predictive analytics for real-time energy management … managed by general atomics ... second life...
TRANSCRIPT
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Presented by
Predictive Analytics for Real-time Energy Management and Dynamic Demand Response
Natasha Balac, Ph.D.Director, Predictive Analytics Center of ExcellenceSan Diego Supercomputer CenterUniversity of California, San Diego
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Brief History of SDSC
• 1985-1997: NSF national supercomputer center; managed by General Atomics
• 1997-2007: NSF PACI program leadership center; managed by UCSD• PACI: Partnerships for Advanced Computational
Infrastructure
• 2007-2009: Internal transition to support more diversified research computing• still NSF national “resource provider”
• 2009-future: Multi-constituency cyberinfrastructure (CI) center• provide data-intensive CI resources, services, and
expertise for campus, state, and nation
• Approaching $1B in lifetime contract and grant activity
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Today’s Information Technologies Drive 21st Century Solutions
• Labs and Centers• Advanced Query Processing• Spatial Data Systems• Data-Driven High Performance Computing• Scientific Workflow Automation Technologies • Large-scale Data Systems• Predictive Analytics• Data Visualization
• We teach and mentor students
• Graph Data Management• Semantic Web• Machine Learning and Analytics• GIS and Spatial Technologies• Information Integration• Text Analytics• Big Data technologies
Will California levees hold in a large-scale
earthquake?
What therapies can be used to cure or control
cancer?
Can financial data be used to accurately predict market
outcomes?
What plants work best for biofuels?
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
What is Gordon?
• A “data-intensive” supercomputer based on SSD flash memory and virtual shared memory
• Emphasizes MEM and IO over FLOPS
• Designed specifically for data-intensive HPC applications
• System utilization is cut in half every 18 months while it takes 2x time to read your disk every 18 months
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Gordon Speeds and FeedsINTEL SANDY BRIDGE COMPUTE NODE
Sockets & Cores 2 & 16
Clock speed 2.6 GHz
DRAM capacity and speed 64 GB, 1,333 MHz
INTEL710 eMLC FLASH I/O NODE
NAND flash SSD drives 16
SSD capacity per drive & per
node16 * 300 GB = 4.8 TB
SMP SUPER-NODE (VIA VSMP)
Compute nodes / I/O Nodes 32 / 2
Addressable DRAM 2 TB
Addressable memory including flash
11.6 TB
GORDON (AGGREGATE)
Compute Nodes 1,024
Compute cores 16,384
Peak performance 341 TF
DRAM/SSD memory 64 TB DRAM; 300 TB SSD
INFINIBAND INTERCONNECT
Architecture Dual-Rail, 3D torus
Link Bandwidth QDR
Vendor Mellanox
LUSTRE-BASED DISK I/O SUBSYSTEM (SHARED)
Total storage: current/planned 4 PB/6 PB (raw)
Total bandwidth 100 GB/s
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
PACE: Closing the gap between Government, Industry and Academia
PACE is a non-profit, public
educational organization
oTo promote, educate and innovate
in the area of Predictive Analytics
oTo leverage predictive analytics to
improve the education and well
being of the global population and
economy
oTo develop and promote a new,
multi-level curriculum to broaden
participation in the field of
predictive analytics
Predictive Analysis
Center of Excellence
Inform, Educate and
Train
Develop Standards
and Methodology
High Performance
Scalable
Data Mining
Foster Research
and Collaboration
Data Mining Repository of Very Large Data Sets
Provide Predictive Analytics Services
Bridge the Industry and Academia
Gap
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Foster Research and Collaboration
Predictive Analysis
Center of Excellence
Inform, Educate and
Train
Develop Standards and Methodology
High Performance
Scalable
Data Mining
Foster Research and Collaboration
Data Mining Repository of Very Large Data Sets
Provide Predictive Analytics Services
Bridge the Industry and Academia
Gap
• Fraud Detection• Modeling user behaviors• Smart Grid Analytics• Solar powered system
modeling• Microgrid anomaly
detection• Battery Storage Analytics• Sport Analytics• Manufacturing• Genomics
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
The City in a City
UCSD Microgrid
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
• 28,000 students and 28,000 faculty and staffRated 7th best public
University in Nation by U.S. News
Jacobs School of Engineering 4th in Bioengineering
• Peak load ~42 MW• Self Generation 30 MW• Solar PV ~1.5 MWSoitec CPV (27.5 kW)
UCSD Microgrid
• Fuel Cell 2.8 MW• Battery systems Second life (60 kWh)Peak shifting (30 kW/30
kWh)3 MW/6 MWH in Q3 2012
Internationally recognized microgrid
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Microgrid’s Electrical One Lines
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
• Provides 85% heating, cooling and electricity for most of the UCSD Campus
• Cogeneration system consists of two 13.5 Megawatt natural gas-fired turbine generators
• These two gas turbines provide 27 Megawatts of electricity for the campus buildings
Central Utility Plant
• 60,000Lb/Hr of Steam per unit, totaling 120,000 Lb/Hr produced as available waste heat, which provides heating and cooling energy for the Chiller Plant and the Heating Plant
• Awarded 1 of 3 “Energy Star Awards” by EPA in 2010 for achieving 66% efficiency (LHV)
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
The Central Utility Plant Saves the UCSD Campus
$850,000 Per Month!
