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 S.Patidar@hw.ac.uk Maxwell Institute for Mathematical Sciences Urban Energy Research Group

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.

Low Carbon Future Project EPSRC funded through ARCC Coordination Network For details see - http://www.ukcip-arcc.org.uk/ 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

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)

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 -

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

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”

Weather Generator (UKCP’09) Emission Scenario Low Medium High Time Period Baseline (1960 – 1990) 2020s (2010 - 2039) 2030s (2020 – 2049) … 2080s (2070 - 2099) 100 Statistically equivalent hourly time series (Each time-series is 30 years in length) Location UK map 5 km grid squares

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

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.

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

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

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.

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.

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 X2 Y1 = aX1 + bX2 Y1 (Principal Component) can explain most of the variability in the X1 and X2.

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.

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.

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)

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

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)

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)

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.

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.

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

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

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

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