Can High Bill Alerts Help Utility Customers to Save Energy? · 2019-08-02 · • Reduce customer...

Ryan Fulleman

Can High Bill Alerts Help Utility Customers to Save Energy?

IEPEC Denver, COAugust 20, 2019

IEPEC, 2019 Denver

Agenda

HBA Pilot Evaluation ApproachFindingsTakeaways and Future Research

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IEPEC, 2019 Denver

HBA Pilot Overview

• Xcel Energy sought to reduce call center call volumes• Many residential customers call about higher-than-expected bills• Reduce customer service costs

• High Bill Alert Pilot Program• 50,000 MN residential dual-fuel customers automatically enrolled• Email alert sent when customer’s monthly electricity or gas consumption

was on track to exceed normal levels• Alerts sent June 2015 - June 2016 • HER customers excluded• Implemented by Opower (Oracle)

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IEPEC, 2019 Denver

High Bill Alert Email

4

Bill alert when spending on track to be 30% higher than normal

Recipient directed to Xcel Energy’s on-line Home Energy Assessment website

Customers can opt out

Features

Xcel Energy branding

IEPEC, 2019 Denver

High Bill Alert Delivery

5

Electric HBAs Gas HBAs

• 59% of HBA emails were opened by recipient• 6.4% of HBA emails resulted in clicking the Xcel Energy on-line energy audit link

FuelNumber of

Customers Sent HBAs

Number of HBAs Sent

Average Number of HBAs per Treated

CustomerElectricity 31,591 100,320 3.2Natural Gas 6,163 13,355 2.2

Sheet1

Electricity Savings

Phase 1 (June 2015 – May 2016)Phase 2 (June 2016 – April 2018)

Model 1Model 2Model 1Model 2Phase 1Phase 2

Average Daily Savings per Customer (kWh)0.0770.0770.1330.134% Savings0.4%0.6%

(0.043)(0.043)(0.057)(0.057)kWh Savings0.0770.134

Customer Fixed EffectsYesYesYesYesBaseline21.388888888923.1034482759

Weather VariablesNoYesNoYes

Month-Year Fixed EffectsYesYesYesYes% Savings Error Margin0.33%0.41%

Number of Customers75,26775,26775,26775,267kWh Savings Error Margin0.06991250.0939295

Number of Observations2,689,1922,689,1922,689,1922,689,192

Notes: The dependent variable is customer electricity ADC for a month-year. All models are linear regression models. Standard errors in parentheses were clustered by customer. Cadmus estimated all models with pre-treatment and post-treatment monthly consumption data.

Table 7. Natural Gas Regression Estimates



Average Daily Savings per Customer (therms)0.00910.0090.00980.0097% Savings0.5%0.5%

(0.004)(0.004)(0.004)(0.004)kWh Savings0.0090.0097






Notes: The dependent variable is customer natural gas ADC for a month-year. All models are linear regression models. Standard errors in parentheses were clustered by customer. Cadmus estimated all models with pre-treatment and post-treatment monthly consumption data.

% Savings

3.2686363636363638E-34.0656052238805963E-33.2686363636363638E-34.0656052238805963E-3Phase 1Phase 23.5999999999999999E-35.7999999999999996E-3

kWh Savings

[VALUE]

6.9912500000000002E-29.3929499999999999E-26.9912500000000002E-29.3929499999999999E-2Phase 1Phase 27.6999999999999999E-20.13400000000000001

Avg. Daily Savings per Customer

% Savings


kWh Savings



Sheet2

FuelNumber of Customers Sent HBAsNumber of HBAs SentAverage Number of HBAs per Treated Customer

Electricity31,591100,3203.2

Natural Gas6,16313,3552.2

Fuel

Number of Customers Sent High Bill Alerts

Total Number of High Bill Alerts Sent

Average Number of High Bill Alerts per Treatment Group Customer

Electricity

31,591

100,320

3.2

Natural Gas

6,163

13,355

2.2

Fuel

Number of Customers

Sent High Bill Alerts

Total Number of High Bill

Alerts Sent

Average Number of High Bill Alerts

per Treatment Group Customer

Electricity 31,591 100,320 3.2

Natural Gas 6,163 13,355 2.2

IEPEC, 2019 Denver

Research Objectives

• No statistically significant effect on customer call center call volumes (Source: Opower/Oracle analysis)

• Cadmus research objective • Estimate customer gas and electricity savings during and after the pilot

