1. CORRELATING PROBABILISTIC CLIMATE PROJECTIONS WITH COOLING
DEMAND IN AN OFFICE BUILDING
Low Carbon Futures
Decision support for building adaptation in a low-carbon climate change future
Sandhya Patidar
Maxwell Institute for Mathematical Sciences
Urban Energy Research Group

2. Contents Low Carbon Futures project
Probabilistic Climate Projections (UKCP09)
Weather Generator
Climate data with Building simulation
Random Sampling Algorithm (RSA)
Principal Component Analysis (PCA)
Modelling Procedure
Model Validation
Summary
1) I am going to start with an introduction to low carbon future project where we are investigating various aspects of building adaptation in context of climate change.
2) Next, I will give a brief introduction to the Probabilistic Climate Projections mainly known as UKCP09.
3) In our project we are mainly using Weather generator tool, so it would be good to know about get some idea about it and what can be obtained through it.
4) Next I will talk about how we manage to integrate these complex probabilistic climate information with dynamic building simulations
5) We use a series of statistical techniques and I will try to provide an overview of our approach step by step which starts from data simplification that is done by Random Sampling Algorithm (RSA) and Principal Component Analysis (PCA) then modelling of climate data to estimate building comfor matrics (such as internal temperature, heating load, cooling loads etc.). Our considered a through model validation exercise so that has been included as well and finally summary.

3. Low Carbon Future Project
EPSRC funded through ARCC Coordination Network For details see -
Research questions:
How climate in the future will affect built environment?
Uses UK Climate Projections (UKCP09) –
Based on advanced technology
Probabilistic not Deterministic
How can Dynamic Building Simulation (DBS) be used with projections of this nature?
Most of the DBS software uses one climate at one time

4. Low Carbon Future Project
To identify risk of buildings “failing” in the future climate and to analyse possible adaptation measure to prevent them from failing?
Developing a simple to use system/model/tool –
Emulate DBS outputs quickly and efficiently for given complex climate information
Without carrying out practically infeasible 1000s of DBS
How will/can this be adopted by industry?
Getting feedback from building industries professionals
To tailor the tool for acceptability to design professionals
To generate outputs in form of simple colour coded tables and probabilistic graphs (with technical details)

5. Low Carbon Future Project
Project team –
Prof. Phil Banfill
Prof. Gavin Gibson
Dr. Gillian Menzie
Dr David Jenkins
Dr Sandhya Patidar
Dr Mehreen Gul
Publications –
10 journal papers, 8 conference papers (including 2 best paper awards), some input to CIBSE Guide A revision process and various other outputs (talks, reports, notes)
So this is all about Low Carbon Future Project and the project team.
A lots of work has been done the all the work has been documented in several journal and conference papers including some input to CIBSE Guide A.
Today I am going to present a part of the work carried out in LCF project. So starting from -

6. Less likely to be less than More likely to be less than
Probabilistic Climate Projections (UKCP’09)
Provides probabilistic projections of climate change for UK
(i.e. provides a range of possible climates with attached probabilities for any particular climate to be happening)
Increase in average temperature (°C)
10% probability
50% probability
90% probability
Less likely to be less than
More likely to be less than
Probabilistic
Deterministic
UKCIP02
UKCP09

7. Probabilistic Climate Projections (UKCP’09)
Time Scale
Annual, Seasonal and Monthly climate averages
(Downscale to Daily and Hourly averages)
Time Period
Seven overlapping future timelines
(“2020s – 2010 to 2039”, “2030s”, … up to “2080s” and one “baseline – 1961 to 1990)
Location
For up to 25 km grid squares
(Downscale to 5 km grid square)
Emission Scenario
“Low” “Medium”
“High”

8. Weather Generator (UKCP’09)
Emission Scenario
Low
Medium
High
Time Period
Baseline (1960 – 1990)
2020s ( )
2030s (2020 – 2049) …
2080s ( )
100 Statistically equivalent hourly time series
(Each time-series is 30 years in length)
Location
UK map 5 km grid squares

9. Weather Generator (UKCP’09)
Weather Generator Outputs
Clim1
Clim2
Clim30 …
Time-series 1
Clim1
Clim2
Clim30 …
Time-series 2
Clim1
Clim2
Clim30 …
Time-series 3
Clim1
Clim2
Clim30 …
Time-series I
Clim1
Clim2
Clim30 …
Time-series 100

10. Weather Generator (UKCP’09)
For each scenario WG produce 3000 climate years
Challenge:
Simulating 1000s of climate with DBS software requires a large amount of computational resources and time
Our Approach:
Simplify climate information (RSA and PCA)
Develop an efficient model that can effectively simulate
these100s of climate years in practically manageable way.

11. Random Sampling Algorithm (RSA)
Weather Generator Outputs
Well – Representative sample of 100 climate years
Clim1
Clim2
Clim30 …
Time-series 1
Clim1
Clim2
Clim30 …
Time-series 2
TS1 - Clim 2
TS2 - Clim 14
Clim1
Clim2
Clim30 …
Time-series 3
TS3 - Clim 27
Clim1
Clim2
Clim30 …
Time-series I
TSi - Clim n
Random Sampling Algorithm
Clim1
Clim2
Clim30 …
Time-series 100
TS100 - Clim9

12. Random Sampling Algorithm (RSA)
Generates a “well representative” sample of 100 climate
years
(by selecting a year randomly from each of the 100 time-series)
Justify - RSA generates a well representative sample
These 100 time series are statistically equivalent
(i.e. each 30 year long time series contain realistic day-to-day and year-to-year weather variability, but variation in statistical description of this variability is very little.)
2. WG produce these 100 time- series by sampling 100 different points across the full UKCP09’s probabilistic distribution.
UKCP09
Probability Distribution Curve

13. Climate data with Building Simulations
Fact
Indoor environment of a building depends on the outdoor climatic conditions.
Idea
Formulate a linear relationship of hourly climate variables and building comfort metrics (internal temperature) including HVAC system (Cooling load and Heating Load).
Note: A single climate year and corresponding outputs of building simulation can be used.

