Automatic Building Energy Model Creation (AutoBEM) and a sampling of ORNL capabilities related to Artificial Intelligence and Buildings

Transcription

1 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 ORNL is managed by UT-Battelle for the US Department of Energy

2 Joshua New, Ph.D., C.E.M. Career Oak Ridge National Laboratory, R&D staff ETSD, Building Technology Research & Integration Center (BTRIC), Building Envelope & Urban Systems Research Group (BEUSR) The University of Tennessee, Joint Faculty Education The University of TN, ( ), Knoxville; Ph.D. Comp. Sci. Jacksonville State University, AL ( , ) 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 ), Voting member 2

3 McKinsey Global Institute Analysis Source: McKinsey Global Institute analysis 3 Building Technologies Office 3

4 Humans and computers 3 lbs (2%), 20 watts (20%) billion neurons 100 trillion synapses Firing time ~milliseconds Words: 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. 4 Building Technologies Office 4

5 $1000 machine helping meat machines 5 Building Technologies Office 5

6 6 Building Technologies Office

7 Gaming 7 Building Technologies Office

8 Hand Gesture Recognition 8 Building Technologies Office

9 Temporal Hand Gesture Recognition Centroid, finger positions Classify: Gesture (circle, square, up/down, left-right shake/undo, etc.) 9 Building Technologies Office

10 Neurophysiologically-based imaged fusion 10 Building Technologies Office

11 Field-deployed color night vision PD GAD T2 SPECT Color Fuse Result 11 Building Technologies Office

12 Retinal Fusion and Human/Computer Training for automated Image Recognition originated from MIT s Lincoln Lab (tank detection from satellites) Full Results Detailed Results 12

13 PhD relationships in scientific data 13 Building Technologies Office

14 Energy-Water Nexus Maximize the efficiency, sustainability and resiliency of interconnected energy and water systems in the planning, development, and operation of urban infrastructures Delta:

15 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 15

16 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 TUMS 16 NEXTCAR

17 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 o F in daily Tmax o F in daily Tmin in. in precipitation Average of all models Downscaled and analyzed 10 CMIPS global climate models with RCP8.5 scenario at 4km and 1km resolution for and

18 UrbanSense Real-time Urban Activity Monitoring Using Pervasive Sensor Network Conductivity Water O 2 Water Temp PlanetSense web-based, APIaccessible, subscription service for real and virtual sensors Population Distribution Water Quality Air Quality Temp., Humidity Pollutants Vibration POI Popularity Bi-directional Traffic Flow Simulation and Modeling Social Media 18

19 From Visual Analytics and Simulations To Actualized Energy Savings in the Marketplace DOE: Office of Science CEC & DOE EERE: BTO Industry & Building Owners Engine (AtticSim/DOE-2) debugged using HPC Science assets enabling visual analytics on 3x(10) 6 simulations CentiMark, the largest nation-wide roofing contractor (installs 2500 roofs/mo), is integrating RSC into their proposal generating system AtticSim 64 DOE-2 Roof Savings Calculator (RSC) web site/service developed (estimates energy and cost savings from roof and attic technologies) 20+ companies interested Leveraging HPC & Vis resources to facilitate deployment of building energy efficiency technologies 19

20 20 Roof Savings Calculator Oscars of Invention

21 21 40 Years: Energy and Quality of Life

22 22 A brief history of energy and life quality

23 23 US Energy Consumption

24 Energy Consumption and Production Commercial Site Energy Consumption by End Use U.S. Home, Avg. Utility Bill - $1,819 24

25 Source of Input Data Residential (~15 min. data) Yarnell (37 sensors) Wolf Creek (4x 356 sensors/building) Campbell Creek (3x 144 sensors/bldg.) Temperatures Plugs Lights Range Washer Radiated heat Dryer Refrigerator Dishwasher Heat pump air flow Shower water flow Etc. 25 Building Technologies Office

26 Energy Modeling for Generalizing Results Calibrated Building Energy Models Real weather file from FRP2 weather station 30 sec Data Measurements (1,071 total channels) Emulated Occupancy Centralized data, alarms, quality assurance, dashboard, analytics Software-controlled rotation among HVAC systems 26 Building Technologies Office

27 Obama and VP inspect Shelby Cobra 27 Building Technologies Office

28 28 Building Technologies Office 3D printed house

29 DOE-BTO Modeling Tools Program 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 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 29 Building Technologies Office Program