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IEPEC, 2019 Denver


7

Agenda

IEPEC, 2019 Denver

Evaluation Approach

• Pilot implemented as an RCT• 50,000 treatment group customers and 25,000 control group customers• Only treatment group customers eligible to receive HBAs

• Monthly billing consumption data• Validation of randomized experiment• Fixed-effects D-in-D panel regression • Estimated savings for two phases:

• Phase 1: HBAs delivered (June 2015 - June 2016) • Phase 2: HBA delivery suspended (July 2016 – April 2018)

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IEPEC, 2019 Denver


9

Agenda

IEPEC, 2019 Denver

Electricity Savings Estimates

10

Percentage SavingskWh Savings

Note: Error bars indicated 90% confidence intervals based on standard errors clustered on customers.

• Electricity savings persisted after Xcel Energy stopped sending HBAs• In Phase 2, electricity savings per customer who received an HBA

equaled 0.16 kWh (0.7%)

0.4%

0.6%

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

Phase 1 Phase 2

0.08

0.13

0.00

0.05

0.10

0.15

0.20

0.25

Phase 1 Phase 2

Avg.

Dai

ly Sa

ving

s per

Cus

tom

er

Chart9

% Savings


Sheet1

Electricity Savings




(0.043)(0.043)(0.057)(0.057)kWh Savings0.0770.134











(0.004)(0.004)(0.004)(0.004)kWh Savings0.0090.0097







% Savings


kWh Savings

[VALUE]



% Savings


kWh Savings



Chart10

kWh Savings

[VALUE]



Sheet1

Electricity Savings




(0.043)(0.043)(0.057)(0.057)kWh Savings0.0770.134











(0.004)(0.004)(0.004)(0.004)kWh Savings0.0090.0097







% Savings


kWh Savings

[VALUE]



% Savings


kWh Savings



IEPEC, 2019 Denver

Monthly Electricity Savings Estimates

11

Note: Error bars indicate 90% confidence intervals based on standard errors clustered on customers.

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Avg

Dai

ly S

avin

gs p

er C

usto

mer

(kW

h)

Estimated Savings 90% Lower Savings Estimate 90% Upper Savings Estimate

Phase 1: HBA treatment period

Chart1

Estimated Savings4215642186422174224842278423094233942370424014243042461424914252242552425834261442644426754270542736427674279542826428564288742917429484297943009430404307043101431324316043191-4.9500000000000002E-21.67E-27.1400000000000005E-29.3799999999999994E-28.1000000000000003E-23.7199999999999997E-29.9500000000000005E-20.131000000000000010.115500000000000014.1099999999999998E-25.1400000000000001E-20.168199999999999990.1510.117000000000000010.108299999999999997.3599999999999999E-23.6799999999999999E-29.3799999999999994E-20.250099999999999990.231399999999999990.14097.2099999999999997E-28.6E-30.043.9600000000000003E-20.123799999999999990.143499999999999990.174499999999999998.0799999999999997E-20.16730.266500000000000010.295599999999999970.260699999999999990.123799999999999997.9200000000000007E-290% Lower Savings Estimate4215642186422174224842278423094233942370424014243042461424914252242552425834261442644426754270542736427674279542826428564288742917429484297943009430404307043101431324316043191-0.15494450000000001-0.11884800000000001-5.5429500000000007E-2-9.8350000000000104E-3-6.8430000000000019E-3-5.1959000000000005E-2-1.0715000000000002E-2-1.916000000000001E-3-5.5719999999999936E-3-6.0232000000000008E-2-4.4503500000000001E-26.6045499999999979E-21.2491000000000002E-2-5.029649999999998E-2-4.8632999999999996E-2-5.0268500000000008E-2-7.4073E-2-1.7566499999999999E-29.8430999999999991E-27.3644500000000002E-23.8714999999999999E-3-5.4893999999999998E-2-0.10753699999999999-7.7453000000000022E-2-0.10993049999999999-5.1886000000000015E-2-1.2610499999999997E-23.22075E-2-4.2904000000000012E-22.7968500000000007E-28.7853000000000014E-29.3593999999999955E-26.8234999999999962E-2-3.9548500000000014E-2-6.358599999999999E-290% Upper Savings Estimate42156421864221742248422784230942339423704240142430424614249142522425524258342614426444267542705427364276742795428264285642887429174294842979430094304043070431014313243160431915.5944500000000008E-20.152247999999999990.19822950.1974350.168843000000000020.1263590.209715000000000010.263916000000000040.2365720.1424320.14730350.27035450.289509000000000020.284296500000000010.2652330.197468499999999990.1476730.205166499999999970.401768999999999990.389155499999999990.277928500000000020.199093999999999990.124736999999999990.157453000000000010.189130499999999980.299486000000000030.29961050.316792499999999980.204504000000000020.30663150.445147000000000010.497605999999999990.453165000000000040.287148500000000030.22198600000000002

