Relevance of Qualitative and Quantitative Data in Sectoral Energy Performance Benchmarking

Sectoral Example- Textile

2

Contents

Need for Sectoral Energy Performance Benchmarking ......................................... 3

Benchmarking types and factors: ......................................................................................................... 4

Model - based Benchmarking .............................................................................................................. 4

Methodology: ...................................................................................................................................... 5

Energy efficiency Benchmarking in Textile Units- A case Study ............................ 6

Energy performance based on Statistical Energy Simulation ................................................................ 6

Data Preparation ............................................................................................................................. 6

Data Mining and exploring the relationship among features: ........................................................... 7

Impact Assessment after statistical simulation: ................................................................................ 7

Energy Performance Index Table: ........................................................................................................ 8

The traditional Energy Performance Index: ...................................................................................... 8

Energy Performance Index – Using Statistical Tool ........................................................................... 8

Critical Analysis for Using Model Based Benchmarking:...................................................................... 10

Conclusion ........................................................................................................... 11

3

Need for Sectoral Energy Performance

Benchmarking Energy Efficiency market place is evolving and is moving more towards market based mechanism, Cap & Trade markets and long term reduction targets both at Country/Sector levels.

Evolution of the market place puts a certain risk on all stakeholders, as we will see in the info graph below.

Market Based Mechanisms

Cap & Trade

Long term reduction targets at

Country/Sector level.

Stringent MRV requirements.

Industries

•Exposure to signifgicantly high targets with respect to improvement potential / sectoral benchmarking

Goverments

•Market stability on account of "partial" benchmarking.

Banks/Financial Institutions

•Potential and EE Project effectiveness dependent on EnPI driven performance evaluation.

ESCOs

•Perofrmance contracts/investment subject to normalisation factors/forecasting models etc.

Benchmarking is a

positive, proactive

process to change

operations in a

structured fashion

to achieve

superior

performance. The

benefits of using

benchmarking are

that functions are

forced to

investigate

external industry

best practices and

incorporate those

practices into

their operations.

This leads to

profitable, high-

asset utilization

businesses that

meet customer

needs and have a

competitive

advantage

Comprehensive use of Data Analytics at different stages

i.e. Policy Design, Implementation and evaluation holds

the key to mitigating the risks for all stakeholders!

4

Benchmarking types and factors:

Internal benchmarking is done to check and follow-up on the EnPIs1 of a plant or group of plants within the same company. It mainly consists of quantitative data which can be both dynamic and static data. Typical examples of factors considered for evaluation of EnPIs are Production capacity, count, TPI, Spindle speed, Capacity Utilization, Product Mix etc. External benchmarking is done to check and follow-up on the EnPIs of the plants at the cluster or the country level. It mainly consists of Qualitative data with factor such as Technology, Labor Skills, Weather, Age, Automation Level etc.

Model - based Benchmarking

Model-based benchmarking calculates benchmarks based on a simulation model of a facility’s performance. The simulation models have the advantage that they can estimate the contribution of a wide range of factors and parameters to facility energy performance in order to determine targets and compare retrofit scenarios. The detailed simulation models require detailed facility information (constructional, production and operational data as inputs). Statistical analysis benchmarking (also known as regression model-based) is being deployed and used in this case study. Here we try to make benchmarking process for energy performance indicators by means of multiple regression analysis, where the relationship between electricity consumption and the explanatory factors e.g. different product types and Count is developed.

1 Energy Performance Indicators

Benchmarking

Internal

Quantitative DataCombination of Dynamic

and Static Data

External (Sectoral)

Combination of Qualitative and Quantitative Data

Mostly static data.

Depth of Data (Type and Volume) Quality and consistency of Data

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Methodology:

• Energy Intensive Industries can track their performanceof EnPIs on a cluster as well as global level. To improveproductivity, reliablity, and quality of the products.

Establish the Goal for Benchmarking

•Need to identify the metrics requirement such as UKG,specific energy consumption, utility efficiency, equipmentefficiency, capacity utilization, labour productivity. Alsothe frequncy of the output metrics required i.e. daily ,monthly, quarterly or yearly.

