towards a design tool for assessing future overheating risks in … · 2018-03-28 · clare...
TRANSCRIPT
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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Overheating metric is the no. of hours >23.9°C
What might this building output look like? - house
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What might this building output look like? - school
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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
0
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0.3
0.4
0.5
0.6
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0.8
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Cor
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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 [email protected]