Quality Assurance of 

EIA C li S t D t EIA

Cooling 

Sys

tem 

D

a

ta 

Cha-Chi FanEnergy-Water Nexus Partners MeetingMarch 22 2012

U.S. Energy Information Administration Independent Statistics & An
Collect data Collect data Review partially Review final

• Internet Data 

Collection (IDC) system edits – initial 

logical checks

Review partially

collected database

• Routine logical 

Review final

database

• Final logical checks• Statistical 

logical checks g checks examinations

Review comments and contact respondents about 

mi i d t d t i i d l ti issing data, data revisions, and explanations

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Types of quality checks Types of quality checks

• Logical checks• Statistical examinations• Other checks

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012
Logical checks

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012
IDC edits IDC edits

• Automated data filters that are programmed in the I

system

– The hours in service cannot exceed the hours in the reporting mon– Reported cooling ID was indicated as retired or out of service, whi

hours in service were non hours in service were non-zero.

– Reported monthly maximum water temperature at discharge is low

reported monthly maximum at withdrawal in a single month. Bypasif hours in service = 0

– Reported average monthly temperature at withdrawal is greater th

maximum monthly temperature at discharge

– Reported average monthly temperature at discharge is greater tha

maximum monthly temperature at discharge maximum monthly temperature at discharge

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Limitations of IDC edits Limitations of IDC edits

• Simple logic• Respondents can bypass the some edits by providin

comments 

– However comments don However, comments don t’ always address the issues always address the issues

• Not able to captures extreme values that meet the lo

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012
Problem flags Problem flags

• Logical checks that perform on the database that ar

outside of the IDC system outside of the IDC system

– Reported EIA923 withdrawal or diversion rate is higher than EIA86

withdrawal rate (for open loop system) or calculated engineer withbound (for close loop systems)

– withdrawal rate= discharge rate= consumption rate – Reported cooling ID was indicated as retired or out of service, whi

hours in service were non-zero or withdrawal/discharge/consumpt

was reported was reported.

– Reported monthly maximum water temperature at discharge is low

reported monthly maximum at withdrawal in a single month. Bypasif hours in service = 0 or if EIA860 water source = wells or municip

– Reported values exceed engineer limits based on EIA860 net cap

and design withdrawal water flow rate (cfs)

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Problem flags Problem flags

• Exceeding engineer limits based on EIA860 net cap

(MW) and design withdrawal water flow rate ( (MW) and design withdrawal water flow rate (cfs)

Withdrawal rate limits Lower limit factor 

(cfs/MW)

Upper limit f(cfs/MW)

Once through 0.75 1.25Cooling tower-wet 0.09 0.12Cooling pond Cooling pond 0 75 . 1 25 .

Discharge rate limits Lower limit factor 

(cfs/cfs)

Upper limit f(cfs/cfs)

Once through 0.98 1Cooling tower-wet 0.002 0.004Cooling pond 0 002 0 004

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 

Cooling pond 0.002 0.004
Problem flags Problem flags

• Exceeding engineer limits based on EIA860 net cap

(MW) and design withdrawal water flow rate ( (MW) and design withdrawal water flow rate (cfs)

Consumption rate limits

Lower limit factor (cfs/cfs)

Upper limit f(cfs/cfs)

Once through 0 0.02Cooling tower-wet 0.02 0.03Cooling pond 0 01 0 02

Annual amount of chloride limits

Lower limit factor (1000 lbs/MW)

Upper limit f(1000 lbs/MW

Cooling pond 0.01 0.02

Once through 0 0.81Cooling tower-wet 0 0.30Cooling pond 0 0 31

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 

Cooling pond 0 0.31
A screen shot of automated water data summary at a cooling system lev

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Limitations of problem flags Limitations of problem flags

• Could the reported values be in different units or co

decimal points be misplaced? decimal points be misplaced?

• Could the reported values be aggregates at a plant 

agg g g y re

gates of different coolin

g s

ystems?

