Green Building Performance Metrics LF10 May 7, 2010 Mark Frankel New Buildings Institute t
Does Better Mean Good?
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Performance Goals and Outcomes 100 Individual Buildings 50 Codes/Policy 0 2000 2010 2020 2030
Approximate Relative Code Stringency 100 90 80 Estimated! Relat tive Energy Int tensity 70 60 50 40 30 20 10 0 0
Limits of Additive Code Strategy % Savi ings PSZ-GAS-Office 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 1 3 5 7 9 11 1 13 15 Fairbanks Phoenix San Francisco Miami Boise Chicago Baltimore Dlth Duluth Helena Albuquerque Memphis El Paso 1Houston # of Measures Burlington Seattle
Performance Regulation Does Not Address All Energy Use Categories Ingeneral general, about25% to 30% of building energy use in non regulated categories Plug loads Cooking/ refrigeration Servers in buildings
Occupant-Driven Loads become the #1 Challenge for Net Zero Buildings
Glazing performance building orientation cooling efficiency infiltration operating hours climate weather occupant density heating efficiency duct design fan size window area HVACcontrol sophistication building mass interiorshading occupant habits data centers kitchen equipment lighting power density filter condition wall color lighting controls furniture configuration exterior vegetation operable window use insolation glazing orientation wall insulation ventilation rate exposed interior surface characteristics domestic hot water use number of computers copiers and printers elevators exterior lighting occupant gender ratio elevation photovoltaics development d l density register location cooling distribution ib i system roof insulation building manager training cool roof building surface to volume ratio building use type janitorial services metering strategies commissioning structural system acoustic treatment slab edge detailing night setback temperature ground water temperature humidity occupant dress code lamp replacement strategy roof slope daylight yg controls sensor calibration corporate culture lease terms utility meter characteristics parking garage ventilation HVAC system capacity number of separate tenants retail space age of equipment ceiling height heating fuel transformer capacity window mullion pattern terms ofmaintenance contract wall thickness building height lighting fixture layout overhangs thermostat location exit lighting private offices refrigerators solar hot water utility meter load diversity
Glazing performance building orientation cooling efficiency infiltration operating hours climate weather occupant density heating efficiency duct design fan size window area HVACcontrol sophistication building mass interiorshading occupant habits data centers kitchen equipment lighting power density filter condition wall color lighting controls furniture configuration exterior vegetation operable window use insolation glazing orientation wall insulation ventilation rate exposed interior surface characteristics domestic hot water use number of computers copiers and printers elevators exterior lighting occupant gender ratio elevation photovoltaics development d l density register location cooling distribution ib i system roof insulation building manager training cool roof building surface to volume ratio building use type janitorial services metering strategies commissioning structural system acoustic treatment slab edge detailing night setback temperature ground water temperature humidity occupant dress code lamp replacement strategy roof slope daylight yg controls sensor calibration corporate culture lease terms utility meter characteristics parking garage ventilation HVAC system capacity number of separate tenants retail space age of equipment ceiling height heating fuel transformer capacity window mullion pattern terms ofmaintenance contract wall thickness building height lighting fixture layout overhangs thermostat location exit lighting private offices refrigerators solar hot water utility meter load diversity
Different Players Affect Building Performance Tenants Computers and Equipment Schedule Habits Staffing Controls Maintenance Commissioning Operation Design Layout Integration Installation Components and Features
Energy use outcome is highly variable, even for High h Performance buildings
Variable Performance of LEED Buildings 140 120 100 CBECS Actual EUI 80 60 Silver Certified Gold/Platinum 40 Interim 2030 20 0 Medium Energy Type Buildings Certified Silver Gold/ Platinum 13
Design process does not predict outcome well
