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

3

N

S

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 [-

]

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 [-

]

SHGC [-]

Static products

t v

is

g-value

COMFORT

Desired ouput (comfort, energy

consumption)

Occupation

Input (External

Climate)

Façade properties at time t

Time


Luzern, Nov 2014

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



Luzern, Nov 2014


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


Luzern, Nov 2014

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)


Luzern, Nov 2014

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

towards the future generation of adaptive glazing facades

Engineering

efn adaptive faade conference

adaptive facades

glazing adaptive technologies

optimal adaptive glazing

glazing technologies

design of bs tool method

performance tool

orientations fabio favoino