towards a correct prediction of real energy savings in dwellings

Energy renovation and occupant behaviour:

Energy renovation and occupant behaviour:

towards a correct prediction of real energy savings in dwellings

Ghent Univesity

Faculty of civil engineering

Department of Architecture & Urban planning

Research Group on Building Physics, Construction and Climate Control

[planning]

9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8

1 study of literature 2 database formatting 3 extension of database

a field-measurements

2010 2011 2012 2013 2014

2009 Project research

2010-2014 PHD research

b external data-collecting 4 data-analyzing & confrontation 5 scenario & sensitivity analysis 6 policy measures & tools

a calculation method b energy-saving measures - PHD-report

[structure]

1. Background 2. Focus & goals 2. Focus & goals

3. Steps & methodology

4. Selection 1:Zoom-in/example

1. Field data sets

2. Field data sets vs. theory

5. Selection 2: challenges

background

GOAL

Quantified reduction of CO2-production

ESTIMATIONS & PROGNOSES:

Pragmatic tools

> reducing energy consumption in buildings vs.

> reducing heating consumption in dwellings a/ new builds

b/ renovation

LIMITATIONS:

available means & time

> efficiency

vs.

Specialised, detailed tools

[dynamic, 3D, CFD,…]

---

Qualitative (comparative) indicators

[K, E,…]

vs.

Quantitative values

[kWh, €, ton CO2, …]

> efficiency

? decision making ?

? planning ?

? motivating actors ?

…

Simplified, generalized, theoretical models

vs.

Complex, variable reality

EPB-software

Focus & goals

quantitative values

1) Diagnosing the 3) Improve

Simplified, generalized, theoretical models

vs.

Complex, variable reality

1) Diagnosing the most important

factors of divergence and

correlations

(focusing on interaction between

user behaviour and local building types, construction methods

3) Improve accuracy of predictions, helping more substantiated decision making

a) Individual house- hold basis (tools

& correlations)

Complex, variable reality

construction methods and

standards)

2) Taking them into account in the models

in a pragmatic way(!)

& correlations) b) Larger scale

policy making (+statistical extrapolations)

Steps & methodology

1. Study of literature: building parameters, user behavior, simulation models

2. Available field data sets: on energy consumption, energy-savings and indoor climate

indoor climate

a) Mapping and formatting in a congruous way b) First evaluative analysis

3. Extending the database: detailling & expansion (parameters and cases)

4. Correlations between the factors that determine the discrepancy between theory and practice

a) Confrontations between models and cases b) Confrontation with literature

c) Confrontation with surveys

5. Scenario and sensitivity analyses 5. Scenario and sensitivity analyses

6. Improvements to the simplified calculation methods:

accuracy/pragmatic (dynamic <> steady state <> …)

7. confrontation with current policy measures and tools to enhance

the energy efficiency of households

Field data sets

• Measurements

– Building envelope – Building envelope

• Transmission (flux-measurements, IR-thermography…)

• Ventilation/infiltration (blowerdoor, flow-box…)

– Building techniques

• Equipment efficiency and settings (heat-recovery...)

– Comfort & user behavior

• Indoor climate & air quality ( θ , RH, CO2)

– Energy consumption

• Gas/electricity/… bills/meters/…

Field data sets

• Surveys

– User behaviour – User behaviour

• presence

• controle

– User experience

• Level of (dis)satisfaction (comfort, cost,…), subjective/objective

– User input

• …

Field data sets

Example 1, building envelope:

• Flux-measurements on retrofit cavity wall insulation

thermal resistance Rm [m².K/W]

flux-analysis: _3.4-NA

Rm,av,0 Rm,av,0, result Rm,av, corr Rm,av, corr,result 0h

• Flux-measurements on retrofit cavity wall insulation

(TETRA-project: na-isolatie van bestaande spouwmuren)

1 1.5 2 2.5

U [W/(m².K)]

U-value: absolute values

before after

-5 0 5 10 15 20 25

21-11-08 0:00 22-11-08 0:00 23-11-08 0:00 24-11-08 0:00 25-11-08 0:00 26-11-08 0:00 27-11-08 0:00 28-11-08 0:00 29-11-08 0:00 30-11-08 0:00

temperature [°C] & heatflux [W/m²]

time [dd-mm-yy hh:mm]

θsi θse Δθs i-e q q*estim ate 0

0,5 1 1,5

thermal resistance Rm [m².K/W]

6h 12h 18h Rm,dyn90%

Rm,dyn

0 0.5 1

U [W/(m².K)]

temperature_1.6-NA1nat Relative humidity_1.6-NA1nat

Field data sets

Example 2, indoor climate:

• Temperature & relative humidity: user inside & climate outside

-5 0 5 10 15 20 25

e iL iK iC iSo iSk iSk

temperatuur θ [°C]

0 25 50 75 100

e iL iK iC iSo iSk iSk

relatieve vochtigheid φ [%]

Living room circulation parents _child1 _child2

outside

Temperatureθ[°C] Living room circulation parents _child1 _child2

outside

• Temperature & relative humidity: user inside & climate outside

• differences between rooms (geometrically)