Avg Daily Savings per Customer (kWh)

Treatment Estimates

util_typePeriodmodel_typemodelMOdel_nParmEstimateStderrProbZ

Epost1DNDE_DND_SIMPLE1treatment_post1_norm-0.44840.04320.0001

Epost1DNDE_DND_OVERALL2treatment_post1_norm-0.04730.04260.2678

Epost1DNDE_DND_OVERALL_WEATHER3treatment_post1_norm-0.04730.04260.2674

Epost1DNDE_DND_OVERALL_HDD4treatment_post1_norm-0.0470.04260.2704

Epost1PO_E_PO_SIMPLE_NO_CTRL1treatment_post1-1.0990.1270.0001

Epost1PO_E_PO_SIMPLE2treatment_post1-0.53950.04720.0001

Epost1PO_E_PO_SIMPLE_PREINTERACTED3treatment_post1-0.31130.0490.0001

Epost1PO_E_PO_AVGPRENOINT4treatment_post1-0.0130.04290.7624

Epost1PO_E_PO_OVERALL5treatment_post1-0.00120.0450.978

Epost1PO_E_PO_OVERALL_WEATHER6treatment_post1-0.0010.0450.9822

Epost1PO_E_PO_OVERALL_HDD7treatment_post1-0.00120.0450.9793

Epost2DNDE_DND_SIMPLE1treatment_post2_norm0.29140.04870.0001




Epost2PO_E_PO_SIMPLE_NO_CTRL1treatment_post20.39520.13030.0024

Epost2PO_E_PO_SIMPLE2treatment_post20.10990.05330.0393

Epost2PO_E_PO_SIMPLE_PREINTERACTED3treatment_post20.14960.05470.0062





Gpost1DNDG_DND_SIMPLE1treatment_post1_norm-0.16240.00430.0001

Gpost1DNDG_DND_OVERALL2treatment_post1_norm-0.00920.00360.0106

Gpost1DNDG_DND_OVERALL_WEATHER3treatment_post1_norm-0.00910.00360.0119

Gpost1DNDG_DND_OVERALL_HDD4treatment_post1_norm-0.00910.00360.0113

Gpost1PO_G_PO_SIMPLE_NO_CTRL1treatment_post1-0.15220.01110.0001

Gpost1PO_G_PO_SIMPLE2treatment_post1-0.12280.01080.0001

Gpost1PO_G_PO_SIMPLE_PREINTERACTED3treatment_post1-0.18220.0110.0001

Gpost1PO_G_PO_AVGPRENOINT4treatment_post10.00530.00990.5961

Gpost1PO_G_PO_OVERALL5treatment_post10.00430.010.6636

Gpost1PO_G_PO_OVERALL_WEATHER6treatment_post10.00430.010.6641

Gpost1PO_G_PO_OVERALL_HDD7treatment_post10.00440.010.6631

Gpost2DNDG_DND_SIMPLE1treatment_post2_norm0.06170.00440.0001




Gpost2PO_G_PO_SIMPLE_NO_CTRL1treatment_post20.18410.01160.0001

Gpost2PO_G_PO_SIMPLE2treatment_post20.16880.01130.0001

Gpost2PO_G_PO_SIMPLE_PREINTERACTED3treatment_post20.13480.01150.0001





Elec Means

E Pre ADC ttest both flags

The TTEST Procedure

Variable: adc

groupNMeanStd DevStd ErrMinimumMaximumgroupMethodMean90% CL MeanStd Dev90% CL Std Dev

CONTROL2391120.180214.47630.09360378.3CONTROL20.180220.02620.33414.476314.368314.5861

TREATMENT4793120.165913.66490.06240437.5GroupAverage Daily Electric Usage (kWh)Difference (Phase 1-Pre)Difference (Phase 2-Pre)TREATMENT20.165920.06320.26913.664913.592713.7379

