Srinivas Katipamula, Ph.D. - cacx.org · Srinivas Katipamula, Ph.D. Staff Scientist, Battelle...
Transcript of Srinivas Katipamula, Ph.D. - cacx.org · Srinivas Katipamula, Ph.D. Staff Scientist, Battelle...
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Analyzing Interval Data Using the Energy Charting and Metrics (ECAM) Tool
1PNWD-SA-9511
Adapted from a presentation by:
Srinivas Katipamula, Ph.D.Staff Scientist, Battelle Pacific Northwest Division
Bill Koran, P.E.Senior Engineer, Quantum Energy Services and Technologies
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Presentation Outline
• ECAM Capabilities
• Analysis of Interval Meter Data with ECAM
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• A tool to assist with analysis of building performance data
• Facilitate the types of analyses that energy engineers, commissioning providers, and building operators might need to make
• “Semi-automation,” not complete automation of the analyses
What is ECAM?
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• Because analyses is not completely automated, the tool is flexible, supporting additional analysis without limiting any normal spreadsheet functions
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ECAM Version 2.0 New Features
• Excel 2007/2010 compatibility
• Full time history chart, filterable to selected months or days
• Load profile as box plots
• Load duration chart (histograms/frequency distributions)
• Chart to check input schedule
• Matrix charts
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• Matrix charts
• Metrics by daytype, occupancy, and month
• Data summaries
• Additional scheduling periods for startup and shutdown
• Integrated user guide/help
• PNNL re-tuning charts (See http://buildingefficiency.labworks.org/large.stm for more details)
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ECAM Detection/Diagnostic Features
• Detection
– Tool does not provide detection directly
– Assists with performance tracking
– Provides the ability to investigate the relationships between variables
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between variables
• Diagnostics
– Tool does not provide diagnostics
– Future versions may provide some limited diagnostic capabilities, but this is not the focus of the tool
– General intent is to maximize the user’s ability to benefit from whatever data is available
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Five Easy Steps
1. Select data from existing spreadsheet
2. Map points
3. Create schedules
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3. Create schedules (optional)
4. Input energy project dates (optional)
5. Create metrics and charts
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User’s Data — Example
DateTime
Outside
Dry Bulb
325
COMMON
CHWS
326
COMMON
CHWR
328
CHILLER
1 AMPS
342 COM
CDW
SUPPLY
343 COM
CDW
RETURN
345 A
CHILLER
ON
414
SCHWP-5
KW
415
SCHWP-6
KW
416
SCHWP-7
KW
8/23/04 12:00 AM 67.4 41.9 48.5 226.1 69.2 77.3 100 0 5 0.8
8/23/04 12:15 AM 67.5 42 48.7 224.3 69 77.1 100 0 4.8 1.3
8/23/04 12:30 AM 67.3 41.6 47.6 206.9 68.9 76.3 100 0 4.7 1.6
8/23/04 12:45 AM 67.1 41.8 47.7 206.7 68.5 75.8 100 0 4.6 1.3
8/23/04 1:00 AM 67.1 42.2 47.6 195.9 68.2 75 100 0 4.7 1.4
8/23/04 1:15 AM 67 41.8 47 193.8 68.1 74.8 100 0 4.6 0
8/23/04 1:30 AM 67 42 46.2 168.1 67.6 72.9 100 0 4.7 0
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8/23/04 1:45 AM 66.8 41.9 45.3 152.1 67.5 71.9 100 0 4.6 0.6
8/23/04 2:00 AM 67 42.2 45.3 150.2 67.8 71.9 100 0 4.6 0.6
8/23/04 2:15 AM 67.1 42 48.5 0 67.6 74.6 100 0 4.8 0.9
8/23/04 2:30 AM 67.1 41.6 47.7 0 68.8 75.7 100 0 4.5 0.5
8/23/04 2:45 AM 67 42 48.4 0 68.2 75.1 100 0 4.7 0.8
8/23/04 3:00 AM 67.1 41.6 47.5 0 68.4 75.1 100 0 4.5 0.9
8/23/04 3:15 AM 67.2 41.7 47.5 0 68.3 74.8 100 0 4.7 0.8
8/23/04 3:30 AM 67.4 42.4 47.9 0 68.2 74.4 100 0 4.6 0.8
8/23/04 3:45 AM 67.4 43.5 48.6 0 68.3 73.8 100 0 4.7 0.9
8/23/04 4:00 AM 67.5 45 49.4 0 68.1 73 100 0 4.8 0
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Step 1: Select data
1a. 1b.
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1c. 1d.
