1. Discussion of Mexican Adaptation of ENERGY STAR Methodology CEC Workshop Mexico City March 2013 Michael Zatz and Alexandra Sullivan ENERGY STAR Commercial Buildings Program U.S. Environmental Protection Agency

2. 2 ENERGY STAR Score Objectives  Help businesses protect the environment through superior energy efficiency  Motivate organizations to develop a strategic approach to energy management  Convey information about energy performance in a simple metric that can be understood by all levels of the organization  Be accessible through a simple and easy to use tool

3. Agenda  Analytical Overview  Analytical Interpretation & Example  Site & Source Energy  Reference Data Requirements  Review of Mexican Office Model  Reference Data Set  Analytical Approach  Conclusions & Next Steps 3

4. 4 ENERGY STAR score Analytical Requirements  Monitor actual as-billed energy data  Create a whole building indicator  Include all fuel types  Capture the interactions of building systems not individual equipment efficiency  Track energy use accounting for weather and operational changes over time  Provide a peer group comparison  Compare a building’s energy performance to its national peer group  Track how changes at a building level alter the building’s standing relative to its peer group

5. 5 ENERGY STAR Score Analytical Development Process  Analyze national survey data  Develop regression models to predict energy use for specific space types based on operation  Compare actual energy use with prediction from the model  More efficient: Actual < Predicted  Less efficient: Actual > Predicted  Create scoring lookup table  Scores are based on the distribution of energy performance across commercial buildings  One point on the ENERGY STAR scale represents one percentile of buildings

6. 6 ENERGY STAR Score Statistical Methodology  Develop the regression model  Coefficients represent average responses  Coefficients provide adjustments for each operational characteristic Does not add the energy use of each piece of equipment Does adjust energy based on correlation between operating characteristic and energy use Energy Intensity = C o + C 1 *Operating Hours + C 2 *Workers per Square Foot + C 3 *Computer per Square Foot + C 4 *HDD + C 5 *CDD + …

7. ENERGY STAR Score Criteria for Characteristics 7

8. 8 ENERGY STAR Score Interpretation and Application The Score Does Evaluate actual billed energy use Normalize for operational characteristics (e.g., size, number of employees, computers, climate) Express the performance of a building compared to its peers, as described by a nationally representative survey The Score Does Not  Sum the energy use of each piece of equipment  Evaluate buildings relative to others in Portfolio Manager  Normalize for technology choices or market conditions (e.g., type of lighting, energy price)  Explain why a building operates as it does

9. 9 ENERGY STAR Score Example  Two example office buildings  Same size and climate (200,000 square foot; Philadelphia)  Different Hours, Workers, Computers  Same Actual Energy Office AOffice B Number of Workers 700400 Weekly Hours of Operation 11260 Number of Computers 750475 Predicted Energy Intensity (kBtu/ft 2 ) 353289 Actual Energy Intensity (kBtu/ft 2 ) 200 Score 8167

10. 10 ENERGY STAR Score Site and Source Energy  Primary Energy  Base fuel combusted to produce a useful product  Natural Gas, Fuel Oil  Secondary Energy  Product of a raw fuel in a form that can be used by a building  Electricity, Heat, Steam

11. 118 Mbtu Natural Gas 100 Mbtu District Steam 100 Mbtu Steam Building A Heat for Occupants: 100 Mbtu Purchase: Natural Gas Type: Primary Site Energy: 118 Source Factor: 1.047 Source Energy: 123.5 Boiler Building A Heat for Occupants: 100 Mbtu Purchase: District Steam Type: Secondary Site Energy: 100 Source Factor: 1.21 Source Energy: 121 Buildings can operate the same but use different amounts of primary and secondary energy

12. 12 ENERGY STAR Score Source and Site Energy  Because ENERGY STAR rates the whole building, the score must account for any mix of fuels  Primary and secondary  Site Energy  Energy consumption expressed on utility bills  Includes combination of primary and secondary energy, which are not directly comparable  Source Energy  Traces on-site consumption back to energy content of primary fuels  Accounts for the losses in conversion from primary to secondary energy (which can occur either on-site or at a utility)  Accounts for losses in distribution to buildings

13. ENERGY STAR Score Data Sources  Commercial Building Energy Consumption Survey (CBECS)  Conducted by Energy Information Administration every 4 years  Bank/Financial Center, Courthouse, Hotel, House of Worship, K-12 School, Medical Office, Office, Residence Hall/Dormitory, Retail, Supermarket, Warehouse (refrigerated/unrefrigerated)  Industry Surveys  Data Center – Conducted by EPA  Hospital – Conducted by American Society for Healthcare Engineering (ASHE)  Senior Care – Conducted by Assisted Living Federation of America (ALFA), American Association of Homes and Services for the Aging (AAHSA), American Health Care Association (AHCA), and National Center on Assisted Living (NCAL)  Wastewater Treatment Plant – Conducted by American Waterworks Association Research Foundation 13

14. ENERGY STAR Score Reference Data Requirements  Sample must:  Be random  Be nationally representative Diverse in size Diverse in geography Diverse in ownership/management  Be sufficiently large to represent the population Given a total restaurant population of about 900,000, a sample of at least 400 buildings would be desirable  Include measured whole building energy use data for all fuel types  Include data on numerous operational characteristics that are potentially important Data on more characteristics than will ultimately be included in the model 14

15. Overview Comments on Mexican Office Model  Follows general ENERGY STAR methodology  Several significant differences  Reference data set  Fuels included  Site vs source energy  Variables analyzed and included in final model 15

16. Reference Data Set Comparison AspectEPA ENERGY STARMexico Number of Buildings755556 Type of BuildingsMixed public and privatePublic (government) only # of VariablesMore than 1009 Fuels IncludedAllElectricity only 16

17. Analysis Comparison AspectEPA ENERGY STARMexico Filters Applied to Data Set Yes??? Number of Buildings Eliminated by Filters 257??? Location VariableNo – Actual heating and cooling degree days used Yes – Adjustment based on one of three regions Technology VariablesNoneCooling System Capacity Site or Source EnergySource EnergySite Energy 17

18. Observations on Mexican Office Model  Not a whole building measure – electricity only  Use of site energy does not present accurate picture of total energy use in buildings – may present challenges in consistency when expanding to other building types  Inclusion of technology variable may reward wasteful oversizing  Sample likely not representative of entire market  Limited variables analyzed due to survey limitations 18

19. Conclusions and Recommended Next Steps  EPA review identified several key differences in the technical approach  Though similar, the Mexican methodology is not fully consistent with EPA’s Methodology  Differences in our approaches may be appropriate given our respective programs  If Mexico proceeds with the current approach, then documentation must clearly state that it is different from that of EPA 19