Diff (1-2)0.014313.94020.1104PrePhase 1Phase 2Diff (1-2)Pooled0.0143-0.16720.195913.940213.8814.001

groupMethodMean90% CL MeanStd Dev90% CL Std DevControl20.18021.38823.0401.2072.860Diff (1-2)Satterthwaite0.0143-0.17080.1994

CONTROL20.180220.026220.334214.476314.368314.5861Treatment20.16621.28322.8831.1172.717

TREATMENT20.165920.063320.268613.664913.592713.7379Difference (T-C)-0.014-0.104-0.157-0.090-0.143

Diff (1-2)Pooled0.0143-0.16720.195913.940213.8814.001

Diff (1-2)Satterthwaite0.0143-0.17080.1994

MethodVariancesDFt ValuePr > |t|

PooledEqual718400.130.89680.6458333333

SatterthwaiteUnequal454140.130.8988

Equality of Variances

MethodNum DFDen DFF ValuePr > F0.36%0.58%000

Folded F23910479301.12 |t|

PooledEqual680340.920.3583



MethodNum DFDen DFF ValuePr > F

Folded F22537454971.14 |t|





Folded F17920358461.18 |t|

PooledEqual58706-1.050.2956

SatterthwaiteUnequal38315-1.040.3



Folded F19606391001.06 |t|Diff (1-2)Satterthwaite-0.0141-0.03290.00478


SatterthwaiteUnequal51289-1.230.21970.54%0.48%



Folded F25856517671.020.099

G Post1 ADC ttest both flags

The TTEST Procedure

Variable: adc

groupNMeanStd DevStd ErrMinimumMaximum

CONTROL247761.67741.24290.0079020.6695

TREATMENT496491.67791.2130.00544020.6873

Diff (1-2)-0.000541.2230.00951groupMethodMean90% CL MeanStd Dev90% CL Std Dev

groupMethodMean90% CL MeanStd Dev90% CL Std DevCONTROL1.67741.66441.69041.24291.23381.2522

CONTROL1.67741.66441.69041.24291.23381.2522TREATMENT1.67791.66891.68691.2131.20671.2194

TREATMENT1.67791.66891.68691.2131.20671.2194Diff (1-2)Pooled-0.00054-0.01620.01511.2231.21781.2283

Diff (1-2)Pooled-0.00054-0.01620.01511.2231.21781.2283Diff (1-2)Satterthwaite-0.00054-0.01630.0152

Diff (1-2)Satterthwaite-0.00054-0.01630.0152






Folded F24775496481.05 |t|





Folded F19606391001.06 |Z|

Error

Intercept0000..

treatment_post1_norm0.07730.0425-0.16070.0061-1.820.06940.00738750.1472125

treatment_post2_norm0.13360.0571-0.2455-0.0218-2.340.01920.03967050.2275295

yr2014_mo6_norm3.06640.10112.86823.264630.32

IEPEC, 2019 Denver

Gas Savings

12

Percentage Savings

0.0090.010

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

Phase 1 Phase 2

Avg.

Dai

ly S

avin

gs p

er C

usto

mer

0.5% 0.5%

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

Phase 1 Phase 2

Therm Savings

Note: Error bars indicated 90% confidence intervals based on standard errors clustered on customers.

• Gas savings persisted after Xcel Energy stopped sending HBAs• In Phase 2, gas savings per customer who received an HBA equaled

0.011 therms (0.6%)

IEPEC, 2019 Denver

Monthly Gas Savings Estimates

13

Note: Error bars indicate 90% confidence intervals based on standard errors clustered on customers.

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

Aver

age

Dai

ly S

avin

gs p

er C

usto

mer

(the

rms)