• The logical checks are general, not specific to spec

desi

gns

• Not able to captures extreme values that meet the lo

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Statistical examinations

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Types of examinations Types of examinations

Univariate Bivariate Multiva

Histogram / box plot Scatter plot Cluster 

Percentile curve 

analysis Cluster analysis Data m

Statistical tests (e.g. 

generalized extreme 

Studentized

deviate)

Correlation Principal c

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 

deviate)
Example 1: Univariate/bivariate

Average withdrawal temperature vs. average discharge temperature

F)ture (degree arge temperawater disch

Avg water withdrawal temperature (degree F)

Avgw

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 

g p (g )
Example 2: Univa

Withdrawal rate v

Withdrawal rate (cfs) / discharge rate (cfs)

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 

Withdrawal rate (cfs) / discharge rate (cfs)
350

400450500

cfs) / 

(cfs)

Example 2: UnivaWithdrawal rate v

150200250300350

drawal rate(

charge rate (

0

50

100

150

0% 50% 100%

Withd

disc

300

/

Percentile

200250

rate(cfs) /

rate (cfs)

100150

hdrawal r

scharge r

0

50

90% 92% 94% 9

Wit

dis

90% 92% 94% 9

Percent

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Example 3: Bivariat

Average withdrawaaverage discharge 

degree F)

ee F)

harge temp (

e temp (degre

vg water disch

Avg water withdrawal temp (degree F)

ter discharge

Av

Avg water withdrawal temp (degree F)

A t ithd 

Avg wat

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 

Avg water withdrawa
Example 4: Bivaria

Withdrawal rate vs

(cfs) drawal rate With

al rate (cfs)

Chl i t (1000 lb )

Withdraw

Chlorine amount (1000 lbs)

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 

Chlorine amo
Example 5: Multivariate cluster, water rates, temperatures, chlorine amo

Chlorine amount (1000lbs)

Withdrawal rate (cfs)

Discharge rate (cfs)

Consumption rate (cfs)

Avg water withdrawal temperature (degree F)

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Example 6: Data mining – clustering suggestions

Chlorine amount vs. cooling type

17.5

17.0

t (1000 lbs) 16 5.

16.0

orine amount

15.5

Chl

15.0

DC

HRI

ltiple

OC

OF

OS

OT

RC

RF

RI

RN

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 

D

H

mu

O

O

O

O

R

Cooling system types
Example 6: Data mining – clustering suggestions

Difference in max withdrawal and discharge temperature vs. water sourc

-15

p (degree F)

-16

17

scharge temp

-

r

-18

awal and dis

-19

n max withdr

GW

OT

PD

SW

Diff in

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 

G

S

Cooling water sources
Example 6: Data mining – examine relationships

Diversion rate vs. consumption rate

700

600

500

te (cfs)

400

Diversion ra

300

200

0

500

000

500

000

500

000

100

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 

5

10

15

20

25

30

Consumption rate (cfs)
Examinations by clusters/groups Examinations by clusters/groups

• Latitude• Cooling system type• Cooling water source• Cooling water type • M th on• Cooling status• State

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Difficulties

• Where the measurements were taken matters• Some plants report combined values without indications• Withdrawal and discharge of cooling pond system do not 

necessaril h i th th ily happen in the same month

• Respondents mistake withdrawal rate with tower water flo• Abnormal distribution doesn’t necessarily mean bad data• There is no one to one mapping of boilers and cooling sy• There are all kinds of combinations of cooling systems an

things work is oftentimes beyond imagination

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Other checks Other checks

• Inputs and comments from data users

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012 
Quality Assurance of 

EIA C li S t D t EIA

Cooling 

Sys

tem 

D

a

ta 

Cha-Chi Fanchachi.fan@eia.gov

U.S. Energy Information Administration Independent Statistics & An
Problem flags Problem flags

• Reported EIA923 withdrawal or diversion rate is hig

EIA860 design withdrawal rate (for open loop systemEIA860 design withdrawal rate (for open loop system

calculated engineer withdrawal upper bound (for closystems)

– Once through/hybrid: 923 withdrawal rate vs 860 design cooling w

Once through/hybrid: 923 withdrawal rate vs. 860 design cooling w

– Cooling pond: 923 diversion rate vs. 860 design cooling water flow – Cooling pond: 923 withdrawal rate vs. calculated engineer withdra

bound

– Wet cooling tower: 923 withdrawal rate vs. calculated engineer wit

upper bound

Cha-Chi Fan, Energy-Water Nexus Partners Meeting, March 22 2012