Climate Variation Sensitivity in

Building Energy Simulation

Presented By:

Roger

Cladingboel

Learning Objectives

• Understand the difference between RMY/TRY/TMY, AMY, and XMY climate files.

• Understand fundamentals differences in data collection and analysis methods for creating TMY & XMY files.

• Understand the impact climate change is having on EUI across multiple building types and multiple climate zones in the US and Canada.

Acknowledgements

• Nathan Kegel, Megan Tosh, Liam Buckley, IES

• Shona O’Dea, DLR Group

• Craig Burton, PositivEnergy Practice

• Ralph Muehlheisen, Argonne National Lab

Previous Research

• Loveland, J.E. & Brown, G.Z. (1989) Impacts of Climate Change on the Energy Performance of Buildings in the United States

• Crawley, D. (1998) Which weather data should you use for energy simulation of commercial buildings?

• Crawley, D. (2008) Building performance simulation: a tool for policymaking• Sustainable United Nations (2009), Buildings and Climate Change: Summary for

Decision Makers• Larsen, et. al. (2011) Green Building and Climate Resilience: Understanding

impacts and preparing for changing conditions• Bhandari, Srhestha, New (2012) Evaluation of weather data sets for building

simulation

Why care about the Weather?

Why Care About the Climate?

Annual average land surface temperature compared against a 1960-1990 average in °C

Source: http://data.giss.nasa.gov/gistemp/time_series.html

Land and ocean global mean

Annual average land surface temperature compared against a 1960-1990 average in °C

Source: https://data.giss.nasa.gov/gistemp/graphs

Annual average land surface temperature compared against a 1960-1990 average in °C

Source: http://data.giss.nasa.gov/gistemp/time_series.html

Atlanta

Brisbane

Melbourne

Cairns

Annual average land surface temperature compared against a 1960-1990 average in °C

Source: http://data.giss.nasa.gov/gistemp/time_series.html

Atlanta

Miami

Fairbanks

Brisbane

Melbourne

Cairns

Impacts of Climate Change on Building Design & Retrofit

• More frequent extremes– Temperatures, solar radiation, storms, floods, etc.

• Changes in “optimized” solutions– Reduced frequency of “typical” year; increased frequency of “extreme”

years• Cost to building owners

– Insurance premiums due to more extreme weather• Changes to payback calculations, ROI, IRR, etc.

– Passive strategies may be more/less effective• Changes to design recommendations

– Should “traditional local” architecture change?• Changes to Standard 90.1/189

– EUI changes to baseline models driven by climate change

Fundamentals of Weather Data

Multi-SensorRadar Based

Reanalysis ModelsSatellite Based

MADIS – Quality Assured METAR (Ground

Observation Data)

Climate Forecast System Reanalysis –

NOAAs global re-analysis dataset

combining satellites, balloons, radar and

ground observations

Combining CFSR and METARHourly

DataBlended METAR/CFSR data set

Gap Filled

Interpolated

Creation of XMY Files

Creation of TMY Files

TMY 3:

TMY 15:

TMY 7:

1985-2014

2000-2014

2008-2014

Each Variable is weighted

▪ Min/max Temperature: 1/20

▪ Avg Temperature: 2/20

▪ Min/max Dewpoint: 1/20

▪ Avg Dewpoint: 2/20

▪ Min/Max Windspeed: 2/20

▪ Avg GHI/DNI: 5/20

▪ Short Term and long term

weighted averages are

compared

▪ Output is one year of data with

these typical months

▪ Interfaces are smoothed with

Boxcar 5 method

XMY vs. TMY

XMY Strengths:

• Ideal for modeling

building performance

in extreme conditions

• Aids in understanding

the range of a climate

over a number of years

TMY Strengths:

• Ideal for modeling

building performance in

historically typical

conditions

• Displays the most

typical weather patterns

for a range of years

XMY weaknesses:

• Would not optimize

model for historically

typical conditions

• Can be skewed by a

once in a generation

extreme year

TMY Weaknesses:

• Is not representative

of highly volatile

climates

• Does not capture

extreme conditions

AMY Comparison

TMY2 vs. TMY3 Comparison

TMY3 Comparison

EU

IK

btu

/sf/yr

Option 1

90.1-2004U=.46 SHGC=.26

Option 2U=.30 SHGC=.38

Option 3

MN 2000 CodeU=.40 SHGC=.70

Option 4U=.21 SHGC=.37

Previous Work

ASHRAE Climate Zones

Other weather sources:

• Meteonorm – software approach, stochastic and interpolation weather file creation from global monthly data.– Climate change scenarios 3No. IPCC– Extreme years P10, P90 etc

• Local XMY (Extreme Weather Year files) creation on request – Exemplary Energy etc.

