Measures that Save The Most Energy Jackie Berger David Carroll ACI New Jersey Home Performance Conference January 25, 2007

Session Outline 1.Introduction 2.Key Concepts 3.Projected Savings vs. Measured Savings 4.Average Savings by Type of Measure 5.Energy Education Savings Potential 6.Maximizing Measure Savings 7.Conclusions

Introduction - Focus Focus – Measures that save the most… –Energy per measure –Energy per household –Energy per dollar spent Other Important Issues –Measures that save the most… Electricity, gas, or fuel oil Greenhouse gas emissions Lives –Measures that furnish the greatest… Avoided costs Economic benefits Note: Dollars saved vary with energy price

Introduction - Scope Sources –APPRISE evaluation studies –Blasnik and Associates evaluation studies –Dalhoff and Associates evaluation studies Geographic scope –Northeast –Midwest –Mountain

Key Concepts Measures that save the most: –Target highest use households –In a way that maximizes effectiveness –With an understanding of what is going on in this house Measures that save the most per dollar spent –Balance delivery costs with energy saving opportunities Spend less when there are fewer opportunities Spend more when there are more opportunities We recommend using “measured savings”

Targeting Usage (ccf)SpendingSavings$ per ccf saved <1,000$65326 ccf$25 1,000-1,400$83680 ccf$10 1,400+$1, ccf$6

Measure Effectiveness Duct Sealing –Ducts outside envelope = High Savings –Ducts inside envelope = Low/No Savings –Ducts in basement = ???? –Ducts in crawl space = ???? Insulation –With properly seal envelope = High Savings –Without air sealing = Low Savings

Focus on This House Example – Baseload Job in Massachusetts House –Previsit Information: Annual electric usage of 10,000 kWh –On-Site Measurement: 6,000 kWh for appliances / 4,000 kWh for space heater –Problem: Program only pays for baseload measures –Solution: Install cfls, encourage behavioral changes, and refer to electric heat program

Projected Savings vs. Measured Savings Value of projections Projection methodology Issues with projections Comparison of projected savings to measured savings

Projections vs. Impacts Data Needs Projections Data Driven Projections Impacts Installed measuresNoYes Pre treatment usageNoYes Post treatment usageNo Yes Degree daysNo Yes Comparison groupNo Yes

Projections vs. Impacts Basic Projection Methodology –Assumptions Measure installation rates Measure retention rates Pre installation usage Measure effectiveness

Projections vs. Impacts Basic Projection Methodology –Calculation Average household saving = Measure Installation Rate * Measure Retention Rate * (Pre Installation Usage – Post Installation Usage)

Projections vs. Impacts Basic Projection Methodology –Calculation Pre Installation Usage per bulb per hour = 60 watts *.001 =.06 kWh Post Installation Usage per bulb per hour = 13 watts *.001 =.013 kWh Change per Bulb per hour = =.047 kWh

Projections vs. Impacts Basic Projection Methodology –Calculation Change per bulb per day =.047 kWh * 2.5 hours/day =.1175 kWh/day Change per bulb per year = kWh/day * 365 days = 43 kWh/year

Projections vs. Impacts Basic Projection Methodology –Calculation Number installed per home = 43 kWh * 8 bulbs = 344 kWh Retention rate = 344 kWh *.8 = 275 kWh saved per home per year

Projections vs. Impacts So simple, what could go wrong… Incorrect assumptions –Measure installation rate –Measure retention rate Bulbs left for occupants to install Bulbs removed Bulbs broken –Existing bulb kWh –Hours of use

Projections vs. Impacts Survey Results Annual kWh Savings by Hours Used 2.5 hr/day1.5 hr/day Bulbs provided (database) Bulbs provided (client reported) Number installed by auditor or client Number not burned out or removed (clients reported that 46% are used at least 1 hour per day)

Projections vs. Impacts Impact Analysis Results High UseModerate Use ProjectedActualProjectedActual kWh Savings per Bulb kWh Savings per Home1, Survey Results – High and Moderate Use Months After Service Delivery % Burned Out6%8%9%13%17% Source: M. Blasnik and Associates.

Projections vs. Impacts How far are we off with the projections? Evaluations that measure actual usage impacts usually find 50% to 70% of projected savings –NEAT Audit – measured savings were 57% and 54% of projected savings (Sharp, 1994 and Dalhoff, 1997) –Ohio electric baseload savings were 58% to 68% of projected –NJ electric baseload savings were 60% - 69% of projected Source: M. Blasnik and Associates.

