Energy Management White Paper No. 5Understanding your energyconsumption dataMay 2013

WHITE PAPER

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Energy Management White Paper No. 5 (May 2013)

Understanding Your Energy Consumption Data

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INTRODUCTION Based on Peter Drucker’s original statement “What gets measured, gets managed”, the

mantra of energy management is “If it isn’t measured, it can’t be managed”.

These days, it is relatively easy to achieve a granular level of energy consumption data,

regardless of application or sector. Data on energy consumption is the lifeblood of good

energy management practise, which itself can bring spectacular results in reducing

energy costs.

Highly competent equipment and software, readily and inexpensively available,

presents us with a potential deluge of data and a plethora of analysis options. The

danger for people untrained or inexperienced in energy management is a state of being

“data rich and information poor” – having so much data and not knowing a) if it is the

right data to be collecting b) what it really means c) whether it is reliable d) whether it

is actionable e) if people know what to do with it and how f) how to assess changes and

outcomes.

Most organisations which have energy data collection and management systems

installed seldom get the best out of them. These systems often appear “simple” but in

truth are so sophisticated, so capable, one can liken them to full-blooded race cars – and

they require skill and knowledge to drive them to their full capacity, to extract

maximum benefit.

Astonishingly, some energy data management systems have over 400 reporting

templates. Identifying and gathering the right data and configuring the right reports

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requires not only an understanding of the system but more especially, a deep

understanding of energy management and the organisation’s activities, processes,

objectives and goals.

In this paper we demonstrate the importance of one aspect: energy management -

Investigative Data Analysis.

What is data analysis and what is being looked at?

For many industries, energy reduction through energy management is now widely seen

as part of operations, good for reducing overheads and carbon emissions. Savings can

go straight to the bottom line.

However, many energy managers or executives with such responsibilities lack the

training and knowledge to interpret energy consumption data. Different types of data

are akin to a series of colours which will only a reveal a picture when applied,

manipulated and structured correctly.

Therefore, it is important

that different techniques

are applied to create a

useful picture. The

results should provide a

good idea where to

begin, raising many

questions, prompting

investigations and

actions which result in

significant energy

reductions.

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Very often data gathering and analysis is the very starting point of energy efficiency

initiatives. It provides the baseline against which the effectiveness of future

improvements and interventions are assessed. Assuming that the ‘correct’ data has been

obtained and data analyses will be carried out. What type of analysis will be performed?

What outcome is expected from the exercise?

Investigative Data Analysis (IDA).

Investigative Data Analysis combines traditional data analysis techniques such as:

a) Weekly, monthly, annual total consumption trends.

Such trend data only provides factual information about energy used but does not take

into account other factors such as production changes, new process added etc, that

might also cause increases in energy use.

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b) Year on year comparisons.

Again, year on year comparison tells us that energy use in 2012 increased significantly

over 2011.

What it doesn’t tell us is that, in this case, the increase was due to expansion of the

factory with several lines added.

What it also doesn’t tell us is that production of products did not increase but

production of recycled raw material increased but which has not been accounted for as

“products produced”.

From a benchmarking point of view, kWh per tonne output would have rocketed

indicating inefficient production but in reality, this was not the case.

As you can see, interpretation of data in the absence of intimate knowledge of site

operations will skew our conclusions.

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c) Simple regression analysis (establish energy use with a driver (e.g. production

information, operating hours etc.).

A simple scatter graph can provide important information about the relationship

between the electricity used and the production weight, in this case, plastic products.

d) Benchmarking (e.g. kWh/m2) & League tables are what we would call good

“1st pass” sorting tools. This is useful when dealing with multi-site portfolios (e.g.

retailers).

It provides a view of, say, top & bottom 20 performing and non-performing sites based

typically on utility-meter data. This provides a focus and narrowing of sites for further

analysis.

With more in-depth techniques such as:

i. Comparative ratios – ideal multi-sites with similar activities.

This technique eliminates the need for specific energy consumption (e.g.

kWh/m2) information.

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The idea is that every such site behaves in a similar way in terms of operations

therefore the average consumption ratio over specific periods should be very

similar. Any large variation to the ratio warrants an investigation.

ii. Sub-meter data analysis at component level (e.g. compressor, computer server

equipment)

This is applied to data recorded at component level. It is ideal for energy

efficiency projects where it is confined to a specific system.

iii. Testing hypotheses by taking actions (make changes immediately).