30 ASHRAE Climate Zones Based on weather stations, most w/ 18+ yrs of quality data ( ) 30 Building Technologies Office 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

31 Redefine CZs using model data Resolution: 2 km x 2 km grid cells Extent: Continental United States Present Conditions ( ): WorldClim Future conditions (2050, 2100): PCM and Hadley GCMs; for multiple scenarios (IPCC AR4 A1FI, B1) 12 energy-related variables Temperature Humidity Radiation Elevation 31 Building Technologies Office

32 Data-derived CZs and assessment 32 Building Technologies Office

33 Make it accurate where the buildings are 33 Building Technologies Office

34 Building-adjusted CZ improvement 34 Building Technologies Office

35 Climate Change Impacts 35 Building Technologies Office

36 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

37 U.S. Building Energy Consumption Buildings Residential 26 % 45 % Buildings consume 73% of electricity Commercial $435B spent on energy bills every year 19 % 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 Goal: Reduce EUI 30% (compared to 2010 baseline) Today 2030

38 Building Energy Modeling Overview Optimal Return on Investment (for building energy savings) 3,000+ building survey, 23-97% monthly error ASHRAE G14 Requires Simulation Engine and Analysis Platform DOE $85M (1995?) Using Monthly utility data Using Hourly utility data CV(RMSE) 15% NMBE 5% CV(RMSE) 30% NMBE 10% EnergyPlus OpenStudio

39 Automatic calibration of software to data Input Model... 39

40 HPC scalability for desktop software 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) CPU Cores Wall-clock Time (mm:ss) Data Size EnergyPlus Simulations 16 18:14 5 GB :19 11 GB :34 22 GB :22 44 GB :30 88 GB 1, : GB 2,048 1,024 21: GB 4,096 2,048 21: GB 8,192 4,096 20: TB 16,384 8,192 26: TB 32,768 16,384 26: TB 65,536 32,768 31: TB 131,072 65,536 44:52 23 TB 262, ,072 68:08 45 TB 524,288 40

41 Calibration Performance automated M&V National HPC Resources Applied Research Industry and building owners Results ASHRAE G14 Requires Autotune Results High Performance Computing Different calibration algorithms Machine learning big data mining Large-scale calibration tests 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: Open source (GitHub): Monthly utility data Hourly utility data Residential home Within 30 /day (actual use $4.97/day) CVR 15% 1.20% NMBE 5% 0.35% CVR 30% 3.65% NMBE 10% 0.35% Results of 20,000+ Autotune calibrations (15 types, tuned inputs each) Other error metrics Leveraging HPC resources to calibrate models for optimized building efficiency decisions Tuned input avg. error Hourly 8% Monthly 15% 3 bldgs, 8-79 inputs

42 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 XML MLSuite PBS Self-Organizing Map with Local Models Regression Tree (using Information Gain) Time Modeling with Local Models Recurrent Neural Networks Genetic Algorithms Ensemble Learning Supercomputer #1 42 Building Technologies Office Linux #1 Linux #218 Supercomputer #2 Acknowledgment: Dr. Lynne Parker (NSF Div. Dir. Info. and Intel. Systems); Dr. Richard Edwards (doctoral student, now Amazon s ad analytics)

43 Model America 2020 calibrated BEM for every U.S. building

44 Manual Segmentation of DC

45 Automatic Road Extraction

46 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+

47 Automatic Building Footprint Extraction 220,005 NAIP images 1 meter multispectral TB compressed 9.8 trillion pixels Portland, OR (25,393 m²) Imagery: June July 2012 Lidar: September 2010 Frankfort, KY (14,801 m²) Imagery: June 2012 Lidar: June 2011 Part of Knox County, TN (18,527 m²) Imagery: June 2012 Lidar: October 2014

48 Prototype Buildings

49 Oak Ridge National Laboratory 4500N S

50 Oak Ridge National Laboratory (interactive)

51 The University of Tennessee (2 days)

52 Chattanooga, TN (North Shore)

53 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.

54 Chattanooga, TN (135,000+ buildings)

55 Virtual EPB (135,481 buildings simulation results) Building ID Area (m 2 ) Number of Floors Color: Energy Use Intensity (kwh/m 2 ) by building type Min/Avg/Max by building type

56 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): 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

57 BTRIC, Software Tools & Models Oak Ridge National Laboratory HPC Tools for Modeling and Simulation Capturing building energy consumption