Automatic Building Energy Model Creation (AutoBEM) and a...
Transcript of Automatic Building Energy Model Creation (AutoBEM) and a...
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ORNL is managed by UT-Battelle
for the US Department of Energy
Automatic Building Energy Model Creation (AutoBEM) and a sampling of ORNL capabilities related to Artificial Intelligence and Buildings
Local ASHRAE Chapter
Presented by:
Joshua New, Ph.D., CEM
BTRIC, Software Tools & Models
Oak Ridge National Laboratory
November 15, 2017
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Joshua New, Ph.D., C.E.M.
• Career
– 2009+ Oak Ridge National Laboratory, R&D staff
• ETSD, Building Technology Research & Integration Center (BTRIC),
Building Envelope & Urban Systems Research Group (BEUSR)
– 2012+ The University of Tennessee, Joint Faculty
• Education
– The University of TN, (2004-2009), Knoxville; Ph.D. Comp. Sci.
– Jacksonville State University, AL (1997-2001, 2001-2004)
M.S. Systems&Software Design, double-B.S. Computer Science
and Mathematics, Physics minor.
• Professional Involvement
– IEEE, Senior Member
– ASHRAE
• TC1.5, Computer Applications, Voting member and officer
• TC4.7, Energy Calculations, Voting member and officer
• SSPC140 and ASHRAE Guideline 14 involvement
• TC4.2, Climatic Information, Voting member and officer
• SSPC169, Weather Data for Building Design Standards
(24% of page count of 90.1-2016), Voting member
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3 Building Technologies Office
McKinsey Global Institute Analysis
Source: McKinsey Global Institute analysis
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4 Building Technologies Office
Humans and computers
• 3 lbs (2%), 20 watts (20%)
• 120-150 billion neurons
• 100 trillion synapses
– Firing time ~milliseconds
• Words: 60-120 bits/second
• 11 million bits/second input
– Consciousness - 40 bits/second
• Working memory – 4-9 words
• Long-term memory – 1-1k TB
• Complex, self-organizing
• PC – 40 lbs, 500 watts
• 4 CPU cores
• 3 billion Hz
– Firing time ~nanoseconds
• Network: 1M-1B bits/second
• 100 million bits/second
– Not yet
• 62,500,000 words
• Disk – 3TB, perfect recall
• “Dumb”, Artificial Intel.
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$1000 machine helping meat machines
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6 Building Technologies Office
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7 Building Technologies Office
Gaming
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8 Building Technologies Office
Hand Gesture Recognition
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Temporal Hand Gesture Recognition
Centroid, finger positions
Classify: Gesture
(circle, square, up/down, left-right shake/undo, etc.)
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10 Building Technologies Office
Neurophysiologically-based imaged fusion
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GAD PD
SPECT T2 Color Fuse Result
Field-deployed color night vision
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Full Results Detailed Results
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Retinal Fusion and Human/Computer Training for
automated Image Recognition originated from
MIT’s Lincoln Lab (tank detection from satellites)
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13 Building Technologies Office
PhD – relationships in scientific data
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Energy-Water Nexus Maximize the efficiency, sustainability and resiliency of interconnected energy and water
systems in the planning, development, and operation of urban infrastructures.
2012
Delta:
2050 - 2012
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Population and Land Use Provide insights into the evolving sociodemographic patterns of human activity that respond
to and transform urban landscapes and systems at varying spatial and temporal scales.
Morphology 1: Proposed (mixed use)
Morphology 2: High Building Density
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Sustainable Mobility
Improve transportation sustainability, safety, and accessibility through enhanced
understanding of the energy and environmental implications of emerging transportation
systems and their interdependencies with other critical infrastructures.
DOE SMART Mobility
Consortium
NEXTCAR
TUMS
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Urban Resiliency Enhance cities understanding of the risks, challenges, and opportunities of the integrated framework of
population, energy, water, transportation, and policy under changing environmental conditions.
Average of
all models
+2.14oF in daily Tmax
+2.51oF in daily Tmin
+0.88 in. in precipitation
Downscaled and analyzed 10
CMIPS global climate models
with RCP8.5 scenario at 4km
and 1km resolution for 1965-
2005 and 2010-2050
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UrbanSense Real-time Urban Activity Monitoring Using Pervasive Sensor Network
Population Distribution
Water Quality Air Quality Temp.,
Humidity Pollutants
Vibration POI Popularity Bi-directional Traffic Flow
Simulation and Modeling
Social Media
Water O2 Water Temp Conductivity
PlanetSense – web-based, API-accessible, subscription service for
real and virtual sensors
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From Visual Analytics and Simulations To Actualized Energy Savings in the Marketplace
Leveraging HPC & Vis resources to facilitate deployment of building energy efficiency technologies
DOE: Office of Science CEC & DOE EERE: BTO Industry & Building Owners
Roof Savings Calculator (RSC)
web site/service developed
(estimates energy and cost savings
from roof and attic technologies)
CentiMark, the largest nation-wide
roofing contractor (installs 2500
roofs/mo), is integrating RSC into
their proposal generating system
AtticSim
DOE-2
Engine (AtticSim/DOE-2) debugged
using HPC Science assets enabling
visual analytics on 3x(10)6 simulations
20+ companies interested
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Roof Savings Calculator “Oscars of Invention”
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40 Years: Energy and Quality of Life
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A brief history of energy and life quality
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US Energy Consumption
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U.S. Home, Avg. Utility Bill - $1,819
Energy Consumption and Production Commercial Site Energy
Consumption by End Use
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25 Building Technologies Office
Source of Input Data • Residential (~15 min. data)
– Yarnell (37 sensors)
– Wolf Creek (4x 356 sensors/building)
– Campbell Creek (3x 144 sensors/bldg.)