14. Climate data with Building Simulations
However it is not that easy – WHY?
WG produces climate information at hourly scale for seven different climate variables. (Temperature, Precipitation, Relative Humidity, Vapour Pressure, Sunlight Fraction, Direct Radiation and Diffuse Radiation)
To account for thermal mass and heat retention effects of the building, up to 72 hours of previous climatic information is required.
i.e. there are 7 × 72 = 504, input climate variables.

15. Principle Component Analysis (PCA)
What is PCA? Statistical method for high dimensional data analysis What PCA do? Transform large number of possibly correlated variables (say, x1 and x2) into small number of uncorrelated variables (Y1 and Y2).
X1 and X Y1 = aX1 + bX2
Y1 (Principal Component) can explain most of the variability in the X1 and X2.

16. PCA for present Study
PCA applied to 504 input climate variable in two steps:
Step 1: To 72 hours of dataset for each climate variable
Exploits correlation within the 72 hours of dataset
Reduces the 504 total input variables into 148 total components (Temperature – 11, Precipitation - 59, Relative Humidity - 16, Vapour Pressure - 7, Sunlight Fraction - 33, Direct Radiation - 13, and Diffuse Radiation - 9)
Accounts for 95% total variation in original dataset.

17. PCA for present Study
Step 2: To 148 components for different climate variable
Exploits correlation between different climate dataset
Reduces 148 total components into 33 sub-components
Temperature (11) – 11
Precipitation, Relative Humidity, Vapour Pressure (82) – 6
Sunlight Fraction, Direct Radiation, Diffuse Radiation (55) – 16
Accounts for 99% total variation in original dataset.

18. Modelling Procedure
Select one climate year randomly from representative sample of 100 climates
Fitting multiple regression model -
To estimate hourly building variant of interest (Indoor temperature, cooling/heating load) corresponding to 33 PC of climate variables
Can include 8 internal heat gain (IHG) variables
Building variant (t) =  (33 PC of Climate Variables) (t)
+  (8 IHG) (t)

19. Modelling Procedure - Low Carbon Future Tool
One Building Simulation
UKCP09
Climate Information
INPUT
Indoor Temperature
Cooling/Heating Loads
Occupancy
Internal Heat Gain
Air Change
Random Sampling
One PCA Climate
Calibrating Regression Coefficient
Data Processing
Principal Component Analysis
100 PCA Climates
Fitting Regression Model
100 Hourly Dynamic Building Simulation Profiles
100 Hourly Regression Model Emulation Profiles
Model Validation
Compare

20. Currently analysed buildings
Detached cavity-filled house
+ Window openings
+ External shading
+ Reduced internal gains
Naturally ventilated school
+ 3 adaptation scenarios as above
Mechanically cooled office
Low-energy house (CALEBRE project)
+ Multiple refurb scenarios (incl. MVHR)

21. Case Study – Predicting Cooling Demand in an Office Building
Number of Occupants 286
Glazing Ratio (% external wall) 40 and Glazing U- value 2.75
U-values(W/m2K) – Wall (0.65), Floor (0.27), Roof (0.87)
Infiltration rate (ac/h) 1
Ventilation rate (I/s/person) 10
ESPr wire diagram of four-storey office building
Height (14.8m) × Length (40m) × Breadth (25m)

22. Model Validation London (Medium Emission Scenario) 2050s
Over 100 Representative Climates
Hourly data
Occupied hours – 9:00 to 19:00 (weekdays))
Residuals – Difference between ESPr and LCFTool estimated value of cooling load.

23. Model Validation London (Medium Emission Scenario) 2050s
Over 100 Representative Climates
Hourly data (Occupied hours )
Model performance is justified in estimating different cooling loads
Residuals – Difference between ESPr and LCFTool estimated value of cooling load.

24. Mechanically-cooled buildings
Defining “failure” for buildings with mechanical cooling?
For a future climate, failure could be:
Cooling plant becomes undersized (unlikely) during the summer
Operation of plant (e.g. part-loading of multiple units) is no longer optimum
Cooling energy consumption/CO2 emissions are higher than originally designed
Similar analysis for heating loads
Can relate to optimum sizing of plant

25. A Simple Application of the Model
London
(Medium Emission Scenario)
2050s
LCFTool used to estimate five summary statistics
Summary Statistics measured for each of 100 climate file and then averaged.
194kW chiller

26. Summary
Various statistical techniques has been used to process complex climate projections with dynamic building simulations
Developed a validated methodology that can used to simulate 1000s of climate efficiently to emulate outputs of DBS software (ESPr or IES)
Methodology has been condenced in form of a simple to use LCFTool (developed in ‘R’ programming language)
LCFTool can be used to assess overheating/HC load risk for a range of building types
A range of suitable outputs can be generated with LCFTool to investigate various adaptation measures

27. Summary Future Plans for LCFTool Interface improvements
Further validation can be done to test limits of tool
More buildings
Different adaptation technologies
Interface improvements
Possibility for use in ARIES project for performing an Energy demand analysis
And so on …
THANK YOU