Identify Output Metrics Needed to Support the Benchmarking Goal

•Data inputs required to generate the desired outputmetrics such as production quantity, raw materialconsumption, Electrcity consumption, gas, liuid fuel,steam consumption, motor parameters. Qualitative datasuch as Technology, Productivuty, weather, labor skills,Automation.

Identify the Data Input Requirement

• Select a tool to display the output metrics whetherspread sheets or web based platforms. The tools shouldnot be too difficult to use to the users. it should take carethe usablity and comapatiblity of the users.

Select a Benchmarking Tool

•Need to specify the collection techniques such as dataentry direct into the platform or in the spreadsheet. Alsothe frequency of the data entry. Also the ways ofcollection and aggregation of data should be thespecified.

Determine a Collection Method

•Performing onsite or offsite verification to check themetrics and magnitude of the variables by comparaingwith the cluster or national level trend.

Consider the Data Verification Process

•Compare with energy simulation /energy modelling tocompare the perormance of the plant with potentialperformance. To go into detailed anlysis identifying lowperforming plants within a group or cluster.

Evaluate Analysis Techniques

•Communicate the idea and the value to ensureinolvement of all stakeholders.

Communicate the Plan and Formalize the Process

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Energy efficiency Benchmarking in Textile Units- A case Study

Energy performance based on Statistical Energy Simulation

Here we are considering datasets for 3 months of three textile plants where we have the independent

variables: Product types i.e. P1, P2 and P3 in (Tons) and counts of production such as C1, C2 and C3 and

the dependent variable is Electricity consumption (kWh).

Data Preparation

a) Missing Value Treatment:

We can detect all the missing values and remove them to make the data more compact so that we can

build a standard model with more robust features. After detecting the missing values we will remove all

the missing values to make the data compact for further processing.

b) Outlier Detection:

We can use different graphical techniques for the detection of outliers. Graphical techniques such as Histogram, density plot and box plot can be used for the detection of outliers. With use of box plot it is clearly visible that the data does not contain any outliers.

After plotting Box plots of variable for Plant 1 above we can statistically detect that factors P2, P3 and C3 have outliers. Similarly we can find outliers in datasets of Plant 2 and Plant 3.

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Data Mining and exploring the relationship among features:

We can apply data mining techniques to get measures of central tendency such as mean, median, quartiles, min and max values for the numerical variables. Also we can use scatter plot and box plot to see the relationship between the features. In the figures the three types of products P1, P2 and P3 are represented by labels 1, 2 and 3 for the three Plant 1, Plant 2 and Plant 3 respectively. We can observe that Plant 2 having the widest range in the individual quantity for types of production. The production level for plant 3 is showing more scattered in the production of three types.

Plant 1 Plant 2 Plant 3

Impact Assessment after statistical simulation:

We can visualize the individual impact of coefficient of independent variables (Production and Count of different types) for all the three plants on electricity consumption. The impact of factor P1 is highest among all the KPIs in Plant 1 and Plant 3 i.e. 40% for Plant 1 and 42% for Plant 3 . Considering C1 factor we can interpret that Plant 1 has least impact of 4% but Plant 2 getting highest impact of 34% for variable C1.

40% 22%31%

4%

3% 20%

22%25% 15%

34%

3%19%

42%

17%29%

11%1% 8%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

P1 P2 P3 C1 C2 C3

Plant 1 Plant 2 Plant 3

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Energy Performance Index Table:

The traditional Energy Performance Index:

Plant Average Production (Tons)

Average Electricity Consumption

( kWh)

UKG Energy Performance Index

Plant 1 45.00 101537 2.26 1.01

Plant 2 131.00 355777 2.72 1.21

Plant 3 25 56094 2.24 1.00

Energy Performance Index – Using Statistical Tool

Case 1

Plant Production % Product Type Average Production(Tons): 45 Counts Calculated

Plant 1 60% Pro1 27.00 26 KWh 116476

6% Pro2 2.70 27 UKG 2.59

34% Pro3 15.30 23

Production % Product Type Average Production(Tons):131 Counts Calculated

Plant 2 60% Pro1 78.60 26 KWh 354638

6% Pro2 7.86 27 UKG 2.71

34% Pro3 44.54 23

Production % Product Type Average Production(Tons): 25 Counts Calculated

Plant 3 60% Pro1 15.00 26 KWh 58959

6% Pro2 1.50 27 UKG 2.36

34% Pro3 8.50 23

Energy performance Index Table

Plant Average Production (Tons)

Expected Average Electricity Consumption

( KWh)

UKG Energy Performance Index

Plant 1 45 116476 2.59 1.10

Plant 2 131 354638 2.71 1.15

Plant 3 25 58959 2.36 1.00

In case 1 we have normalized the production quantity product mix and count of the three types of

production. We can observe the change in the magnitude of the UKG and Energy performance index of

the three plants but there is no change in ranking on the basis of Index.