Estimated Savings 90% Lower Savings Estimate 90% Upper Savings Estimate

Phase 1: HBA treatment period

Chart2

Estimated Savings42156421864221742248422784230942339423704240142430424614249142522425524258342614426444267542705427364276742795428264285642887429174294842979430094304043070431014313243160431918.6999999999999994E-36.7999999999999996E-37.4999999999999997E-37.6E-31.26E-21.41E-21.06E-24.1000000000000003E-33.7000000000000002E-38.0999999999999996E-31.0999999999999999E-21.21E-27.4000000000000003E-36.0000000000000001E-31.8E-35.1999999999999998E-38.8999999999999999E-39.5999999999999992E-31.37E-21.2E-29.1000000000000004E-31.0800000000000001E-29.5999999999999992E-31.2800000000000001E-27.9000000000000008E-34.0000000000000001E-31.11E-21.52E-21.43E-21.4500000000000001E-21.61E-21.6500000000000001E-22.3E-31.17E-21.01E-290% Lower Savings Estimate4215642186422174224842278423094233942370424014243042461424914252242552425834261442644426754270542736427674279542826428564288742917429484297943009430404307043101431324316043191-6.4340000000000005E-3-8.992E-3-8.6210000000000002E-3-7.6984999999999996E-31.5785E-36.5329999999999997E-3-3.876000000000001E-3-1.90945E-2-1.3407999999999998E-2-4.5399999999999954E-42.7749999999999997E-3-1.0600000000000002E-3-9.049999999999999E-3-1.09435E-2-1.5307999999999999E-2-1.1085500000000002E-2-3.1085000000000002E-35.5249999999999917E-4-9.4944999999999995E-3-1.2017E-2-7.8434999999999998E-3-1.5374999999999989E-3-2.7000000000000114E-4-1.0179999999999981E-3-9.3724999999999989E-3-1.4095E-2-7.4884999999999969E-3-2.2370000000000011E-33.4429999999999999E-3-4.6949999999999943E-4-1.1207000000000002E-2-1.7715999999999996E-2-2.6322999999999999E-2-5.4079999999999979E-3-7.5700000000000073E-490% Upper Savings Estimate42156421864221742248422784230942339423704240142430424614249142522425524258342614426444267542705427364276742795428264285642887429174294842979430094304043070431014313243160431912.3834000000000001E-22.2592000000000001E-22.3621E-22.2898499999999999E-22.36215E-22.1666999999999999E-22.5076000000000001E-22.7294499999999999E-22.0808E-21.6653999999999999E-21.9224999999999999E-22.5259999999999998E-22.385E-22.2943499999999999E-21.8907999999999998E-22.1485500000000001E-22.09085E-21.8647499999999997E-23.6894499999999997E-23.6017E-22.6043500000000001E-22.3137499999999998E-21.9470000000000001E-22.6617999999999999E-22.51725E-22.2095E-22.96885E-23.2636999999999999E-22.5156999999999999E-22.9469500000000003E-24.3407000000000001E-25.0715999999999997E-23.0922999999999999E-22.8808E-22.0957E-2

Average Daily Savings per Customer (therms)

Treatment Estimates

util_typePeriodmodel_typemodelMOdel_nParmEstimateStderrProbZ

Epost1DNDE_DND_SIMPLE1treatment_post1_norm-0.44840.04320.0001




Epost1PO_E_PO_SIMPLE_NO_CTRL1treatment_post1-1.0990.1270.0001

Epost1PO_E_PO_SIMPLE2treatment_post1-0.53950.04720.0001

Epost1PO_E_PO_SIMPLE_PREINTERACTED3treatment_post1-0.31130.0490.0001





Epost2DNDE_DND_SIMPLE1treatment_post2_norm0.29140.04870.0001




Epost2PO_E_PO_SIMPLE_NO_CTRL1treatment_post20.39520.13030.0024

Epost2PO_E_PO_SIMPLE2treatment_post20.10990.05330.0393

Epost2PO_E_PO_SIMPLE_PREINTERACTED3treatment_post20.14960.05470.0062





Gpost1DNDG_DND_SIMPLE1treatment_post1_norm-0.16240.00430.0001




Gpost1PO_G_PO_SIMPLE_NO_CTRL1treatment_post1-0.15220.01110.0001

Gpost1PO_G_PO_SIMPLE2treatment_post1-0.12280.01080.0001

Gpost1PO_G_PO_SIMPLE_PREINTERACTED3treatment_post1-0.18220.0110.0001





Gpost2DNDG_DND_SIMPLE1treatment_post2_norm0.06170.00440.0001




Gpost2PO_G_PO_SIMPLE_NO_CTRL1treatment_post20.18410.01160.0001

Gpost2PO_G_PO_SIMPLE2treatment_post20.16880.01130.0001

Gpost2PO_G_PO_SIMPLE_PREINTERACTED3treatment_post20.13480.01150.0001





Elec Means

E Pre ADC ttest both flags

The TTEST Procedure

Variable: adc

groupNMeanStd DevStd ErrMinimumMaximumgroupMethodMean90% CL MeanStd Dev90% CL Std Dev