This is probably one of the more effective and convincing methods - when it is

possible. The idea is to gather and compare half hourly consumption data

immediately (a day or two) before and after taking energy reduction actions.

This is especially useful when preventing base-load (night) wastage, challenging

‘normal’ practises and setting of a new energy baseline.

e) Aggregate Energy Efficiency Method

This is also known as the NOVEM method, which was developed in the Netherlands. It’s

especially useful when setting relative targets when there is a diverse product mix. It

aggregates energy efficiency measure incorporating a simple means of correcting for

distortions introduced by changes in product mix. In a simple site measure, distortions

can arise where energy intensities vary between product groups. The NOVEM

methodology is also capable of handling target adjustment for unanticipated changes in

product mix and output levels

Investigative Demand Analysis (IDA) will:

i. Reduce base loads and set targets.

ii. Reduce shoulder period energy consumption.

iii. Reveal and compare efficient and inefficient multi-sites.

iv. Encourage operation changes to meet the required profiles.

v. Provide a means to ‘normalising’ and measures relative changes to

energy use.

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Application of Investigative Data Analysis.

IDA can be applied to all types of industries. This is especially effective for multi-site

retailers, offices, schools, hotels, manufacturing facilities etc. The analysis will yield

information - ‘clues’ - which will raise investigative questions that will ultimately lead to

energy reduction solutions.

Typical scenarios:

Office Type

In most cases, the profile will be very similar on a daily basis with a certain amount of

variation between summer and winter periods.

Here are typical questions for which you should know the answers. If you don’t, then

you are probably not managing your energy effectively:

What would a typical weekly profile look like? Does it look like a ‘top-hat’ or a

stretched ‘n’?

What should be a typical base load of such an office be?

Do you know the level of lowest practically achievable base-load?

Is plant and equipment operating at “optimum” with correct set points?

Do you have the required controls?

Thorough Investigative Demand Analysis will typically save 10 – 20% of consumption

even if you have carried out some energy reduction measures.

The Manufacturing Environment

There are a number of IDA steps here that may be similar to that for an office. However,

a typical manufacturing environment may have many energy intensive systems (such as

compressed air, refrigeration circuits, large pumps, motors etc.) not to mention the

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process equipment. It is very important to determine the overall distribution of energy

use and whether it is a 24/7 operation.

Take a typical example of a plastic manufacturing facility. Majority of energy goes into

injection moulding machines, grinders, compounders etc. Energy data collected at

machine sub-metering level revealed that heaters had been left switched on for days

when the machinery was not scheduled for production.

Another area was compressed air. When compressor energy consumption was

compared between (a) complete shutdown (b) during full production and (c) partial

production, it was noted that energy used during full and partial production was very

similar. But what was the cause? Air was still supplied to process machines with

‘process’ leaks. An interlock system was put in place to shut off air supply when the

main machine was switched off.

Thorough Investigative Demand Analysis will typically save 5 – 15% of consumption

even if you have carried out some energy reduction measures.

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Multi-retail & Entertainment Environment

In a multi-retail environment, the operations will be similar to that of an office type

environment with slight variations between opening, closing hours and post-closing

hours (re-stocking, stock takes etc.). In some entertainment centres, there can be

variable periods of activity - but plant and equipment may not be correctly scheduled to

coincide, leading to wastage of energy. Quite often, these sites have poor control of

HVAC systems, sometimes running 24/7. Also, back office energy wastage can occur

through e.g. split air-conditioning and lighting.

Thorough Investigative Demand Analysis will typically save 10 – 15% of energy consumption,

even if you have carried out some energy reduction measures. Furthermore, actions taken

successfully in one store/site can be emulated and replicated throughout the whole estate

giving an even higher of return.

CONCLUSION

Having the correct level of data and careful analysis carried out will provide vital information as

to where to focus energy reduction efforts. This often saves time, effort and often the greatest

value.

Investigative Data Analysis identifies data quality deficiencies, shortens energy project

identification to yield immediate results through hypotheses testing with real actions taken on

the spot.

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Contact us: Drumbeat Energy Limited – Specialists in Energy Management Regent’s Place, 338 Euston Road, London NW1 3BT Telephone: 020 7078 4103 Email: info@drumbeatenergy.com Web: www.drumbeatenergy.com