– Dryer
– Refrigerator
– Dishwasher
– Heat pump air flow
– Shower water flow
– Etc.
– Temperatures
– Plugs
– Lights
– Range
– Washer
– Radiated heat
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26 Building Technologies Office
Energy Modeling for Generalizing Results
Real weather
file from FRP2
weather station
Emulated
Occupancy 30 sec
Data Measurements
(1,071 total channels)
Calibrated Building
Energy Models
Software-controlled
rotation among
HVAC systems Centralized data, alarms,
quality assurance,
dashboard, analytics
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27 Building Technologies Office
Obama and VP inspect Shelby Cobra
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28 Building Technologies Office
3D printed house
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29 Building Technologies Office
Products: state-of-the-art & industrial strength • EnergyPlus engine: established
– Bentley, Trane TRACE, Autodesk, ASHRAE, CEC
• Spawn-of-EnergyPlus: cyberphysical control
• OpenStudio middleware: market-facing
Ecosystems: for developers & end-users • Partner with companies & professional organizations
• http://unmethours.com
Saves Energy • Individual ECMs (5-20% savings) vs.
Holistic optimization (30-50% savings)
• 600 TBTU / year by 2030 [BTO p-tool conservative]
• Will track integrative design & simulation use via AIA+2030 Commitment
• Prototype development, AEDGS, prescriptive guides, standards
DOE-BTO Modeling Tools Program
29 Building Technologies Program
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30 Building Technologies Office
ASHRAE Climate Zones
• Based on
weather
stations, most
w/ 18+ yrs of
quality data
(1961-1990)
Updated every 4 years (2021)
Climate Zone 0 (extremely hot): 10,800 < CDD 50°F Int’l Energy Conservation Code (IECC) adopts for 2018 code
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Redefine CZs using model data • Resolution: 2 km x 2 km grid cells
• Extent: Continental United States
• Present Conditions (1950-2000): WorldClim
• Future conditions (2050, 2100): PCM and Hadley GCMs; for
multiple scenarios (IPCC AR4 A1FI, B1)
• 12 energy-related variables
• Temperature
• Humidity
• Radiation
• Elevation
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32 Building Technologies Office
Data-derived CZs and assessment
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33 Building Technologies Office
Make it accurate where the buildings are
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34 Building Technologies Office
Building-adjusted CZ improvement
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35 Building Technologies Office
Climate Change Impacts
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Automatic Building Energy Model Creation (AutoBEM) Acknowledgements
• U.S. Department of Energy
• National Nuclear Security Administration
• Oak Ridge National Laboratory
• Building Technologies Office
• Office of Electricity
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$435B spent on energy bills every year
45%
Buildings
26%
Residential
19%
Commercial
Today 2030
Goal: Reduce EUI 30%
(compared to 2010
baseline)
U.S. Building Energy Consumption
Buildings consume 73% of electricity
130 million U.S. Buildings Building Energy Modeling – building descriptions + weather = estimated
building energy consumption
$9B/yr – ESCO; $7B/yr – utility EE $14B/yr – DR management systems 0.3% modified, BEM < 10% of those
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Optimal Return on Investment (for building energy savings)
Simulation Engine and Analysis Platform
DOE–$85M (1995–?)
EnergyPlus OpenStudio
Building Energy Modeling Overview
ASHRAE G14
Requires
Using Monthly utility data
CV(RMSE) 15%
NMBE 5%
Using Hourly utility data
CV(RMSE) 30%
NMBE 10%
3,000+ building survey, 23-97% monthly error
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.
.
.
