unido enms expert training module 1 day 2€¢ manual readings • meters accurate and working •...

UNIDO EnMS Expert Training

Module 1

Day 2

1

2

TopicDuration(hours)

Exercise(mins)

Breakduration

StartTime

EndTime

DAY 2

ER1 Bills and sub-meters 30 30 08:30 09:30ER2 Analyse energy use 30 30 09:30 10:30Break 15 10:30 10:45ER3 Identify and quantify SEUs 30 30 10:45 11:45ER4 Identify and quantify drivers and analyseSEUs 30 30 11:45 12:45

Lunch 45 12:45 13:30ER4 Identify and quantify drivers and analyseSEUs 30 13:30 14:00

ER5 Baseline and EnPIs, EnPI tool 30 45 14:00 15:15Break 15 15:15 15:30ER7 Technical energy audits 15 15:30 15:45ER8 Identify energy saving opportunities 15 40 15:45 16:40TOTALS 2.75 3.25 1.25

Planning workflow

3

1. Energy bill and sub-meter data

2. Analyze past,present and future

energy use

7. Technical energyaudits

8. Identify opportunities forimproved performance,review and decide on

action plans

6. Review operationalcontrol for all SEUs

4. Identify Drivers, getdata and analyze SEUs

3. Identify and quantifySignificant Energy Users

(SEUs)

5. Develop baselines andPerformance indicators

foreach SEU

What are my energy sources, uses andconsumption levels?

• Electrical, natural gas, propane, hydro, wind?• What facilities, systems or equipment are using energy?• What data do we have and where/how can we get it?• What data do we need and where/how can we get it?• How much energy are we using?• How much did we use in the past?• What are energy predictions for the future?• What are the trends?• Where do we stand against benchmarks?

4

• Collect past and current monthly consumption data at thefacility level (energy bills)

• Determine what other data may be available for analysis Sub-meter data Interval data Equipment information Other data

• Determine PAST and CURRENT energy consumption by use• Note: The time period for data collected will depend on your

organization and what data is available.

Analyze Energy Use & Consumption

5

Total PlantEnergy

ElectricityOff-site

GeneratedSteam

Natural Gas Propane

Identify all energy sources that cross the fence line!

Utilize flow chartsEnergy sources

6

6

Analyze Energy UseUtilize flow charts• Energy sources &

consumption• Energy use &

consumption

Total PlantEnergy

Consumption(kWh/month)

Electrical Energy(kWh/month)

Motors(kWh/month)

Heaters(kWh/month)

Off-siteGenerated

Steam(kWh/month)

Natural Gas(kWh/month)

Steam(kWh/month)

Heaters(kWh/month)

Propane(kWh/month)

Steam(kWh/month)

7

7

Typical Barriers• Lack of data• Production and energy data on different time frames• Lack of metering• Meters not calibrated• Data not organized for analysis• Notion that energy data not important to equipment operation

8

Identifies most costly uses Identifies trends Highlights problems early Forms basis for comparison Used to evaluate progress

Value to the OrganizationAnalysis of Past and Present Data

9

Exercise ER1 Energy Data

• Enter available utility data and information for each energy source in your

scope into the billing worksheet; is there any utility data missing?

• Include water if relevant

• Alter the columns as you require.

• If you already do this elsewhere, discuss with the trainer if your existing

format is ok.

• General Rule: Never enter data in rows in Excel – columns only

• Calculate unit energy cost for electricity and all other fuels.

• Convert energy consumption data into a common unit (kWh or GJ); which

energy source is the most expensive per unit?

