climate variation sensitivity in building energy simulation · 2017-11-19 · –max simulation...
TRANSCRIPT
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