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#AnalyticsXC o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
Prediction of Chilled Water Plant Failures and System Optimization Using Multivariate Modeling Techniques
Joel UrbanDirector of Quality Assurance
Brady Services, Inc.
Leah Lehman
SAS
IoT Program Director
#AnalyticsXC o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
Prediction of Chilled Water Plant Failures and System Optimization Using Multivariate Modeling Techniques
Joel UrbanDirector of Quality Assurance
Brady Services, Inc.
Leah Lehman
SAS
IoT Program Director
#analyticsx
C o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
The SAS Smart Campus
Project
Joel Urban, CEM (Brady Services) and Leah Lehman, Ph.D. (SAS)
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C o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
Why Should The Market Care?
The Project
The Original Demo
The Challenges and Vision
Q&A
Outline
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C o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
Why Should The Market Care?
If you own/operate a business or other organization…
If you own/operate a building(s)…
If you care about your OPEX and CAPEX budgets…
If tenant comfort and employee productivity are important…
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Electricity is
> 2x more
expensive
than any
other energy
source!
Electricity
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Why Should The Market Care?
Nationally, the average commercial building uses 43.7% of its total energy consumption for Heating, Ventilation, and Air Conditioning (HVAC).
Approximately half of the HVAC energy consumption is for Air Conditioning (A/C).
A/C consumes electricity… a lot of expensive electricity.
[Source: US Energy Information Administration, 2012 Commercial Building Energy Consumption Survey (CBECS)]
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C o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
C o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
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C o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
Why Should The Market Care?
Everyone wants to be comfortable in their work place.
Owner/operator of a building has a budget to maximize.
Cost to operate a chiller plant is a significant portion of
the typical OPEX budget.
Cost of catastrophic equipment failure is high.
• A chiller alone can cost $25k - $250k, or more.
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Why Should The Market Care?
Potential savings from implementing a predictive maintenance program:
Return on Investment
Maintenance Costs
Equipment Breakdowns
Downtime Productivity
25-30% 70-75% 35-45% 20-25%
[Source: Operations and Maintenance Best Practices Guide. US Department of Energy]
10X
Improved Efficiency and Reduced Energy Consumption
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The Project
Make SAS World HQ
campus in Cary, NC a
Smart Campus.
Real-world example of an
IoT-enabled advanced
analytics application for
new and existing
buildings.
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The Project
Apply SAS® Visual Analytics (VA), Event Stream
Processing (ESP), Asset Performance Analytics (APA), and
other applicable software to:
1. Create algorithms to facilitate predictive maintenance
and service events.
2. Create diagnostic algorithms that identify opportunities
for optimization of building operations and controls.
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The Original Demo Bldg Q Data:
• Subset of history starting August 2014 to April 2016
• 5 or 15 minute actual data values
• 10,471 sensors (tags)
• 9 assets (e.g., AHUs, chillers, boilers, cooling towers, etc.)
• 12 events (e.g., AHU supply fan failure, chiller failure, etc.)
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The Original Demo
Dimensions:
• Standard Industrial Classification (e.g., Agriculture, Manufacturing,
Mining, etc.)
• Facility Type (e.g., Real Estate, Utilities, etc.)
• Building (C, Q)
Floor
Common Equipment
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The Original Demo
Sensor Names:
• facility name (BQ), Location code (F5), Asset (AHU-5), Control
Device name (MP581.5.1), tag name (Suppl.Fan.Speed)
Example:
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1
2
3
4
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Increase in
supply fan failures
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Explore pattern
of sensors leading up to
failure events
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Association rule mining
When this
variable is over its 95th %ile,
chances are 30% greater
for a supply fan failure
Early warnings in the sensors
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• Identify when operation is outside expected stable range
• Alert of potential problems and predict Aug 27th failure
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• Alert field of potential failure
• Implement corrective action
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The Challenges
Change Management
Demonstrate value to stakeholders
Connectivity
Data Acquisition and Quality
Data Context
Time-Series vs. Relational Data
Standardization: Tagging and Tagging (project-haystack.org)
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The Vision
Chiller Plant → Boiler Plant → Air Handlers → Lighting → Ancillary
Systems (e.g., kitchen equipment) → Solar PV
Bldg Q → Bldg C → Bldg A (new) → etc.
Historical Analytics → Real-Time Analytics → Predictive Analytics
→ Visual Analytics
Fully implement analytical platform and turn over to SAS Facility Management and Sustainability teams by 2nd Quarter 2017
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Contact Info
Joel Urban, CEM
Advanced Analytics, Project Lead
Director of Quality Assurance
Brady Services, Inc.
joel.urban@bradyservices.com
Leah Lehman, Ph.D.
Smart Campus, Project Lead
Principle Product Manager
SAS Institute, Inc.
leah.lehman@sas.com
SAS Global Forum 2017April 2-5 | Orlando, FL
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