10

11


Exercise(mins)

Breakduration

StartTime

EndTime

DAY 2ER1 Bills and sub-meters 30 30 08:30 09:30

ER2 Analyse energy use 30 30 09:30 10:30Break 15 10:30 10:45ER3 Identify and quantify SEUs 30 30 10:45 11:45ER4 Identify and quantify drivers and analyseSEUs 30 30 11:45 12:45



Planning workflow


2. Analyze past,present and future

energy use



action plans




(SEUs)


foreach SEU

12

Information

• Simple trends

• Annualised trends

• Trend of average unit price (AUP)

• Trend of annualised use vs target

13

To estimate future energy consumption by use,consider:• How will product mix change in the next 3-5 years?• What is production level expected to be in 3-5 years?• What operating equipment will be utilized (or idled) due to

new product development, production mix or productionvolume changes?

• Will the same number of hours per year and shifts beoperating?

• What are economic and industry forecasts indicating withrespect to energy budgets or supply?

• Are supplier or material changes expected?

Estimate Future Energy Use

14

Exercise ER 2

- Review your data in these trends- Is there anything new?- How much energy will you use next year?

Note: It is best to delete everything from this tab andbuild it yourself to your specific requirement. Assumingyour excel proficiency is good enough. If not ask forhelp from the team.

15

See you in 15 minutes!

16

17


Exercise(mins)

Breakduration

StartTime

EndTime

DAY 2ER1 Bills and sub-meters 30 30 08:30 09:30ER2 Analyse energy use 30 30 09:30 10:30Break 15 10:30 10:45

ER3 Identify and quantify SEUs 30 30 10:45 11:45ER4 Identify and quantify drivers and analyseSEUs 30 30 11:45 12:45


ER5 Baseline and EnPIs, EnPI tool 30 45 14:00 15:15Break 15 15:15 15:30ER7 Technical energy audits 15 15:30 15:45ER8 Identify energy saving opportunities 15 40 15:45 16:40TOTALS 2,75 3,25 1,25

Planning workflow


2. Analyze past, presentand future energy use



action plans




(SEUs)

5. Develop baselines andPerformance indicators for

each SEU

18

Significant Energy Uses

Significant component of the organizationconsumption

Equipment, processes, facilities, systems Considerable opportunity for improvement Determined by organization! Document methods and criteria

19

Significant Energy Use Identification

• Use facility and process flow diagrams to identify energy

uses and interactions

• Show primary and secondary energy streams

• Use previously collected data to determine energy use

• Is additional data required?

• Group equipment and processes into logical systems

• Which people affect the energy use of that item/system?

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How to quantify each energy user• Do you have sub-metering?

• Automatically logged to a database• Manual readings• Meters accurate and working• Data collection process working, consistent and accurate

• Do you have local meters?• These can be read manually and calculated/estimated• Care with time of readings

• Motor List, Heat Balance, Sankey Diagram• Ideally identify at least 80% of energy use• SEU list is the basis of much of the EnMS

21

Heat (energy) balance• Use what you know:

• Steam flow• Feedwater flow (= steam flow approximately)• Fuel flow (heat flow = fuel flow * efficiency)• Gas bills• Hot water flow and temperature difference (dT)

(Q=m*Cp*dT)• Build up a balance

• Heat in = heat out• If you have a significant gap, you may need to measure it• Ultrasonic flow meters, portable heat meters

• More challenging than electrical power• Typically fewer measuring points

22

• Organize data in energy balance or other method to identify equipmentand processes

• Use internal knowledge to add to list

• Techniques

• Energy balance

• Ranking methods

• Six sigma tools

• Other data analyses

• Remember Pareto Rule (80/20)

• Start with a few

Significant Energy Use Identification

23

Description kW % Annual $Melter 9,634 53.4% $2,959,879Hi Press Air Compressor 2,330 12.9% $715,852Med Press Air Compressor 780 4.3% $239,641Med Freq. 545 3.0% $167,442Forming Fans 494 2.7% $151,773Oven Scrubber 450 2.5% $138,255Scrubber 414 2.3% $127,194Cooling Water 407 2.3% $125,044Filtered Air 373 0.0% $114,598Fans 336 1.9% $103,230Med Freq 320 1.8% $98,314East Scrubber 255 1.4% $78,344Forming Fans 150 0.8% $46,085F. Fans West 4,5 122 0.7% $37,482Line Drive 69 0.4% $21,199Other loads and misc. 1,241 6.9% $381,276