Input
Model
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Automatic calibration of software to data
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HPC scalability for desktop software CPU
Cores Wall-clock
Time (mm:ss) Data
Size
EnergyPlus Simulations
16 18:14 5 GB 64
32 18:19 11 GB 128
64 18:34 22 GB 256
128 18:22 44 GB 512
256 20:30 88 GB 1,024
512 20:43 176 GB 2,048
1,024 21:03 351 GB 4,096
2,048 21:11 703 GB 8,192
4,096 20:00 1.4 TB 16,384
8,192 26:14 2.8 TB 32,768
16,384 26:11 5.6 TB 65,536
32,768 31:29 11.5 TB 131,072
65,536 44:52 23 TB 262,144
131,072 68:08 45 TB 524,288
Titan is the world’s fastest buildings energy model
(BEM) simulator
>500k building simulations in <1 hour
130M US buildings could be simulated
in 2 weeks
8M simulations of DOE prototypes
(270 TB)
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Calibration Performance – automated M&V
Industry and building owners Applied Research National HPC Resources
Leveraging HPC resources to calibrate models for optimized building efficiency decisions
Features • Calibrate any model to data
• Calibrates to the data you have
(monthly utility bills to submetering)
• Runs on a laptop and in the cloud
• 35 Publications: http://bit.ly/autotune_science
• Open source (GitHub): http://bit.ly/autotune_code
Results
Hourly – 8%
Monthly – 15%
Tuned input
avg. error
Within
30¢/day (actual
use $4.97/day)
Residential
home
ASHRAE
G14 Requires
Autotune Results
Monthly utility data
CVR 15% 1.20%
NMBE 5% 0.35%
Hourly utility data
CVR 30% 3.65%
NMBE 10% 0.35%
Results of 20,000+ Autotune calibrations (15 types, 47-282 tuned inputs each)
High Performance Computing • Different calibration algorithms
• Machine learning – big data mining
• Large-scale calibration tests
Other error metrics
3 bldgs, 8-79 inputs
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42 Building Technologies Office
Acknowledgment: Dr. Lynne Parker (NSF Div.
Dir. Info. and Intel. Systems);
Dr. Richard Edwards (doctoral student, now
Amazon’s ad analytics)
MLSuite: HPC-enabled suite of Artificial Intel.
• Linear Regression
• Feedforward Neural Network
• Support Vector Machine
Regression
• Non-Linear Regression
• K-Means with Local Models
• Gaussian Mixture Model with
Local Models
• Self-Organizing Map with Local
Models
• Regression Tree (using
Information Gain)
• Time Modeling with Local Models
• Recurrent Neural Networks
• Genetic Algorithms
• Ensemble Learning
MLSuite XML
PBS
Linux #1
Super-computer #1
Linux #218
Super-computer #2 …
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Model America 2020 – calibrated BEM for every U.S. building
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Manual Segmentation of DC
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Automatic Road Extraction
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Automatic Building Footprint Extraction
Algorithm: Deep Learning extended and using GPUs for fast building footprint and area extraction over large geographical areas.
Multi-company Competition Precision/Recall – 30/35; Current Precision/Recall – 60+/60+
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Automatic Building Footprint Extraction
Portland, OR (25,393 m²)
Imagery: June – July 2012
Lidar: September 2010
Part of Knox County, TN (18,527 m²)
Imagery: June 2012
Lidar: October 2014
Frankfort, KY (14,801 m²)
Imagery: June 2012
Lidar: June 2011
• 220,005 NAIP images
• 1 meter multispectral
• 2012-2014
• 5.8 TB compressed
• 9.8 trillion pixels
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Prototype Buildings
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Oak Ridge National Laboratory
4500N 4020 4500S
4512 6000 6008
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Oak Ridge National Laboratory (interactive)
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The University of Tennessee (2 days)
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Chattanooga, TN (North Shore)
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Utility Use Cases for Virtual EPB
• Peak Rate Structure - model peak segment customers in aggregate as disproportionate contributors to electric utilities’ wholesale demand charges for more equitable rate structures.
• Demand Side Management – identify DSM products and grid services for better distribution grid management that allow both utilities and rate-payers to share in peak reduction
• Grid stability services – quantify improved load models
• Emissions – accurately account for emissions contributed by each building, providing enhanced abilities for utilities to best comply with national emission policies.
• Energy Efficiency – accurate modeling/forecasting of every building energy profile virtually in a scalable fashion allows better follow-up and more targeted energy audits/retrofits.
• Customer Education - better understand building’s energy usage as a function of weather to provide better information during customer billing enquiries.
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Chattanooga, TN (135,000+ buildings)
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Virtual EPB (135,481 buildings – simulation results)
• Building ID • Area (m2) • Number of Floors • Color: Energy Use
Intensity (kWh/m2) by building type
• Min/Avg/Max by building type
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Resources
• Toolbox for Urban Mobility Simulations (TUMS) - tums.ornl.gov
• City-scale sensing and visual analytics (PlanetSENSE) - planetsense.ornl.gov
• Adaptation Tool, stormwater management (UrbanCAT) - urbancat.extranet.ornl.gov/
• Roof Savings Calculator (RSC) - roofcalc.com
• World’s fastest buildings simulator, Titan supercomputer (AutoSIM): • https://www.olcf.ornl.gov/support/getting-started/olcf-director-discretion-project-
application/
• Automatic Building Energy Models (AutoBEM)
• Oak Ridge National Laboratory: bit.ly/ornl_buildings, Geometry, EnergyPlus Simulation Results
• The University of Tennessee: bit.ly/ut_buildings
• Chattanooga, TN: bit.ly/epb_buildings
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BTRIC, Software Tools & Models Oak Ridge National Laboratory
HPC Tools for Modeling and Simulation
Capturing building energy consumption