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Case 2

Plant Production % Product Type Average Production(Tons): 45 Counts Calculated

Plant 1 20% Pro1 9.00 26 KWh 100724

42% Pro2 18.90 27 UKG 2.24

38% Pro3 17.10 23

Production % Product Type Average Production(Tons):131 Counts Calculated

Plant 2 35% Pro1 45.85 15 KWh 336247

30% Pro2 39.30 38 UKG 2.57

35% Pro3 45.85 25

Production % Product Type Average Production(Tons): 25 Counts Calculated

Plant3 60% Pro1 15.00 26 KWh 59237

6% Pro2 1.50 25 UKG 2.37

34% Pro3 8.50 24

Energy performance Index Table

Plant Average Production (Tons)

Expected Average Electricity Consumption

( KWh)

UKG Energy Performance Index

Plant 1 45 100724 2.24 1.00

Plant 2 131 336247 2.57 1.15

Plant 3 25 59237 2.37 1.06

In Case 2 after change in production mix quantity, the change in the magnitude and the ranking of the

Plants on the basis of Energy Performance Index can be observed.

Energy Performance Index Table

Plant Traditional Way Case 1 Case 2

Plant 1 1.01 1.10 1.00

Plant 2 1.21 1.15 1.15

Plant 3 1.00 1.00 1.06

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So we can infer that energy market based mechanisms should take into account more qualitative and

quantitative features with more depth and consistency before providing any energy or emission

reduction targets which might be very lower or very higher .

Critical Analysis for Using Model Based Benchmarking:

The importance of Qualitative data in Benchmarking cannot be ignored. The qualitative factors can be

sub-categorized and provide a label to have better insight into plant performance. Examples of a few

factors have been listed below:-

I) Technology/Modernity: a) New, b) Medium and c) Old : The plant with new technology will consume

less energy and would have higher productivity.

II) Plant Automation: a) High, b) Medium and c) Low : Plant automation does not help in the lesser

energy consumption (in most cases) it only removes human interference in the process.

III) Weather: a) Dry, b)Moderately Humid and c) Humid : Plant in the dry area will be consuming more

energy as it will require higher humidification to maintain the humidity level for the products to be

manufactured and vice versa.

We see that post normalization the indices have changed; in this case we have only considered 2 factors and 3 units, when we consider a cluster with almost

200 units and with over a dozen factors both the magnitude of the index and the overall rankings can change impacting all stakeholders!

The magnitude and Energy performance index will become completely opposite of the general

trend or can remain the same which can only be told with higher volume and varied datasets!

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Conclusion

The objective is to make best benchmarking tool that caters all the factors significant in the driving the

energy consumption of the plant. With larger volume, greater depth and consistent data more insight

can be brought into the performance level, and also the hidden factors causing the improvement and

deterioration in energy performance can be captured and tracked.

Analysis and indexing to be done by Algorithms, making the process system

driven (Faceless)

Integration of unit wise data through EIMAS or through data logging facility.

Establish Central Energy Efficiency Data Repository

Identify the depth (Type and

Volume) of Quantitative and Qualitative data to be collected

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About E-Cube Energy

E-Cube Energy has emerged as “Thought Leader” around the subject of using Energy Analytics in Industries/SMEs to accelerate energy efficiency. Energy Analytics represents systematic, evidence and data led process in identifying and assessing opportunities to advance energy savings in unit operations in industry. It's beginning to make inroads in Indian industry as a management tool to establish realistic energy savings targets and as a means to measure and verify progress. Our company through its products and solutions helps industries/utilities gets started with their Energy Data Strategy and transition to Faster, Better and Cheaper way of assessing, reporting and managing Energy Performance. To know more about us and how we can help you get started with your EDA strategy visit us at www.eetpl.in or call us at +91 033 40052780