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EEO Representative Assessments

EEO Representative Assessments. PRESENTED BY: GEOFF ANDREWS TO: SYDNEY WORKSHOP 8 th September 2011. Why would you use an RA ?. Reduce investigation costs Reduce investigation time; quicker implementation (probably) greater understanding, more opportunities.

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EEO Representative Assessments

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  1. EEO Representative Assessments PRESENTED BY: GEOFF ANDREWS TO: SYDNEY WORKSHOP 8th September 2011

  2. Why would you use an RA ? • Reduce investigation costs • Reduce investigation time; quicker implementation • (probably)greater understanding, more opportunities

  3. What is the rationale / purpose for RA? • Understand energy usage at all sites by analysing energy usage at some sites. • Identify and quantify savings opportunities at all sites by assessing some sites in detail.

  4. Opening the Box

  5. energy use, varies ......

  6. energy use, varies because ...... Influences, (Independent Variables) Energy Use Process(EUP) Energy Input

  7. energy usage model 'energy usage model' Influences, or Independent Variables, e.g.: Energy Input Energy Use Process(EUP) y = a + bx1 + cx1 + dx1 + ex1 ......

  8. Tools for Understanding Energy Use • Sampling • segment the population into groups of similar sites • analyse energy usage and opportunities • extrapolate to the population • Regression Analysis • high level data, across all sites • check results with (e.g.) sub-metering, engineering calculations • Non-statistical methods • all other EEO analysis methods for individual sites. • continue to build understanding

  9. Statistical Sampling ? Statistical sampling • Similar (energy usage and use) population. • Classify and divide into very similar segments • Analyse, identify, assess opportunities • Extrapolate to the population • Considerations • Effort in undertaking 'energy audits' • How similar are they really? • How to extrapolate different ideas to population

  10. Regression Analysis y = a + bx

  11. Regression Analysis y = a + bx1 + cx1 + dx1 + ex1 ......

  12. Multiple (variables) Regression Analysis

  13. Regression Analysis: Data Input

  14. Regression Analysis results Energy Predicted = 771,244 + 558 x1+ 852 x10 + 922 x11 – 719,103 x17 Annual Electricity Consumption (kWh / year) = 771,244 + 558 xSelling Area (m²)+ 852 x Refrigeration volume – cool with no doors+ 922 x Refrigeration volume – frozen with doors– 719,103 x Air-lock Entrance Base-load with an air lock: = 52,121 kWh / year Additional electricity consumption without air-lock = 719,103 kWh / year

  15. Benefits • Revealed importance of air lock(and moisture, refrigeration, etc,).$75,000 / year / store.Other benefits .... • Avoid concentrating on areas with lower potential • Interactions .

  16. Regression analysis, hourly data

  17. Regression Analysis, considerations For accuracy and insights, we need: • lots of data • heterogenity • guess the right variables

  18. 'Non-Statistical Methods' All other methods used to analyse energy usage on a single site, • applied to understand the energy usage model, • across all sites.

  19. RA Project Team Contacts • Geoff Andrews, Genesis Now 1800 22 99 11 • Mark Bernard, Fleet Software and Systems0419 595 900 • Albert Dessi, RET 02 6213 7247

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