Download - Prediction of Chilled Water Plant Failures and System · PDF filePrediction of Chilled Water Plant Failures and System Optimization Using Multivariate Modeling Techniques Joel Urban

#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)


#analyticsx


Why Should The Market Care?

The Project

The Original Demo

The Challenges and Vision

Q&A

Outline


#analyticsx



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…


#analyticsx


Electricity is

> 2x more

expensive

than any

other energy

source!

Electricity


#analyticsx



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)]


#analyticsx



#analyticsx



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.


#analyticsx



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


#analyticsx


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.


#analyticsx


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.


#analyticsx


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.)


#analyticsx


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


#analyticsx


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:


#analyticsx


1

2

3

4


#analyticsx


Increase in

supply fan failures


#analyticsx


Explore pattern

of sensors leading up to

failure events


#analyticsx


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


#analyticsx


• Identify when operation is outside expected stable range

• Alert of potential problems and predict Aug 27th failure


#analyticsx


• Alert field of potential failure

• Implement corrective action


#analyticsx


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)


#analyticsx


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


#analyticsx


Contact Info

Joel Urban, CEM

Advanced Analytics, Project Lead

Director of Quality Assurance

Brady Services, Inc.

[email protected]

Leah Lehman, Ph.D.

Smart Campus, Project Lead

Principle Product Manager

SAS Institute, Inc.

[email protected]

SAS Global Forum 2017April 2-5 | Orlando, FL

#SASGF

mailto:[email protected]

mailto:[email protected]


#AnalyticsX