100% Load Factor kW 18,042 100.0% $5,543,090

66% oftotalload

Rank Uses

24

SEU Pie Chart

27.07%

11.44%

33.33%

28.16%

Significant Energy Uses For MiningOperation

Blunging

Steam System

High Shear

Other

25

26

Connections to Significance

Significantuses

Objectives,targets andaction plan

Competence,training andawareness

Operationalcontrol

Monitoring,measurementand analysis

Continual Improvement• Start with a few significant uses – keep it manageable!

• Add to the identified significant energy uses over time,improving the efficiency and control of moreequipment, systems, and processes.

• Addressing the connections associated with significantenergy uses will quickly consume resources!

27

Typical Barriers• Not focusing on large energy users and systems• Not including a cross-functional team when determining

significance• Identifying too many significant systems• Inadequate submetering• Inadequate data analysis• Lack of connection with organization’s strategic focus for future

energy use estimation• Focus on data or system inadequacies

28

Exercise – Significant Energy Uses

• Use the ER3 tabs• Motor List• SEU list and calculation

29

30


Exercise(mins)

Breakduration

StartTime

EndTime

DAY 2ER1 Bills and sub-meters 30 30 08:30 09:30ER2 Analyse energy use 30 30 09:30 10:30Break 15 10:30 10:45ER3 Identify and quantify SEUs 30 30 10:45 11:45ER4 Identify and quantify drivers andanalyse SEUs 30 30 11:45 12:45



Problem: energy consumption varies due to,

• Weather• Daylight availability• Production throughputs• Mileages• Occupancy• …etc• “driving factors”• Terminology: drivers, independent variables, energy factors

All mean the same, decide which you will use

31

Planning workflow

32





action plans




(SEUs)


each SEU

What does this tell us?

34

Same gas data in annualised view

35

Previous gas data vs heating degree days (HDD)

36

Straight line formula• Y = mX + C• Energy (E) = Factor (F) * Driver (D) + Constant (c)• E = FD+c• In the previous case:

Gas = 48.651 * HDD + 13238• This formula can be used to predict expected

consumption for any given driver• We can compare predicted vs. actual usage to

indicate performance!

37

In general

• Expected energy consumption can be anyfunction of relevant driving factors, D

E = f(D1, D2, ……. Dn)• Use the simplest effective model• A straight-line relationship is often good enough

38

Other modelsMultivariate linear regression:

Y = b + m1X1 + m2X2

Polynomial linear regression:

Y = b + m1X1 + m2(X2)2

Nonlinear regression (energy use in cement industry):

Courtesy of Argonne National Laboratory and EPA, ANL/DIS -06-3

39

The main message

• Establish relationships between energy consumptions andappropriate energy (driving) factors

• Sometimes called “performance characteristics”

• Use these to calculate expected consumption based onproduction activity, prevailing weather etc.

• Thereby detect unexplained deviations

40

Measurement Plan

• Do you have enough instrumentation to develop yourEnPIs?

• List additional instrumentation needed if any

41

Exercise – Work on your drivers

Demo how to do it in Excel

Note: it is critical that all participants cando this. It will come up again and again. Itis in the exam and is required to have an

effective EnMS!

