UNIDO EnMS Expert Training Module 1 Day 2€¢ Manual readings • Meters accurate and working •...
Transcript of UNIDO EnMS Expert Training Module 1 Day 2€¢ Manual readings • Meters accurate and working •...
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UNIDO EnMS Expert Training
Module 1
Day 2
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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
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Planning workflow
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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
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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?
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• 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
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Total PlantEnergy
ElectricityOff-site
GeneratedSteam
Natural Gas Propane
Identify all energy sources that cross the fence line!
Utilize flow chartsEnergy sources
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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)
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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
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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
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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?
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TopicDuration(hours)
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
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
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Planning workflow
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
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Information
• Simple trends
• Annualised trends
• Trend of average unit price (AUP)
• Trend of annualised use vs target
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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
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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.
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See you in 15 minutes!
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TopicDuration(hours)
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
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
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Planning workflow
1. Energy bill and sub-meter data
2. Analyze past, presentand 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 for
each SEU
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Significant Energy Uses
Significant component of the organizationconsumption
Equipment, processes, facilities, systems Considerable opportunity for improvement Determined by organization! Document methods and criteria
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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
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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
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• 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
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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
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SEU Pie Chart
27.07%
11.44%
33.33%
28.16%
Significant Energy Uses For MiningOperation
Blunging
Steam System
High Shear
Other
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Connections to Significance
Significantuses
Objectives,targets andaction plan
Competence,training andawareness
Operationalcontrol
Monitoring,measurementand analysis
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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!
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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
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Exercise – Significant Energy Uses
• Use the ER3 tabs• Motor List• SEU list and calculation
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TopicDuration(hours)
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
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
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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
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Planning workflow
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1. Energy bill and sub-meter data
2. Analyze past, presentand 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 for
each SEU
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What does this tell us?
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Same gas data in annualised view
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Previous gas data vs heating degree days (HDD)
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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!
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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
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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
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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
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Measurement Plan
• Do you have enough instrumentation to develop yourEnPIs?
• List additional instrumentation needed if any
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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!
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See you in 15 minutes!
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TopicDuration(hours)
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
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
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Exercise
• Continue with the exercise
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TopicDuration(hours)
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 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
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Planning workflow
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1. Energy bill and sub-meter data
2. Analyze past, presentand 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 for
each SEU
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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
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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
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■ 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
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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
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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
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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)
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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
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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
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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
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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
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Difference between expected and actual
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CUmulative SUM of difference (CUSUM)
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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
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Cumulative savings can be tracked
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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)
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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)
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Value to the Organization
• Understand energyuse for baselineperiod
• Have a comparativepoint for measuringimprovement
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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
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• Drivers worksheet• EnPI Tool• EnPI Tool Instruction Guide
Tools
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EnPI Tool
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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
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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).
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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.
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Step 3: Data Review
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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
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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
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Step 4: Y1-Y3Simple regression – one dependent variable
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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
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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
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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
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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.
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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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See you in 15 minutes!
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TopicDuration(hours)
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 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
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Planning workflow
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1. Energy bill and sub-meter data
2. Analyze past, presentand 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 for
each SEU
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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
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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.
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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
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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
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Baseload
• The energy you usewhen there is noproductive activity
• Very often a majoropportunity forimprovement
• Measure andanalyse baseload ifit is significant
Baseload
Production Level (tonnes)
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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)
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90
TopicDuration(hours)
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 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 15:45
ER8 Identify energy saving opportunities 15 40 15:45 16:40TOTALS 2.75 3.25 1.25
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Planning workflow
1. Energy bill and sub-meter data
2. Analyze past, presentand future energy use
7. Technical energyaudits
8. Identify opportunities forimproved performance,
review and decide on actionplans
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
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Sources for Identifying Improvement Opportunities
ECOsDriveridentification
SEU analysis
EnergyAssessments
SystemOptimisation
study
Staff ideas
ECO = Energy Conservation Opportunity
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ECO Database
• Develop a list of all potential ideas• Select items for implementation• Plan and manage their implementation
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• 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
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Which opportunities to implement?
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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
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Comprehensive list ofopportunitiesList of prioritized opportunitiesAdequately focuses
organizational resources
Value to the Organization IdentifyingOpportunities
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Exercise – Energy SavingOpportunities
Populate the tool
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