• variations within rooms (time)

≠ EPB: 18°C (~user behaviour)

Living room kitchen circulation Sl-r_parents Sl-r_child1 Sl-r_child2

outside Living room kitchen circulation Sl-r_parents Sl-r_child1 Sl-r_child2

outside

Field data sets

Example 2, indoor climate:

• Temperature: day-cycle ~ user profile (& building)

• Temperature: day-cycle ~ user profile (& building)

10 15 20 25

gemiddelde temperatuur θ [°C]

EPB=18°C e

iL iK iC iSo iSk iSk -

Living room kitchen circulation Sl-r_parents Sl-r_child1 Sl-r_child2 buiten

Temperatureθ[°C]

variation 1 Indoor climate

~ day-cycle

(outdoor climate &

usage)

0 5

00:15 01:15 02:15 03:15 04:15 05:15 06:15 07:15 08:15 09:15 10:15 11:15 12:15 13:15 14:15 15:15 16:15 17:15 18:15 19:15 20:15 21:15 22:15 23:15

gemiddelde temperatuur θ [

- - -

Temperature

Field data sets

Example 2, indoor climate:

• Temperature: year-cycle ~ outdoor climate (& building)

• Temperature: year-cycle ~ outdoor climate (& building)

15 20 25 30

binnentemperatuur: dag-gemiddelde θi [°C]

θgem,i = θgem,e EPB=18°C gem_iL gem_iK gem_iC gem_iSo gem_iSk gem_iSk -

-

Living room kitchen circulation Sl-r_parents Sl-r_child1 Sl-r_child2

inside ai [°C]

Variation 2 Indoor climate

~ Outdoor

climate

5 10

-5 0 5 10 15

binnentemperatuur

dag-gemiddelde buitentemperatuur θe [°C]

- - -

Daily average outside temperatureθae [°C]

Dailyaverage inside temperatureθai [

Field data sets vs. theory

Example 3, energy consumption: Theory vs. reality

(1)

Degree-days

Heating + hot water

Degree- days use-changes/

renovations

(1)

Cumulative ~ time

real, cumulated cunsumption (uncorrected)

Cumulatedenergyconsumption[kWh]

Heating

Hot water

Cumulated

Field data sets vs. theory

Example 3, energy consumption: Theory vs. reality

(1)

Time [dd-mm-yyyy]

Heating + hot water

use-changes/ days renovations

(1)

Cumulative ~ time

real, cumulated cunsumption (uncorrected)

Cumulatedenergyconsumption[kWh] Time [

Heating

Hot water

Cumulated

Degree-days

Field data sets vs. theory

Example 3, energy consumption:

• Theory vs. reality Energy consumption heating & hot water

• Theory vs. reality

29.96

29.96 - 50.12 EPB

voor - na

werkelijk voor - na EPB

vs.

werkelijk

100 150 200

vloer [kWh/(m².j)]

heating & hot water

(per year, per m² floor surface)

besparing sww before - after

real consumption before - after EPB

vs.

real consumption

decrease hot water heating

136.44

86.32

71.70

57.50 11.00

11.00 - 14.20 50

E/Svloer

verwheating

Challenges (selection)

• Databases

39 old houses from past research (only indoor climate)

25 case-studies of renovation (before/after) : CWI 25 case-studies of renovation (before/after) : CWI

> 17 case-studies new builts : newly built, uniform houses 2009 (> 30)

5 case-studies passive-houses(heavy/masonry & light-weight/wood-frame)

future:

o extended monitoring + completed with LE & PH:

Social housing neighboorhood (renovation: CWI & glazing) [2010]

Newly built- & renovated-neighbourhood Kortrijk (LE & PH) [2010-2014]

new cases PH (incl. summer comfort)

…

o Enquêtes & statistical data-collecting and -analyses

– Mapping and formatting different databases,

with different data-sets and origin in a congruous way with different data-sets and origin in a congruous way – Balance between amount & precision

» limited number of detailed in-situ monitoring + bigger amount of limited data

» defining minimum boundary conditions and amount of collected data

» Isolated samples (delimited varying parameters) vs. representative sample (combinations)

Challenges (selection)

• Correlations

– Discerning correlations between – Discerning correlations between

direct measurements

(e.g. inside temperature spreadings <> simulation error, inside temperature spreadings <> insulation levels, building type,…)

– Finding correlations to pragmatic (in)direct parameters

(e.g. inside temperature spreadings <> heating (e.g. inside temperature spreadings <> heating system, control system, setpoint, user)

(necessary for pragmatic implementation in

models, both for individual case-analyses as for

statistical extrapolation on larger scale)

Challenges (selection)

• Correlations

inside-temperature user behaviour

building

characteristics

outdoor-temperature

Energy consumption theory vs. practice TECHNIQUES

Boundary conditions

Qualitative indicators ENVELOPE

Transmission

(Flux-measurements, IR- thermography,…)

Air-thightness

Qualitative indicators +

Quantitative estimations

(increasing comfort

<>

reducing energy consumption)