42


43

44


Exercise(mins)

Breakduration

StartTime

EndTime

DAY 2ER1 Bills and sub-meters 30 30 08:30 09:30ER2 Analyse energy use 30 30 09:30 10:30Break 15 10:30 10:45ER3 Identify and quantify SEUs 30 30 10:45 11:45ER4 Identify and quantify drivers and analyseSEUs 30 30 11:45 12:45

Lunch 45 12:45 13:30ER4 Identify and quantify drivers andanalyse SEUs 30 13:30 14:00


Exercise

• Continue with the exercise

45

46


Exercise(mins)

Breakduration

StartTime

EndTime



ER5 Baseline and EnPIs, EnPI tool 30 45 14:00 15:15Break 15 15:15 15:30ER7 Technical energy audits 15 15:30 15:45ER8 Identify energy saving opportunities 15 40 15:45 16:40TOTALS 2,75 3,25 1,25

Planning workflow

47





action plans




(SEUs)


each SEU

Energy Metrics – levels of complexity• Simple:

• Simple: consumption last month v same month lastyear

• Simple: compare actual consumption with budget• Simple: annualised trend of cost and consumption

• More complex (but beware!)• Energy use per unit output• Cooling energy per cooling degree day• Specific energy consumption (SEC)

• Regression analysis is usually best• Same principles apply to EnPIs and verification of

savings

48

Purpose of energy metrics

• Objective support for decision making- too often subjective reasons are used!

• We need to know how much energy we are using• We need to know if performance is improving• We need to know if we are meeting targets• We need to be able to verify savings of

improvements

ES = Bpeu – Rpeu ± A

49

■ Facility-wide EnPIs

■ Process-unit level• Product specific• Process specific

■ Energy System level• Compressed Air – kW / m3/sec• Steam systems – kWh / kg/hr• Furnace – kWh / unit

Example Performance Indicators

50

Simple ratios – beware!• Energy use per unit of output (Energy Intensity) e.g. kWh/T of product Useful in energy intensive industries for benchmarking

internally and externally Beware in others, especially in cases with large baseloads Almost of no value in judging energy performance Usually tracks output better than energy

• Energy Efficiency (energy in compared with energy out) E.g. boiler efficiency is a useful indicator but beware: Decreasing boiler load through pipe insulation, leak repair

or demand management will almost always result inreduced efficiency due to lower loads

Overall system efficiency will improve but not the boilerefficiency

51

Annualised trend

• Moving total of previous 12 months (or 52 weeks, etc)• Removes seasonal effects• Gives a real view of comparison v budget• Effects of a change stay for next 12 periods• Absolute numbers

• No allowance for changing drivers or activity levels• Very useful for forecasting, you can quickly judge what

next 12 months use will be• You need to correct for known changes in output or

other

52

Other indicators - be careful!

• Specific Energy Consumption (SEC)• For example air compressor SEC will usually increase if leaks

are repaired or demand reduced.• This does not mean you shouldn’t reduce demand• It means that care is needed in the use of this indicator

• Coefficient of Performance (COP)• Used as a measure of refrigeration plant performance• = cooling load (kW) / electrical power to compressor (kW)• COSP = cooling load (kW) / power to compressors plus

auxiliaries loads such as fans and pumps• Often reduces as load reduces (centrifugal compressors can

be an exception)

53

Performance checking with EnPI

• We use energy for known purposes (“outputs”)

• If we can measure useful output, we should be able toestimate expected energy consumption

• Thus we can gauge actual consumption…

Waste relative to target characteristic Savings relative to historical baseline

54

Energy Baseline• Basis of comparison for evaluating energy performance

• Facility-wide• Systems and equipment• Significant energy uses

• Uses pieces of initial energy review• Energy use data• Energy consumption data

• Facility-determined time period• Point in time• Period of time

• Measure energy performance improvement against thebaseline

55

Targets and baselines

• “Target” characteristicFor management controlBase on best achievable performanceKeep continually adjusting

Performance Characteristic Lines

0

500

1000

1500

2000

2500

3000

3500

0 100 200 300 400 500

Driving factor

Ene

rgy

used

56

Targets and baselines

• Historical baseline characteristicFor assessing savingsUsually derived from ‘base year’ dataLeave unchanged Performance Characteristic Lines

0

500

1000

1500

2000

2500

3000

3500

0 100 200 300 400 500

Driving factor

Ene

rgy

used

57

Difference between expected and actual

59

CUmulative SUM of difference (CUSUM)

60

Historical baseline characteristic

• Answers the question “how much would Ihave used in the absence of my energy-saving measures?”

