - Orientation

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Cumulative time frequency Ideal performance (energy saved)

compared to optimized static


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Tvis Rome Climate Tvis/g-value Rome Climate

Conclusions: Future Adaptive glazing

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• Seasonal adaptive glazing: in general 10-12% energy saving for ESW orientations for all climates

compared to best static performance (more for hotter climates);

• Daily adaptive glazing: additional 10-15% energy saving for NEWS for all climates compared to best

static performance (more for colder climates);

• Hourly adaptive glazing: additional 15% compared to daily adaptive (40% compared to best static) for

one scenario (week in July in London, South Oriented);

• Cooling demand nearly eliminated (80-97% less), the higher the adaptiveness the lower the energy for

cooling and heating;

• Modulation of U-value important only in hotter climates

• Increasing the modulation range of g-value e Tvis important in colder climates

• NIR and visible spectrum need to be independently tunable (achievable with a combination of

technologies or with new synthetyzed material)

• Same technology could be used for different climates and orientations

• Complexity of the solution could be reduced to fewer descrete points (less complex technological

solution, easier to control)


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Objective: Design a tool that can be used to assist the design and the

optimisation of new adaptive façade concepts and technologies:

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Adaptive

Façade

Opt tool

Rely on validated Energy

Simulation Tool

General formulation of adaptive

building envelope (timescale,

active/passive, physical properties)

Integrated with optimisation algorithms

for design and control optimisation

Multi-domain: considers all

physical domain involved

(energy, comfort)

Modular and User

friendly

Computationally efficient

and scalable

Accurate and reliable

modelling of adaptiveness

Method for design optimisation for

adaptive building envelope

Conclusions: Tool


Luzern, Nov 2014

Towards the future generation of adaptive

glazing facades:

any case study???

email: [email protected]

Fabio Favoino, Mauro Overend

Engineering

Towards the Future Generation of Adaptive Glazing Facades