towards a design tool for assessing future overheating risks in … · 2018-03-28 · clare...

Towards a design tool for assessing future overheating risks in buildings

Prof Phil Banfill Urban Energy Research Group, Heriot-Watt University ARCC Conference, Oxford, April 2011

Contents

!! Project background and objectives !! Emulating climate data for use in building simulation !! Validation of our regression method !! A possible design tool !! What do designers / stakeholders think of it? !! Conclusions

Low Carbon Futures Objectives

!! How can dynamic building simulation use a probabilistic climate database?

!! How can this be used for designing adaptations to prevent buildings “failing” in the future climate?

!! How can all the above be incorporated into a method that is useful for industry for overheating analyses?

How are our buildings vulnerable to climate change?

!! Naturally ventilated non-domestic buildings? !! Will a building that doesn’t currently need mechanical

cooling have less future flexibility? !! What can the occupant do if the building overheats due to

climate change? Use portable or retrofit cooling units? !! Cooling systems becoming under-sized?

!! What happens when the plant margin decreases? !! In many cases current cooling plants are over-sized so is

this just a question of the system “merely” using more energy?

UKCP’09 - Probability

!! Translating the term “probability” into “risk” begins to draw parallels with existing building approaches, for example: !! What is the risk of structural degradation of masonry? !! What is the risk of investment not returning financially? !! What is the risk of a building overheating due to the effects

of a future climate? !! This final question can utilise the UKCP’09 database

!! Probabilistic climates must be converted to probabilistic internal temperature profiles

Climate data for use with building simulation

Obtaining climate descriptions

Weather Generator

Emission Scenario

Low Medium

High

Time Period Baseline

2030 2050 2080

Location Edinburgh

London

100 statistically equivalent hourly time series; 30 years in length

Obtaining climate descriptions Up to 100 statistically equivalent hourly time series;

30 years in length

Random sampling

100 climate years

Multiple building

simulation

Overheating risk analysis

Are the 100 years statistically representative?

Can this relationship be emulated from just one climate?

Obtaining climate descriptions

!! WG produces 3000 climates for each scenario. !! Algorithm based on statistical methods : A year is selected at random from each of the 100 time

series to generate a representative sample of size 100

Results analysis

!! Can hourly simulation output be predicted through input-output regression techniques?

!! If so, then a large number of climates can be put through a regression equation rather than the simulation engine

!! This would be suitable for a probabilistic climate database where thousands of “climates” can be generated

Principal Component Analysis “PCA”

!! Statistical method for high dimensional data analysis

!! Transform large number

of possibly correlated variables (say, x1 and x2) into small number of uncorrelated variables (Y1)

X1 and X2 Y1 = aX1 + bX2

PCA for present study

!! Aim: Predict hourly internal temperatures based on climate data

!! First simplify weather dataset: 72 hours of weather dataset corresponding to each of

the 7 climate variables, i.e. 72 ! 7 = 504. !! PCA accounts for 95% of total variation: Reduces input climate dataset of dimension 504 to a

much lower dimension of 33.

PCA for present study

bedroom temperature principal components

regression coefficients

Case study building 1

!! Filled cavity-wall detached house !! Defined from Tarbase project !! Three behaviour schedules

!! NO ADAPTATION: No reaction from user to overheating !! ADAPTATION 1: Window opened if bedroom temperature

exceeds 23.9!C at night, with air-change modelled as a ventilation network

!! ADAPTATION 2: Reduced internal heat gains and external shading

Case study building 1

Detached house, built to 2002 Building Regs

N

Ground First

Case study building 2

!! Primary school, 2000 build !! Defined from Tarbase project !! Four behaviour schedules

!! NO ADAPTATION: No reaction from users to overheating !! ADAPTATION 1: Increased ventilation rate from 8 to 12 l/

s/person !! ADAPTATION 2: Reduced internal heat gains by using

more efficient lighting and appliances !! ADAPTATION 3: External shading to reduce solar gain