• Allows absolute kWh savings to becomputed Gives clean, objective view Production, weather, etc. already

accounted for

61

Cumulative savings can be tracked

62

Baseline alternatives

• Baseline will be used for future comparison of

improvements

• Ideally based on regression analysis as shown

• Can be absolute consumption, e.g. 1 GWh per

annum

• SEC: kWh per unit of output (beware)

63

Adjust Energy Baseline

Major process changes Major operational changes Major energy system changes When EnPIs no longer reflect organizational

use As determined by the organization

(predetermined method)

64

Value to the Organization

• Understand energyuse for baselineperiod

• Have a comparativepoint for measuringimprovement

65

Documents• Identified Energy Performance Indicators (EnPIs)• Method for determining and updating EnPIs• Energy management baseline

Records• Baseline (data pieces of initial energy review)• Review and comparison of EnPIs to baseline

Documents & Records

66

• Drivers worksheet• EnPI Tool• EnPI Tool Instruction Guide

Tools

67

EnPI Tool

68

P-value, the P-value represents the probability that there is NOT a statisticallysignificant relationship between variables. Therefore, a low P-value(preferably below 0.1) can be interpreted to indicate that it is unlikely that twovariable are NOT related.

For example, lets say a farmer collected data on the number of chickens on hisfarm and the number of eggs produced for any given month. He determined thatfor his given data set the P-Value was of 0.03. What does this mean? From thiswe can conclude that there is a 3 in 100 chance that the number of egg producedis NOT related to the number of chickens.

R2 is the measurement of how well a regression model fits actual data points.The value can range from 0 to 1 where 1 represents a perfect fit of theregression to the actual data.

In Summary, since we want to consider variables that both have a statisticallysignificant impact on the energy consumed and that we can effectively model,both the P-value and R2 will be considered.

Statistical Terms

69

Step 1: Energy Utilities

UtilitiesUnits of Data

Entered

MJConversion

Factor

Generation /T&D

EfficiencyElectricity kWh 0.0036 33.3%Natural Gas GJ 1 100.0%[None] GJ 1 100.0%

On the Step 1- Energy Utilities tab, select the units of the energy datacollected. Also, enter the generation efficiency values for each energysource (if needed).

70

Step 2: Data Entry

EnPI Tool v3.02metric, © 2011 Georgia Tech Research Corporation

1000 Data Points Max

DateElectricity

(kWh)Natural Gas

(GJ) [None] (GJ) Slurry (tons) Dry (tons) Total (tons)

MeanMonthly

Temp ( C )

MeanMonthly Dew

Pt. ( C ) HDD CDD1 01/01/07 5,826,876 108,362 22,667 31,275 53,942 5.6 -2.8 721 02 02/01/07 5,588,342 108,783 22,490 33,691 56,181 8.9 2.8 485 03 03/04/07 5,906,176 102,723 29,513 29,614 59,127 14.4 9.4 235 114 04/04/07 6,116,421 129,144 27,092 35,683 62,775 16.7 11.1 116 3535 05/05/07 6,267,905 119,629 28,553 35,378 63,931 21.7 16.7 4 1946 06/05/07 6,020,986 121,938 28,172 36,209 64,381 24.4 19.4 0 3307 07/06/07 5,893,577 134,257 28,915 35,491 64,406 26.1 21.7 0 4258 08/06/07 5,664,996 121,641 29,123 31,724 60,847 26.7 22.2 0 4519 09/06/07 5,533,966 123,068 23,534 35,441 58,975 22.2 16.7 12 238

10 10/07/07 5,975,510 104,574 27,304 33,924 61,228 17.8 12.2 71 4211 11/07/07 5,129,101 87,914 25,144 25,070 50,214 14.4 8.9 251 2912 12/08/07 6,277,006 122,705 29,125 35,425 64,550 5.6 0.0 701 013 01/08/08 6,192,191 111,426 24,909 29,321 54,230 6.7 0.0 666 014 02/08/08 6,045,216 127,055 24,932 32,610 57,542 7.2 1.7 574 1

Utilities Independent Variables

In the Step 2-Data Entry tab, either type or copy and paste the dates and data associatedwith the utility and potential drivers.