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
• 1.2 MWs under PPA• 58 kW Demo sites• 63 kW Owned by campus• 190 kW Sustainable Comm. Program
Campus Solar Power
Currently have 1.5 MWs on campus
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Price Center And Student Center
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
30 kW/30 KWH PV Integrated Storage System
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
UCSD’s Electrification of the Transportation Sector
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
• Molten Carbonate fuel cell
technology from FuelCell
Energy of Danbury CT
• CA’s first and only
“directed biogas” project
• Largest commercially
available design
• Commenced commercial
operations in Dec ‘11
2.8 MW Fuel Cell Demonstration
• Installed and operated under a PPA with BioFuels Energy LLC at a cost that is parity with the grid
• 10% of the campus’ baseload energy supply has a footprint the size of a tennis court
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
2.8 MW Fuel Cell Demonstration
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
August 14 Demand Response
• “SDG&E has notified the campus that energy demand reduction is needed between 1 and 6 p.m. today due to continued high temperatures taxing regional power reserves. Without demand reduction, SDG&E may be required to take actions to stabilize the utility grid
• Beginning at 1 p.m. today, Facilities Management will automatically reduce power demand by adjusting campus comfort cooling settings
• Heating, ventilation and air conditioning in office areas will go into "unoccupied" mode and spaces will be warmer or cooler than normal, depending on the space. Temperature set points in lab areas will range between 68 and 76 degrees. Airflow will not be affected”
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Comparison between August 13 and August 14
Pow
er (k
W)
Time (24 hours)
August 13
August 14 Demand Response
Imports = Black; Total Consumption = Blue
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
DR: Stacked Area Chart Comparison
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Po
wer
Dem
an
d (
kW
)
Time (hour of day)
UCSD Power Demand (Imported and Generated)on August 13th and 14th
SDGE Imports
Estimated SolarProduction
Steam Turbine
Generator B
Generator A
Fuel Cell
August 13th 2012 August 14th 2012
Po
wer
Dem
an
d (
kW
)
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Comparison with September 14
Po
wer
(k
W)
Time (hours)
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Significance
• UCSD can reliably reduce load by 6 MW in 54 minutes
• Sufficient chilled water must be available to allow converting the steam chiller to a steam generator
• Can building energy response be improved by using existing HVAC data for efficiency?
• Building KPI Intern program • Summer of 2012
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Why work on Building control systems
• As of 2010, the DoE reported that building energy consumption represents 41.1% of the U.S. Primary Energy Consumption which was greater than the industrial and transportation sectors
• A small improvement in building HVAC efficiency can result in large reduction in carbon emissions and lower costs of operations
• Building might be useful “control” devices in local area power systems, especially to help compensate for solar PV and EV charging system intermittency
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
•Measures performance of a building
•Annual energy per square foot
•Can include other variables:
Occupancy
Building type
Direction the windows face
Energy source
•Comfort KPI can include:
Amount of air flow
Zone temperature
Quality of air (CO2)
Key Performance Indicator
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
“You Can’t Manage What You Don’t Measure”
2
9
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
• Problem
Legacy naming conventions not documented
Multiple names for same object begin measured
• Approach
Use object oriented design of naming structure
Use IEC 61850 recommended names for leaf objects
Use ISO 50001 Building Automation Standards
• Goals
Set foundation for auto discovery and naming of BACnetsystems
Use PI AF data references to auto create PI tags with rigid structure
Create PI AF names and tagnames with fixed width components
Tag naming issues and approach
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
California UCSD WarrenComputer &
Science Engineering
Building
Floor 1Room 1106
HVAC Room Control Actual Cooling Setpoint
Actual Heating Setpoint
Actual Supply Air
Flow Setpoint
MeasurementActual
Supply Air Flow
Damper Position
Zone Temperature
Before
After
Naming Structure
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Results of the Building Model
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Supply Air Flow & Damper Position
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Computer Science & Engineering Building
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Weekly Pattern Large Building Energy Usage
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
UCSD Smart Grid
• UCSD Smart Grid sensor network data set• 45MW peak micro grid; daily population of over 54,000 people
• Self-generate 92% of its own annual electricity load
• Smart Grid data – over 100,000 measurements/sec
• Sensor and environmental/weather data• Large amount of multivariate and heterogeneous data streaming from
complex sensor networks
• Predictive Analytics throughout the Microgrid
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Big Data from Microgrids
• Complexities introduced by the large amount of multivariate and heterogeneous data streaming from complex sensor networks