• Weathershift – online algorithmic approach converting existing EPW files.

Weathershift service:

• This tool uses data from global climate change modeling to produce EPW weather files adjusted for changing climate conditions. Cost to building owners

• The projected data can be viewed for three future time periods based on the emission scenario selected to the left.Passive strategies may be more/less effective

• The RCP’s (Representative Concentration Pathways) are greenhouse gas emission scenarios for the 21st century that result in the CO2 equivalent atmospheric concentrations

• Weathershift adjusts weatherfiles for future climate conditions based on RCP 4.5 (moderately aggressive mitigation) and RCP 8.5 (business as usual)

GHG Concentrations / Emission Scenarios

RCP 8.5 – Business As Usual, RCP 4.5 more typical

Predicted temperature increase scenarios, 10/50/90th percentiles default

• Typical city RCP 8.5 2090 prediction

Sydney EPW - weathershifted

Sydney EPW - weathershifted

Default EPW

2050 RCP 8.5 90th

Percentile

The Research Project

• Used geometry from DOE Reference Buildings• Used 90.1-2007 PRM Space-by-Space baseline• 16 Reference Building Types• 16 Climate Zones• 8 Climate Data Sets per Climate Zone

– 5 TMY (or CWEC)– 3 XMY

• 2,048 Simulations

Parametric Tool

Features:• Grouping can vary studies i.e:

• weather variable x7 (then)• extract variable x3 (then)• flow variable x3

• Total of 13 simulations

• Or variables can be ‘Combined’ then the variables that are defined as ‘Link’ will run as follows:

• weather variable x1 • (then for that weather) extract variable x3 • (then for that weather) flow variable x3

• repeat for each weather variable

• Total of 42 simulations

Reference Buildings

Locations Used

• 1A – Miami, FL• 2A – Houston, TX• 2B – Phoenix, AZ• 3A – Atlanta, GA• 3B – Las Vegas, NV• 3C – San Francisco, CA• 4A – Baltimore, MD• 4B – Albuquerque, NM• 4C – Portland, OR• 5A – Chicago, IL• 5B – Boulder, CO• 5C – Vancouver, BC• 6A – Minneapolis, MN• 6B – Helena, MT• 7 – Duluth, MN• 8 – Fairbanks, AK

Method

1. Selected Location2. Created Geometry/Zones3. Assigned constructions

1. ASHRAE 90.1-2007 baseline for new construction4. Assigned internal gains/schedules

1. ASHRAE 90.1-2007 Space-by-Space Method5. Created HVAC systems

1. ASHRAE Baseline Systems as appropriate per 90.1-2007 PRM6. Assigned Zones to HVAC systems7. Sized HVAC systems (auto-sized)

1. Design-day sizing using ASHRAE Heat Balance Method8. Ran Energy Simulations

1. Parametric runs - varied climate data set1. TMY2 (or CWEC), TMY3 (DOE), TMY3 (WA), TMY(7), TMY(15), XMY(MAX), XMY(MIN), XMY (MIN-MAX)

Results

• Over 1 TB of data generated from the 2,048 simulations (used fully detailed results)– Max simulation time (12 minutes – Hospital)– Hardware used: i7 laptop, 1 TB HD, 12 GB RAM

(purchased in 2013)– Software used: IES <VE> 2014 Feature Pack 1

• Every building type shows significant EUI (Energy Use Intensity) variance based on varying the weather data

Medium Office

Hospital

Secondary School

Small Hotel

Strip Mall

Full Service Restaurant

Warehouse

Conclusions• TMY2 and TMY3 data is not appropriate for making future

decisions (aka design decisions) for all building types• XMY file types likely give better understanding of EUI, energy end-

use, and other building performance metrics when considering climate change, but…

• XMY files on their own are likely insufficient to understand future “typical” climates

• Multiple climate files are needed to understand the broader impacts of building performance over time in the face of rapid climate change

What can you do?

• Consider accounting for climate change in energy simulation (and by extension) your design process and actual building performance:

• www.betterenergymodels.org