Case study building 2

Primary school, 2000 build

21m

40m

7.5m

16m

8m 8m

6m

7.5m

3m

6m

Teaching spaceStaffWCStorageCirculation area

Total floor area: 840m2

Teaching: 480m2

Storage: 90m2

Staff/admin: 64m2

Toilets: 36m2

Circulation: 170m2

Age: 2000 construction

N

Case study simulations

!! Simulated in ESP-r building simulation software !! Run for 20 climate scenarios across all

adaptations !! Baseline, 2030, 2050, 2080 !! Low, Medium and High emissions !! Edinburgh and London

!! Each scenario described by 100 climates "!6000 sets of outputs

Validation of method

Simulation vs Regression

!! Objective is to emulate simulation engine !! NOT to match with empirical data

!! If the regression output agrees with the simulation output then we have a possiby useful design tool

!! Large reductions in computational stage !! 100 climates simulated in ESPr ! 90mins !! 1 climate in ESPr, 99 through regression ~ 5mins

Comparison of hourly results

!! Case study house !! Regression values

are found to be within ±1oC of ESP-r values for:

!! No Adaptation: 92% !! Adaptation 1: 94% !! Adaptation 2: 93% of

data.

Do overheating metrics provide more suitable comparisons?

!! Hourly comparison is useful for more detailed analyses and for validating the basis of the approach

!! For a design tool, general overheating metrics might be more useful, e.g. !! % of hours above 28°C !! No. of hours above a threshold at certain times of the day

!! Note: If hourly comparison is acceptable then any overheating definition can be applied

Do overheating metrics provide more suitable comparisons?

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A possible design tool

Proposed methodology for using regression approach 1.! Building designer simulates individual building with

single (or a small number of) present-day climates 2.! “Regression tool” uses these results to calibrate

the regression coefficients 3.! Regression tool can now be used for thousands of

climates from UKCP’09 4.! Probabilistic output of the risk of that specific

building failing for a future climate scenario Could be used in early stage of design.

Probabilistic output - house

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What do designers think about it?

Questionnaire, interviews, focus groups

Aim: to elicit current industry practice and perceptions. Do designers think climate change is important? Do designers think overheating is a real risk? At what stage in the process can we influence the

design? How can we do it?

Climate is just one of several important criteria

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Perceived importance of climate change in building design

Vital Irrelevant

London non-domestic

London domestic

Edinburgh domestic

Edinburgh non-domestic

“While … temperatures will increase by a few degrees, Scotland will still be significantly cooler than the rest of the UK, and … summer overheating risks are likely to remain quite low”.

- Adapting to Climate Change, Consultation Scottish Govt report

Typical Practice Correlation: climate + other design variables

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Design Variables

Best Practice Correlation: climate + other design variables

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Stakeholders’ comments

John Easton, Archial

!! The project breaks new ground by synthesising simpler and more usable data for practical design simulations from complex future climate models.

!! Potential application in everyday domestic and non-domestic building design practice, for both new-build and refurbishment.

!! Could help achieve adaptation objectives.

Clare Wildfire, Mott MacDonald Fulcrum

!! The Low Carbon Futures tool will allow designers to move from deterministic to risk-based decision making without needing a major, time-consuming study to accompany each step.

!! The tool will be a vital resource to help track the implications for overheating in low and zero carbon buildings.

Conclusions

!! The probabilistic format of climate files can be exploited if used with care

!! Initial results suggest that the large dataset can be processed efficiently with building simulation

!! This leads to a possible design tool for overheating risk !! Overheating risk is under-recognised by some sectors

!! Future overheating analyses rely on current overheating being correctly defined

!! Likely to affect the adoption of any “new” form of climate analysis in building design

Thank you for listening

Prof Phil Banfill Urban Energy Research Group, Heriot-Watt University P.F.G.Banfill@hw.ac.uk