Note: There are 1,000 data rows available in the Step 2-Data Entry tab. This should besufficient for monthly or weekly data.

71

Step 3: Data Review

72

Interpretation• P-value for each X and Y• P-value is the probability that the X and Y pair are not

correlated.• If the p-value is less than 0.1, there is less than a 10%

chance that the X and Y pair are not correlated.• Determine statistically significant relationships• Review scatter plots worksheet: Step3 – Data Review

(graph)• Determine if results make sense

Step 3: Data Review

73

Step 4: Y1-Y3 Regression

First select the period that is beingmodeled by adjusting the boxes that arelabeled “Model Year First Row” and “ModelYear Last Row”.

• Select the variables to be included in themodel based on the analysis completed onthe Step 3-Data Review tab.

• Ensure appropriate variables aredesignated “Yes”. If there is an unwantedvariable in the list, click on the drop downbox next to that variable, and change it to“No”.

• Click “Evaluate Model” button.74

75

Step 4: Y1-Y3Simple regression – one dependent variable

Interpretation

1. Review the p-values at the bottom of the data entry chart. Ensure the p-value for eachvariable is less than 0.10. Variables that have high p-values should be removed from theregression equation. This can be done by selecting “No” next to the variable name at thetop right of the screen. Then hit the “Evaluate Model” button again.

2. The F-test is a test of model significance. The F-test p-value for the model is located atthe bottom of the Step 4-Regression tab. Ensure the p-value for the model is less than0.10.

3. Review the R2 value for the regression equation. (The R2 value quantifies the amount ofvariation in the dependent variable, Y, which is explained by the regression equation.Ideally, you would like for the R2 value to be high, indicating that you have a model thatexplains a large portion of the variation in energy consumption.)

4. If the R2 value for the model is low, review the factors to determine if a factor that canimpact energy usage has been overlooked.

5. Determine, based on process knowledge, whether the regression makes sense.

Step 4: Regression

76

Step 5: EnPI1. Confirm that the modeled periods are the same for each of the utilities. This

can be done using the table labeled “Confirm Modeled Period for EachUtility Are Same” at the top middle of the worksheet.

2. Confirm that there are no issues with the previously defined models usingthe information provided at the top left of the Step 5 worksheet as

77

Step 5: EnPI – Forecast Method1. Select “Forecast” from the “Select Modeling Method” drop down menu.

2. Then select the year that will be evaluated using thebaseline model.

EnPI Tool v3.02metric, © 2011 Georgia Tech Research CorporationConfirm Modeled Period for Each Utility Are SameUtility Electricity Natural Gas [None]First Row 01/01/07 01/01/07 01/00/00Last Row 12/08/07 12/08/07 01/00/00

Select Modeling Method Forecast

Year Zero Last YearLast Year of Evaluated Period, First Row 01/14/09Last Year of Evaluated Period, Last Row 12/21/09

Performance Improvement (+) or Decline (-) 4.3%

Tool calculates total %performanceimprovement

78

Step 5: EnPI – Data Validation• The Data Validation Check at the bottom

of the Step 5 worksheet verifies themodel is being appropriately applied.

• The top section includes characteristicsof the modeled year data.

• The second section shows the valid datarange, and the third section gives theaverage value for each variable of theyear chosen to be evaluated.

• If the average of the evaluated year fallswithin the valid data range, then themodel is still valid.