Range of Energy Performance Outcome SCHEDULE AND USE OCCUPANT BEHAVIOR ACTUAL SYSTEM OPERATION (Cx) MODELED SYSTEM OPERATION Use Energy
HP Buildings Tend to be Optimistic About High Performance Design EUI (Adj2) 3.0 2.5 2.0 1.5 LEEDlevel Certified Silver Gold Platinum Actual / 1.0 0.5 1 0.0 0 20 40 60 80 Design EUI (Adj2) 100 120 140 16
Measurement and Feedback
Property Manager # Energy Star Labeled Buildings Size of Energy Star Labeled Buildings (SF) CB Richard Ellis 270 82,135,316 Hines 132 73,434,544 Jones Lang LaSalle 101 42,540,304 Cushman Wakefield 98 36,566,129 GSA 75 32,766,272 Transwestern 126 30,699,266 Tishman Speyer 44 27,528,281 Brookfield Properties 26 20,606,431 Macguire Properties 30 15,151,185 Boston Properties 32 14,345,309 Arden Realty 114 11,860,857 Equity Office 32 12,461,316 Irvine Company 36 10,258,991 RREEF 45 9,041,878 Shorestein 41 9,407,215 3% of Total Energy Star 35% of ES Buildings 44% of ES SF
The best performing buildings can t differentiate themselves from very good buildings
Reversing the Scale; Lower is Better Energy Star Score EUI zepi EQ Energy Use Intensity zero Energy Performance Index Energy Quotient
Building Labeling
Getting Feedback to the Right Audience Design / Construction Not operated right Not occupied as expected Tenants / Occupants Not operated right Not designed right Owner / Operator Not designed right Occupants don t behave right Understanding Responsibility for Performance
Metering Implications Who uses the data Designer/Operator/Tenant/Code How much information to collect Energy Star/IPMPV/LEED What data to collect Equipment vs end use How often to review the information
Data Needs VS. Capabilities Participant Designer Occupant Operator Owner Data Source Time Interval Minute+ Hour Day Week Month Year BAS Energy Star M&V Utility Dashboard
Dashboard Monitoring Systems
Savings From Feedback Residential Commercial Tenant 0 5 10 15 20 25 30 35 % Savings
Actionable Data and Proxies No Weekend Setback CO O2 ppm CO2 Sensor Calibration Lights on when Unoccupied Scheduled Occupancy
POE Comfort Factor (avg rating) TEMP OVERALL (0.6) How cold (0.3) How warm (0.6) Temp shifts (0.6) Temp controls work (0.1) AQ OVERALL (0.9) Air freshness (1.0) Air movement (0.7) Air controls work (0.1) ACOUSTICS OVERALL (0.0) Noise: background (0.0) Noise: adjoining areas ( 0.1) Noise: vent systs (1.2) Noise: lights (1.6) Noise: outside (0.7) LIGHTING OVERALL (1.0) How bright (1.0) Amount of light (0.9) Glare from lights (1.1) Light controls work (0.6) Daylight amount (1.1) Glare from windows (0.7) HELPS WORK (0.6) HELPS HEALTH (0.7) Simple data relationships become powerful tools BUILDING OVERALL (0.9) 0% 50% 100% to analyze and monitor building performance 29
Energy Signature 120 Measured = Design --> 100 red EUI Measur 80 60 40 20 0 0 20 40 60 80 Design EUI 100 120 Applying Business Intelligence to Readily Available Data
Automated Interpretations rage Hourly Usage, W/sf Ave 4.0 3.5 3.0 2.5 2.0 1.5 1.0 Heat G Heat E Cool E DHW (E or G) 0.5 Int+Ext 0.0 Gain 35 45 55 65 Ref: 11 Mean Monthly Temperature, deg F Ave erage Hourly Usage, W/sf 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 45 55 65 75 Ref: 112 Mean Monthly Temperature, deg F Heat G Heat E Cool E DHW (E or G) Int+Ext Gain Ave erage Hourly Usage, W/sf 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 35 45 55 65 Ref: 8 Mean Monthly Temperature, deg F Heat G Heat E Cool E DHW (E or G) Int+Ext Gain Very low occupant loads Efficient shell and ventilation Low occupant loads Efficient shell, ventilation Inefficient cooling Heating control inefficiency Low occupant loads Inefficient shell, ventilation Possible solar gain influence
System Performance Comparison Density W/ft2 Avera age Power Monthly Energy / Temperature Signatures (Site Energy) 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 05 0.5 0.0 15 25 35 45 55 65 75 85 Monthly Average Degrees F Ref: Site total G: Site total GSHP school: Site total
Demonstrating Operational Improvements Average Pow wer Density W/ /ft2 Monthly Energy / Temperature Signatures (Site Energy) 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 10 20 30 40 50 60 70 80 90 Monthly Average Degrees F Reference: school L: Before changes L: After changes Normalized Power, Watts/ft2 9 8 7 6 5 4 3 2 1 0 Fuel Use Reduction 10 30 50 70 Mean Monthly Temp, Deg F First Year Fuel Second Year Fuel Basecase Fuel Comparis on Fuel
Operational Trends
74 yrs 78 yrs Research Priorities
Green Building Performance Metrics LF10 May 7, 2010 Mark Frankel New Buildings Institute t