CONTROL2391120.180214.47630.09360378.3CONTROL20.180220.02620.33414.476314.368314.5861

TREATMENT4793120.165913.66490.06240437.5GroupAverage Daily Electric Usage (kWh)Difference (Phase 1-Pre)Difference (Phase 2-Pre)TREATMENT20.165920.06320.26913.664913.592713.7379

Diff (1-2)0.014313.94020.1104PrePhase 1Phase 2Diff (1-2)Pooled0.0143-0.16720.195913.940213.8814.001

groupMethodMean90% CL MeanStd Dev90% CL Std DevControl20.18021.38823.0401.2072.860Diff (1-2)Satterthwaite0.0143-0.17080.1994

CONTROL20.180220.026220.334214.476314.368314.5861Treatment20.16621.28322.8831.1172.717

TREATMENT20.165920.063320.268613.664913.592713.7379Difference (T-C)-0.014-0.104-0.157-0.090-0.143

Diff (1-2)Pooled0.0143-0.16720.195913.940213.8814.001

Diff (1-2)Satterthwaite0.0143-0.17080.1994


PooledEqual718400.130.89680.6458333333



MethodNum DFDen DFF ValuePr > F0.36%0.58%000

Folded F23910479301.12 |t|





Folded F22537454971.14 |t|





Folded F17920358461.18 |t|





Folded F19606391001.06 |t|Diff (1-2)Satterthwaite-0.0141-0.03290.00478


SatterthwaiteUnequal51289-1.230.21970.54%0.48%



Folded F25856517671.020.099

G Post1 ADC ttest both flags

The TTEST Procedure

Variable: adc

groupNMeanStd DevStd ErrMinimumMaximum

CONTROL247761.67741.24290.0079020.6695

TREATMENT496491.67791.2130.00544020.6873

Diff (1-2)-0.000541.2230.00951groupMethodMean90% CL MeanStd Dev90% CL Std Dev

groupMethodMean90% CL MeanStd Dev90% CL Std DevCONTROL1.67741.66441.69041.24291.23381.2522

CONTROL1.67741.66441.69041.24291.23381.2522TREATMENT1.67791.66891.68691.2131.20671.2194

TREATMENT1.67791.66891.68691.2131.20671.2194Diff (1-2)Pooled-0.00054-0.01620.01511.2231.21781.2283

Diff (1-2)Pooled-0.00054-0.01620.01511.2231.21781.2283Diff (1-2)Satterthwaite-0.00054-0.01630.0152

Diff (1-2)Satterthwaite-0.00054-0.01630.0152






Folded F24775496481.05 |t|





Folded F19606391001.06 |Z|

Error

Intercept0000..

treatment_post1_norm0.07730.0425-0.16070.0061-1.820.06940.00738750.1472125

treatment_post2_norm0.13360.0571-0.2455-0.0218-2.340.01920.03967050.2275295

yr2014_mo6_norm3.06640.10112.86823.264630.32

IEPEC, 2019 Denver


14

Agenda

IEPEC, 2019 Denver

Takeaways

15

#1: HBAs did not reduce call center call volumes but generated energy savings- Electricity and natural gas savings of about 0.5% during treatment

#2: Gas and electricity savings persisted after HBA treatment ended- Electricity savings increased after Xcel Energy stopped sending HBAs

#3: HBAs have other potential benefits that this evaluation did not assess- Utility customer welfare

• Customers are inattentive• HBAs may reduce costs of tracking consumption, and help customers avoid

billing surprises, regret, and billing arrearages- Customer satisfaction with the utility

IEPEC, 2019 Denver

Outstanding Research Questions

16

#1: What are effects of HBAs on customer engagement, satisfaction, and welfare?- Value from alerts- Behavioral changes- Impact on customer satisfaction with utility

Research approach: Customer surveys, willingness-to-pay analysis

#2: How do HBAs generate energy savings? Why do HBA savings persist?- Visits to Home Energy Assessment website?- Participation in Excel Energy efficiency programs?

Research approach: Channeling/joint savings analysis

IEPEC, 2019 Denver

Authors

17

Ryan FullemanSenior AnalystCadmus Energy [email protected]

Julie HermanProduct DeveloperXcel [email protected]

Grant Jacobsen, Ph.D.Senior Economist, Cadmus Energy ServicesAssociate Professor, University of [email protected]

Jim Stewart, Ph.D.Principal EconomistCadmus Energy [email protected]

IEPEC, 2019 Denver18

Thank You

Questions?

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Can High Bill Alerts Help Utility Customers to Save Energy? · 2019-08-02 · • Reduce customer...

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