Targeted Energy Efficiency with Multi-facility Benchmarking

1. Targeted Energy Efficiency with Multi-facility Benchmarking 14th Annual E Source Energy Managers' Roundtable May 9-11, 2007 Boston, Massachusetts Kelly Kissock Ph.D. P.E. Associate Professor Department of Mechanical Engineering, University of Dayton Principal: Go Sustainable Energy

2. Corporate Energy Managers • Responsible for multiple facilities • Responsibilities include: • Reducing energy costs • Energy cost budgeting • Documenting energy / environmental performance • Carry out responsibilities with limited resources: • Budget • Time • Expertise

3. Some Broadly Available Resources • Utility billing data • “Free”, accurate • Production/sales/occupancy data • Primary metrics of most corporations • Weather data • Actual and typical free over internet • Computational and graphical power of computers

4. Utilizing Resources to Fulfill Responsibilities RESPONSILITIES • Responsible for multiple facilities • Responsibilities include: • Reducing energy costs • Energy cost budgeting • Documenting energy / environmental performance • Carry out responsibilities with limited resources: • Budget • Time • Talent RESOURCES • Utility billing data • Free, accurate • Production/sales/occupancy data • Primary metrics of most corporations • Weather data • Actual and typical free over internet • Computational and graphical power of computers

5. Benchmarking • Benchmarking ~ comparisons • Benchmarking types • Multi-facility benchmarking • Compare facilities • Internal and external to corporation • Identify worst, average and best performers • Past-performance benchmarking • Compare performance over time • Identify improving and declining performance

6. Benchmarking Difficulties • Influential variables (weather, production, sales, occupancy) vary: • By location • Over time • Thus, hard to compare “energy performance” • Between locations • Over time • Rarely helps understand performance • Why is a facility the best, average or worst? • Why is performance improving or declining? • Difficulties reduce effectiveness of benchmarking efforts

7. Our Method To Improve Usefulness of Benchmarking • Uses power of computer • Graphical, statistical and engineering analyses • Transparent – trustworthy results • Complementary to EnergyStar – 6 metrics+ instead of one • Target energy efficiency efforts • Save time and money

8. ‘Facility Metrics’ Method 1) Characterize performance with ‘Energy signature’ model 2) Remove noise with ‘Normalized annual consumption’ NAC 3) Track performance with ‘Sliding NAC’ analysis 4) Benchmark performance with ‘Multi-site sliding NAC’ analysis

9. Data Requirements • Monthly electricity and/or fuel bills • Influential variables • Floor area, production, sales, occupancy, etc. • Actual and typical weather data

10. Actual and Typical Temperature Data http://www.engr.udayton.edu/weather • http://rredc.nrel.gov/solar/old_data/nsrdb/tmy2/

11. 1) Characterize Performance with Energy Signature Model • Develop • multi-variable • three parameter • change-point model • Of energy use as function of • temperature • influential variables.

12. Load Gas and Temperature Data 3 Years of Gas Bills 3 Years of Temperature Data

13. Three-parameter Heating (3PH) Model

14. Three-parameter Heating (3PH) Model HS Eind Tbal

15. Three-parameter Cooling (3PC) Model CS Eind Tbal

16. Physical Meaning of Coefficients Balance temperature • Outdoor temperature where heating/cooling begins • Thermostat set point • Internal heat gain • Slope • Heating/Cooling energy per degree of temperature • Heat loss/gain through envelope • Efficiency of heating/cooling equipment • Production/sales/occupancy • Energy use per unit prod/sales/occ • Independent energy use

17. Disaggregate Fuel Use Fuel Weather Production/Sales Independent Temperature

18. Disaggregate Electricity Use Electricity Weather Production/Sales Independent Temperature

19. Use: Budgeting Fuel Weather Production/Sales Production/Sales Independent Temperature

20. Use: Inform Decisions & Allocate Resources Fuel Weather Production/Sales Independent Temperature

21. Use: Identify Opportunities Electricity Weather Production/Sales Independent Temperature

22. Use: Identify Opportunities Fuel Weather Production/Sales Independent Temperature

23. Use: Identify Opportunities Fuel Fuel Weather Weather Production/Sales Production/Sales Independent Independent Temperature Temperature

24. Use: Identify Opportunities Fuel Fuel Weather Weather Production/Sales Production/Sales Independent Independent Temperature Temperature

25. Using Models to Identify Problems: Chillers Left On R2 = 0.92 CV-RMSE = 22.4%

26. Using Models to Identify Problems: Malfunction Economizer R2 = 0.70 CV-RMSE = 7.8%

27. Using Models to Identify Problems: High Scatter = Poor Control R2 = 0.59 CV-RMSE = 68% Observation Heating energy varies by 3X at same temp! Discovery Didn’t close shipping doors!

28. Using Models to Identify Success:Low Scatter = Good Control R2 = 0.99 CV-RMSE = 1.1%

29. 2) Normalized Annual Consumption: NAC • Utility bills tells us ‘Annual Consumption’, which how much energy building consumed with weather that actually occurred • We want to know how much energy building would have consumed during ‘normal’ weather. • This is called ‘Normalized Annual Consumption’ NAC • Calculating NAC is a two step process.

30. Calculating NAC: Step 1 of 2: Actual gas bills + Actual weather data = Energy signature model

31. Calculating NAC: Step 2 of 2: Energy signature model + Typical weather = NAC production/sales/occupancy

32. NAC is “Noisefree” Energy Consumption • NAC removes ‘noise’ from variable weather, production/sales/occu-pancy • NAC reveals true energy use characteristic of facility • NAC allows comparison of sites with different weather, production/sales/occu-pancy

33. 3) Track ‘Noiseless’ Performance with ‘Sliding NAC’ Analysis • Calculate NAC for every twelve month period in data set. • Change in NAC indicates change in building energy use characteristic • Understand change in NAC by examining change in energy signature coefficients

34. Sliding NAC

35. Sliding NAC and Heating Slope

36. Sliding NAC and Independent Fuel Use

37. Sliding NAC and Balance Temperature

38. 4) Benchmark Performance with ‘Multi-site Sliding NAC’ • Quantify average energy performance and distribution of energy performance across all sites • Benchmark best/worst NAC and change in NAC • Benchmark best/worst coefficients and change in coefficients

39. NAC and Change in NAC

40. Slope and Change in Slope

41. Sorted NAC Overall biggest gas users Independent of • Weather • Production • Sales

42. Sorted Independent Energy Use • Best candidates for • Timers • Insulation • Water heater retrofits • Water temp set backs

43. Sorted Heating Slope • Best • candidates for • Weatherization • Furnace • efficiency • Air conditioning • efficiency

44. Sorted Balance Temperature • Best • candidates for • Programmable • thermostats • Temperature • controls

45. Our Experience • 80%+ of sites presented problems we expected

46. Summary Process of: • Characterizing performance with ‘Energy Signature’ model • Removing weather, production, etc. noise with ‘Normalized Annual Consumption’ NAC • Tracking change in energy use with ‘Sliding NAC’ analysis • Benchmarking performance with ‘Multi-site Sliding NAC’ analysis Can: • Improve budgeting process • Identify energy saving opportunities • Target energy efficiency efforts to improve effectiveness • Measure and track savings and effectiveness • Save time, money and resources…..

47. Thank you! questions to kkissock@udayton.edu 937-229-2852