WHITE PAPER
Energy Management White Paper No. 5 Understanding your energy consumption data May 2013
Energy Management White Paper No. 5 (May 2013)
Understanding Your Energy Consumption Data ________________________________________________________________________________
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 1|Pag e
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 begin,
idea
where
raising
questions, investigations
to
many
prompting 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. 5|Pag e
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. 6|Pag e
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. _________________________________________________________________________________________________________ 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 9|Pag e