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Step 2: Map points
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User’s points are mapped to common point names as perUser’s points are mapped to common point names as per
A Specifications Guide for Performance Monitoring SystemsA Specifications Guide for Performance Monitoring Systems
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Step 3: Create Schedule (optional)
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Forms are similar to eQuestForms are similar to eQuest
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Step 4: Input Energy Project Dates (optional)
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Step 5: Create Metrics or Charts
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Preprocessing of Data
• Time stamp disaggregation
• Daytyping
• Occupancy
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• Pre/Post Energy Project
• Normalizations
• Calculations
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ECAM Metrics Creation Capabilities
• Can be normalized:
– Building area (e.g. W/sqft)
– Cooling tons (e.g. kW/ton)
– cfm (e.g. Watts/cfm)
• Can be filtered by:
– Year
– Month
– Pre/post time periods
In ECAM, metrics are typically data In ECAM, metrics are typically data averaged over a particular time periodaveraged over a particular time period
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– cfm (e.g. Watts/cfm)
– gpm (e.g. gpm/ton)– Pre/post time periods
– Daytype
– Time of day
– Occupancy
– Weather conditions
– Status of equipment
– CombinationsAll filters and many normalizations All filters and many normalizations are automatically set up by ECAMare automatically set up by ECAM
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Automatic Creation Of Additional Fields For Metrics
• Equipment Status
• Demand (kW)
• Chilled water tons
• from demand (kW) or
current (amps)
• from current (amps)
approximate calculation
• whenever flows and
temperatures are available
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• Watts per square foot
• CFM per square foot
• kW per ton
• gpm per ton
temperatures are available
• for all electrical demand
points (kW)
• for all air flow points
• for all related points
• for all related points
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Interval Data Visualization
• Load profiles
– Raw data
– Averages
– Pre/post
– By daytype
– Calendars
• Scatter charts
– Raw data
– Averages
– Pre/post
– By daytype
– By occupancy
16
– Calendars – By occupancy
– By time of day
– Choice of independent variable
– Multiple points
Filtering capabilities are providedfor all types of charts
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Load Profile by Daytype
2.5
3.0
3.5
4.0
4.5
Avg
Ele
cM
tr_W
att
s_p
erS
F
Weekday
Year (All)
Month (All)
MonthYr Sep 2006
Weekday (All)
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0.0
0.5
1.0
1.5
2.0
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Avg
Ele
cM
tr_W
att
s_p
erS
F
Saturday
Sunday
Holiday
Weekday (All)
Day (All)
Holiday (All)
5degBin (All)
1degBin (All)
TempRng (All)
Filtering Options
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Calendar Load Profile
October
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
1 2
3 4 5 6 7 8 9
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
6
8
6
8
6
8
6
8
6
8
6
8
6
8
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10 11 12 13 14 15 16
17 18 19 20 21 22 23
24 25 26 27 28 29 30
31
0
2
4
0
2
4
0
2
4
0
2
4
0
2
4
0
2
4
0
2
4
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
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3-D Load Profile
7/1/20067/2/2006
7/3/20067/4/2006
7/5/20067/6/2006
7/7/20067/8/2006
7/9/20067/10/2006
7/11/20067/12/2006
7/13/20067/14/2006
7/15/20067/16/2006
7/17/2006
7/18/2006
7/19/2006
7/20/2006
7/21/20064.00
5.00
12:00 AM
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12:00
AM3:00
AM6:00
AM9:00
AM12:00
PM3:00
PM6:00
PM9:00
PM
7/21/2006
7/22/2006
7/23/2006
7/24/2006
7/25/2006
7/26/2006
7/27/2006
7/28/2006
7/29/2006
7/30/2006
7/31/2006
0.00
1.00
2.00
3.0012:00 AM
2:00 AM
4:00 AM
6:00 AM
8:00 AM
10:00 AM
12:00 PM
2:00 PM
4:00 PM
6:00 PM
8:00 PM
10:00 PM
7/1
/20
06
7/2
/20
06
7/3
/20
06
7/4
/20
06
7/5
/20
06
7/6
/20
06
7/7
/20
06
7/8
/20
06
7/9
/20
06
7/1
0/2
00
6
7/1
1/2
00
6
7/1
2/2
00
6
7/1
3/2
00
6
7/1
4/2
00
6
7/1
5/2
00
6
7/1
6/2
00
6
7/1
7/2
00
6
7/1
8/2
00
6
7/1
9/2
00
6
7/2
0/2
00
6
7/2
1/2
00
6
7/2
2/2
00
6
7/2
3/2
00
6
7/2
4/2
00
6
7/2
5/2
00
6
7/2
6/2
00
6
7/2
7/2
00
6
7/2
8/2
00
6
7/2
9/2
00
6
7/3
0/2
00
6
7/3
1/2
00
6
4.69-5.00
4.38-4.69
4.06-4.38
3.75-4.06
3.44-3.75
3.13-3.44
2.81-3.13
2.50-2.81
2.19-2.50
1.88-2.19
1.56-1.88
1.25-1.56
0.94-1.25
0.63-0.94
0.31-0.63
0.00-0.31Surface Chart
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Box Plot Load Profile
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3.0
4.0
5.0
6.0
Avg
Ele
cM
tr_W
att
s_p
erS
F
During Changes
Before 11-15-06
After 1-24-07
Scatter Chart by Date Range
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0.0
1.0
2.0
0 20 40 60 80 100 120
AHU_TempOa
Avg
Ele
cM
tr_W
att
s_p
erS
F
Excel won’t create
Scatter Charts based on
PivotTables (PivotCharts).
ECAM includes
Scatter Charts based on
PivotTables.
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Scatter Chart by Occupancywith Binned Weather
800
1,000
1,200
Av
g E
lecM
tr_k
W
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0
200
400
600
20 30 40 50 60 70 80 90 100
1degBin
Av
g E
lecM
tr_k
W
Occ
Unocc
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INTERVAL METER DATA ANALYSIS EXAMPLES
23
ANALYSIS EXAMPLES
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Example: Load Profile by Daytype
24 Hour Occupied
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24 Hour Occupied Schedule
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Example: Load Profile by Daytype
Morning
Building occupied
(6 AM)
Unoccupied/ shutdown
•
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Morning startup
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Example: Typical Office Building
50% load reduction
between peak and nighttime
Long shutdown
period
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5 day occupied schedule and unoccupied on
weekends
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Example: Good Office Building
80% Peak Reduction
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Good startup/shutdown
(6 AM/6 PM)
Building not occupied on weekend
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Example: Comparing data before and after improvements
50% reduction
15% Peak load reduction
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Additional 30% reduction at night
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Comparing Consumption from Two Identical Buildings
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Comparing Regression Analysis from Two Identical Buildings
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