The model validity can also be verified at thetop left of the Step 5 worksheet.

79

Exercise: Use the tools with your own data

Establish your own model

Is it good?What does it tell you?

If not good (low R2) what does it tell you?Why is is low?

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81

82


Exercise(mins)

Breakduration

StartTime

EndTime




Planning workflow

83





action plans




(SEUs)


each SEU

System Optimisation

• Examine the whole system and notindividual components

• Establish user requirements andspecification

• Examine opportunities with use• Examine opportunities with distribution• Examine opportunities with generation last

84

Typical system approach process

• What does the user need?• Consider variations, e.g. seasonal, occupancy,

production schedules, alternative services, etc.• Optimise use of the service

• How is it used, operations, controls, etc.• Optimise distribution of the service

• Leaks, pressure drops, insulation, etc.• FINALLY optimise generation of the service

• Boilers, chillers, air compressors, pumps, etc.

85

Pump system example1. Minimise user

requirement2. Shut bypasses3. Determine actual

flow and pressurerequirement

4. Reselect motorand pump

5. Replace 150m3/hwith 25m3/h

6. Save 75% or176MWh p.a.

28kW

6kW

86

A profile of losses operating a 5000 kW boiler with NG at60% firing rate (annual fuel bill = $800,000)

Boiler LossesStack Losses 18% $144,000Blowdown Losses 4% $ 32,000Surface Losses 3 % $ 24,000

28% $200,000Distribution System Losses

Insulation Losses 7% $ 56,000Steam Leaks 6% $ 48,000Blowing Traps 5% $ 40,000Flash Losses 11% $ 88,000Return Losses 9% $ 72,000

38% $304,000

Combined Losses 66% $504,000System Efficiency 34% $296,000

Steam System Perspective

87

Baseload

• The energy you usewhen there is noproductive activity

• Very often a majoropportunity forimprovement

• Measure andanalyse baseload ifit is significant

Baseload

Production Level (tonnes)

88

Examine potential for renewable and alternativeenergy sources

• Which renewable sources are available?Solar (thermal or photovoltaic)Wind powerBiomass

• Which renewable technologies are economical with theseresources?

• Which alternative energy sources are available?Waste heat recoveryFuel switching

• Which might be economical?Cogeneration (Combined Heat and Power (CHP)

89

90


Exercise(mins)

Breakduration

StartTime

EndTime



ER5 Baseline and EnPIs, EnPI tool 30 45 14:00 15:15Break 15 15:15 15:30ER7 Technical energy audits 15 15:30 15:45

ER8 Identify energy saving opportunities 15 40 15:45 16:40TOTALS 2.75 3.25 1.25

Planning workflow




8. Identify opportunities forimproved performance,

review and decide on actionplans




(SEUs)


foreach SEU

91

Sources for Identifying Improvement Opportunities

ECOsDriveridentification

SEU analysis

EnergyAssessments

SystemOptimisation

study

Staff ideas

ECO = Energy Conservation Opportunity

92

ECO Database

• Develop a list of all potential ideas• Select items for implementation• Plan and manage their implementation

93

• Compile a list of opportunities from energy

assessments, employee suggestions, etc.

• Determine and document prioritization criteria

• Apply the prioritization criteria uniformly to

develop a prioritized list of opportunities

Prioritization of Opportunities

94

Which opportunities to implement?

95

Typical Barriers

• Lack of opportunity identification• Neglecting employee input• Focus on one method for opportunity identification• Failure to establish prioritization criteria• Failure to document prioritization criteria and decisions to

ensure consistency• Failure to prioritize• Preconceived ideas about the effectiveness or not of

some technologies

96

Comprehensive list ofopportunitiesList of prioritized opportunitiesAdequately focuses

organizational resources

Value to the Organization IdentifyingOpportunities

97

Exercise – Energy SavingOpportunities

Populate the tool

98