Texas Tech University has reduced its energy use index by 37% since FY2000. The successful measures were implemented during a time frame in which the energy data infrastructure has been gradually upgraded to effect a paradigm shift from deferred maintenance to persistent commissioning. This presentation discusses analytic methods that have been in use before and after those ongoing upgrades.

1. Energy Data
and the
Transition from Deferred Maintenance
to
Persistent Commissioning

2. Texas Tech University
Lubbock, Texas
37,000 Students
100 Buildings of Significant Occupation
9,200,000 ft2
A Tier One Research University
Classified Research Very High on the Carnegie Scale

3. Texas Tech is a Tier One “Research Very High” Carnegie Class
Office of Energy Management

4. 110
120
130
140
150
160
170
180
190
200
210
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
209.0 208.0
184.5 183.3
178.8 178.5
183.3
178.0
170.5 170.9
178.2
173.5
169.0
164.4
161.0
152.2 151.8
132.4
Energy Use Index FY 2000-2017
(kbtu/ft2)
37%
EUI 112?

5. 110
120
130
140
150
160
170
180
190
200
210
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
209.0 208.0
184.5 183.3
178.8 178.5
183.3
178.0
170.5 170.9
178.2
173.5
169.0
164.4
161.0
152.2 151.8
132.4
AHU
schedules:
Point of Use
Lighting
Upgrades
AHU
VFDs
CHACP
Pump
VFDs
Free
Cooling
WSE
Freeze
Protection
Protocol
Un-
documented
Schedules
RCx 3
Buildings
eSight
EAS and
Smart
Meters
Electric
Chillers
FAC
Exceptions
Tuning
Crew
RCx 5
Buildings
CUSUM
Analysis

6. Meter Infrastructure and Manual Data Mining
Inadequate meter coverage for electricity, steam and chilled water
Monthly granularity
Dumb meters
BAS Systems: multiple, proprietary, antiquated, not really automated
Institutional knowledge not managed in a database
Stuck in a paradigm of Reactive Maintenance

7. Reactive
Maintenance
Preventative
Maintenance
Predictive
Maintenance
Reliability Centered
Maintenance
Total Productive
Maintenance
Recommissioning
Persistent
Commissioning

8. Real Time Data: What to do in the Meantime
Mine the data: Audit and Publish
Perform the Best Possible Analysis at the Limiting Granularity
Establish competency and the benefits of enhanced data acquisition
Energy Balance
Isolate Cost Drivers
Statistics– a helpful recommendation

10. Monthly EUI

11. June EUI trend
Degree Days: 438 442 380 339 395
12.47
12.30
12.36
11.78
11.60
11.00
11.20
11.40
11.60
11.80
12.00
12.20
12.40
12.60
12.80
FY 12 FY 13 FY14 FY15 FY16
June EUI
Goal: 11.81
Actual: 11.60
Predicted: 12.84

12. Energy Balance
Model 325,027$ Bills 395,548$
Lighting 50,917$ Electricity 226,357$
Plug loads 38,244$ Steam 124,990$
HVAC Electric 146,666$ Chilled Water 44,201$
Steam 45,000$ 27,750$
Chilled Water 44,201$
Electricity 235,826$
Lighting
16%
Plug loads
12%
HVAC Electric
45%
Steam
14%
Chilled Water
13%
Student Recreation Energy Costs by
Model
Lighting Plug loads HVAC Electric Steam Chilled Water
Electricity
57%
Steam
32%
Chilled Water
11%
Student Recreation Energy Costs by
Meter
Electricity Steam Chilled Water
Lighting
22%
Plug loads
16%
HVAC Electric
62%
Student Recreation ELECTRIC Costs by
Model
Lighting Plug loads HVAC Electric

13. Freeze Protection Protocol 2016
7,503,972
49,910,595
103,867,995
32,374,440
5,395,740
10,791,480
5,395,740
0
20,000,000
40,000,000
60,000,000
80,000,000
100,000,000
Oct 5 -
Oct 11
Oct 12 -
Oct 18
Oct 19 -
Oct 25
Oct 26 -
Nov 1
Nov 2 -
Nov 8
Nov 9 -
Nov 15
Nov 16 -
Nov 22
Nov 23 -
Nov 29
Nov 30 -
Dec 6
Dec 7 -
Dec 13
Dec 14 -
Dec 20
Dec 21 -
Dec 27
Dec 28 -
Jan 3
Jan 4 -
Jan 10
Jan 11 -
Jan 17
Jan 18 -
Jan 24
Jan 25 -
Jan 31
Feb 1-Feb
7
Feb 8 -
Feb 14
Feb 15 -
Feb 21
Feb 22 -
Feb 28
Mar 2 -
Mar 7
Mar 8 -
Mar 14
Mar 15 -
Mar 21
Mar 22 -
Mar 28
Freeze Protection Cost:
EUI:0.4180
Square Foot Hours: 215,239,962
$60,358
HDD thru March 21: 3236.1

14. Normalizing for Weather
Degree Day:
a unit of heat load representing the daily average number of degrees
which the outdoor temperature deviates from the indoor set point.
Usually about 65°.
Heating Degree Days, Cooling Degree Days
National Weather Service publishes them for all areas

15. Regression Analysis with Degree Days

16. CUSUM – Summer Steam
-14.00
-12.00
-10.00
-8.00
-6.00
-4.00
-2.00
0.00
2.00
CUSUM Reset to FY 14
Actual – Predicted plotted cumulatively

17. Brandon McCoy
eSight Energy Accounting System

18. Why eSight?
Function
ex: Cusum
Modularity of their offerings
Growing features as our infrastructure and competencies grow
Allowed us to start by integrating existing capable meters before investing in new meters
Incremental investments
Pay by data-point
Investment costs do not exceed initial benefits
Low cost innovation
Minimization of financial risks
Moderating our commitment
until we knew it met our needs
while overcoming technical obstacles to implementation

19. Chilled Water Malfunction -- $200,000

20. New Chilled Water Profile

21. Meter Upgrades
Immediate success with existing meters
Used prior energy savings to fund new meter installations and
upgrades

22. Meter Data Acquisition
CHW
BTU Meter
eSight
Energy Accounting
System
Steam/Condensate
BTU Meter
Domestic Water
Meter
Electric Meter
SQL/CSV
IP
Network
(BACnet/IP)
RO Meter
Gas Meter

23. Status Report
25% metered
13% integrated into the eSight Energy Accounting System
Process is ongoing
Manual data mining
Reactive maintenance
Meter installations
eSight data points

24. After Real Time Data
Diminished recommissioning effort
Repairs are being effected by eSight
Our administration is motived to avoid RCx costs
We are using this to motivate them to fund eSight and
Persistent Commissioning
Reporting opportunity costs
More responsive maintenance staff

25. Organizational Prescriptions
Financial Structure
Associate energy budgets and maintenance budgets in the same FOAP so that
energy-related maintenance expenses can be recovered by energy savings but
the funds are earmarked for energy efficiency measures.
Alignment of Incentives
Made possible by financial structure. Establish energy budgets for sub-units and
bill, or subject to highest level review.
Maintenance Strategies:
Prioritize for energy payback (without compromising the primary mission).
For performance indicators, measure against the upper bound of desired
performance, not against the backlog of deficiencies.

26. Office of Energy Management & Sustainability
For more information, please contact Lon Mirll at:
806.834.0748 or lon.mirll@ttu.edu