Average Savings by Measure Type Methodology for developing measured savings Methodology for attribution of savings to measures Evaluation findings – electric baseload Evaluation findings – space heating measures

Usage Impact Analysis Usage Impact Methodology –Obtain pre and post energy usage data for program participants –Use regression model to adjust usage for changes in weather from “normal weather year” –Construct weather normalized change in usage for treated households –Construct weather normalized change in usage for comparison households

Usage Impact Analysis Usage Impact Methodology –Run regression to determine measure specific impacts Usage change = α + β * household characteristics + γ 1 * measure 1 + γ 2 * measure 2 + γ 3 * measure 3 + μ

Measure Savings – Evaluation Findings kWh Savings Per Measure Ohio EPPPPL WRAPNJ CPCO E$P High UseMod UseBaseloadFull Cost CFL Refrigerator Freezer760 Air conditioner172 Source: M. Blasnik and Associates.

Measure Savings – Evaluation Findings $ Cost per kWh Savings By Measure NJ CP CFL$0.061 Refrigerator$0.069 Source: M. Blasnik and Associates.

Measure Savings – Evaluation Findings CCF Savings Per Measure NJ CPCO E$PIL WAPOH WAPIA WAP Heater Replacement Attic insulation Air sealing Thermostat41 Source: M. Blasnik and Associates.

Measure Savings – Evaluation Findings $ Cost Per CCF Saved By Measure NJ CPCO E$P Heater Replacement$1.30 Attic insulation$0.60$0.20 Air sealing$1.23 Thermostat$0.19 Source: M. Blasnik and Associates.

Potential for Education Major opportunities Potential vs. realization Successful models

Education Impacts Ohio EPP Unprompted Agreed toTaken Turn off lights54%16% Turn off appliances14%3% Use CFLs10%5% Conserve energy10%2% Use double spin on clothes washer9%2% Reduce heating temperature5%1% Line dry clothes4%0% Reduce water heater temperature3%1% Wash clothes in cold water1% None19%

Education Impacts Niagara Mohawk Unprompted Actions Taken As a Result of: WorkshopVideo In-Home Education Turn off lights43%40%33% Install CFLs27%20%24% Turn down thermostat14%15%10% Reduce TV usage11%3%6% Turn off appliances11%9% Turn down water temperature10%12%10% Reduce use of AC9%3%6% Use cold water for clothes washing9%5%6% Set back temperature at night/when out5%4%2%

Potential Education Savings WattageReductionNumberSavings Electric MeasureskWh Turn off lights604 hrs/day4350 Turn off lights at night608 hrs/day2350 Reduce central AC3º3ºAll times250 Reduce TV usage1004 hrs/day2292 Turn off computer2508 hrs/day1730 Gas MeasuresTherms Turn down water temperature10°All times25 Turn down thermostat2°2°All times84 Use cold water for clothes washingCold wash4/week52 Set back temperature at night4º4ºNight58 AC – 72 to 75 degrees, heating 72 to 70 degrees

Maximizing Measure Savings Look at energy bills Do diagnostic tests /take measurements Apply bills, tests, and measurements to decision criteria Build a package of measures that are complimentary and complete

Energy Bills Usage (ccf)SpendingSavings$ per ccf saved <1,000$65326 ccf$25 1,000-1,400$83680 ccf$10 1,400+$1, ccf$6

Tests and Measurements Determine how each system is performing –HVAC Thermal envelope Heating / cooling distribution Equipment Performance Controls –Other end uses Refrigerator metering Water flow test

Decision Criteria Technical –Audit Tool (e.g. NEAT audit) –Priority List –Rule of Thumb Financial –Spending limit –Spending goal –Spending target –Financial incentives

Issues - Technical No Usage Data –Decisions without most essential data Limited Usage Data –Decisions without information on seasonality Audit Models –Data entry sometimes gets in the way of investigating source / causes of usage problems –Note: Data entry on baseline conditions and treatments is essential for program management

Issues - Financial Spending Limits –Do they focus delivery on highest saving measures or restrict delivery of cost-effective measures? Spending Goals –Do they ensure comprehensiveness or encourage a program to over-invest? Spending Target –Do they furnish flexibility or result in over-investment in some homes and under-investment in others?

Recommendations Usage Data – Essential for good decision-making Decision Criteria - Field staff need a good tool for determining which measures to install Financial Guidelines – Should vary with energy savings potential and should be expressed as a range

Conclusions Insulation / Air Sealing / Duct Sealing – In Northeast and Midwest, well-designed and implemented programs are big energy savers Electric Baseload – Refrigerators and cfls save lots of energy, particularly if highest using households are targeted Energy Education Potential – Behavioral changes have the potential to achieve large energy savings, but we have not seen any programs with significant measured energy savings

Contact Information Jackie Berger, , jackie- David Carroll, , david-