• Extremely large, complex sensor networks, enabling a novel feature reduction method that scales well
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
4 V’s of Big Data
IBM, 2012
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Predictive Analytics for Discovering Energy Consumption Patterns
• The utility and the consumer both benefit from consumption analytics
• Forecasting the energy consumption patterns in the UCSD campus microgrid
• Different spatial and temporal granularities
• Novel Feature Engineering
• Machine learning for demand response optimization
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Predicting Energy Consumption
• Modeling the energy usage behavior of campus buildings is an important step to improving the efficiency of the system
• An accurate model of future energy usage enables:• anomalies discovery and point to wasteful usage of energy
• combat high future demands
• optimize pre-heating or pre-cooling scheduling
• maximize the energy efficiency
• help guide decisions regarding demand response, excess generation, renewable supply and load balancing and manage power outages
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Multivariate Time Series Classification Approach
Metafeature Extraction
Global Feature Extraction
Feature Space Segmentation
Feature Space Abstraction
Intermediate Dataset Creation
Time Series Input X
Final Model Y
Step 2: Global Feature
Extraction
Step 3: Feature Space
Segmentation
Step 1: Metafeature
Extraction
Step 6: Standard
MineTool
Step 5: Intermediate
Dataset Creation
Step 4: Feature Space
Abstraction
MineTool-TimeSeries [Karimabadi, Sipes, et al
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
• Observed 3 MW
demand response from
buildings
• Observed 30 minute
time constant from
buildings
• Asked the question
- Can buildings be used
for microgrid control?
How can buildings be used as control variables?
• What effect will solar
PV have on grid
stability?
• What effect will fast
EV charging have on
grid stability?
• Can the Microgrid
deliver ancillary
services to the area
power system?
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Solar Power Integration Through Predictive Analytics
Predictive
analytics
Solar + EV
integration
Business &
Societal change
Battery Storage
Analytics
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Distributed Energy Generation
• Rapid growth of renewable generation capacity
• Photovoltaic (PV) generation on residential and commercial rooftops
• Interconnected in the electric distribution system, rather than at the high-voltage transmission level
• Population of plug-in hybrids and electric vehicles (EVs) is expected to grow substantially
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Analytics/Visualization Examples
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Power Generation Dashboard with PowerPivot
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Power Generation Dashboard with Power View
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Power Generation with Maps
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Building Energy Consumption Analytics with PowerPivot
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Building Energy Consumption Analytics with Maps
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Building Carbon Emission Analytics with Power View
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Building Carbon Emission Analytics with Power Pivot
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Building Carbon Emission Analytics with Maps
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Building Energy Consumption with GeoFlow Heat Map
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
• Clean Tech San Diego, OSIsoft, SDG&E and UC San
Diego Common data infrastructure connects physical
assets: electrical, gas, water, waste, buildings,
transportation &traffic
• Platform to securely transfer high volumes of Big Data
from multiple, distributed measurement units
• Crowd-sourced Big Data in a cyber-secure, private cloud
• Predictive analytics on real-time time-series data
Sustainable San Diego Partnership
Predictive Analytics Center of Excellence
White House Big Data Event: “Data to Knowledge to
Action” – Launch Partners Award
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Smart City (San Diego)
PI 2012 + SQL 2012 EE + SharePoint 2013
PI Visualization (DL, WP, CS,
PB)
AF, Asset Based Analytics
PowerView, PowerPivot,
GeoFlow
San Diego Gas &
Electric
(SDG&E)
SDSU UCSD
PI Cloud Connect
PI
PI Cloud Connect
Pipeline
Interface
Pipeline
Interface
Pipeline
Interface
Downtown Buildings
Azure IaaS (VM)Windows 8“Prius” dashboard
Power ViewBusiness Insight “drill down”
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
A common data infrastructure that connects all of the various physical assets/infrastructures
–Electrical
–Gas
–Water
–Transportation & Traffic
–Critical Facilities
–Communications
–Buildings
Why does a City need a Data Infrastructure?
Smart Infrastructure
Gas & Electric
Water & Wastewater
Waste Management
Critical Facilities & Buildings
Transportation & Traffic
Communications & Data Centers
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
PACE EducationEducating the new generation of Data Scientist
• Data mining Boot Camps
• Boot Camp 1
• April, 2014
• Boot Camp 2
• May 2014
• On-site training
• Tech Talks - 3rd Wednesday
• Workshops – Hadoop, PMML
• Summer Institute
• Institute for Data Science and Engineering
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Mike Gualtieri's blog
© Copyr i gh t 2014 OSIso f t , LLC.
Thank You!
• www.pace.sdsc.edu
• For further information, contact Natasha Balac [email protected]
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