Prediction of Chilled Water Plant Failures and System Optimization Using Multivariate Modeling Techniques

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1 Prediction of Chilled Water Plant Failures and System Optimization Using Multivariate Modeling Techniques Joel Urban Director of Quality Assurance Brady Services, Inc. Leah Lehman IoT Program Director SAS #AnalyticsX

2 Prediction of Chilled Water Plant Failures and System Optimization Using Multivariate Modeling Techniques Joel Urban Director of Quality Assurance Brady Services, Inc. Leah Lehman IoT Program Director SAS #AnalyticsX

3 The SAS Smart Campus Project Joel Urban, CEM (Brady Services) and Leah Lehman, Ph.D. (SAS) #analyticsx

4 Outline Why Should The Market Care? The Project The Original Demo The Challenges and Vision Q&A

5 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

6 Electricity Electricity is > 2x more expensive than any other energy source!

7 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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9 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.

10 Why Should The Market Care? Potential savings from implementing a predictive maintenance program: Return on Investment Maintenance Costs Equipment Breakdowns Downtime Productivity 10X 25-30% 70-75% 35-45% 20-25% Improved Efficiency and Reduced Energy Consumption [Source: Operations and Maintenance Best Practices Guide. US Department of Energy]

11 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.

12 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.

13 Bldg Q Data: The Original Demo Subset of history starting August 2014 to April 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.)

14 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

15 Sensor Names: The Original Demo facility name (BQ), Location code (F5), Asset (AHU-5), Control Device name (MP ), tag name (Suppl.Fan.Speed) Example:

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17 Increase in supply fan failures

18 Explore pattern of sensors leading up to failure events

19 Association rule mining When this variable is over its 95 th %ile, chances are 30% greater for a supply fan failure Early warnings in the sensors

20 Identify when operation is outside expected stable range Alert of potential problems and predict Aug 27 th failure

21 Alert field of potential failure Implement corrective action

22 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)

23 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 2 nd Quarter 2017

24 SAS Global Forum 2017 April 2-5 Orlando, FL #SASGF Contact Info Joel Urban, CEM Advanced Analytics, Project Lead Director of Quality Assurance Brady Services, Inc. Leah Lehman, Ph.D. Smart Campus, Project Lead Principle Product Manager SAS Institute, Inc.

25 #AnalyticsX