Big Energy Data Management

Recent times have seen the advent of cloud based services and solutions where energy data is being stored in the cloud and being accessed from anywhere, anytime through remote mobile devices. This has been made possible by web-based systems that can usually bring real-time meter-data into clear view allowing for proactive business and facility management decisions. Some web based systems may even support multi utility metering points and come in handy for businesses operating multiple sites.

Whereas all this has been made possible by increased use of smart devices/ intelligent energy devices that capture data at more regular intervals; the challenge facing businesses is how to transform the large data/big volume of data into insights and action plans that would translate into increased performance in terms of increased energy efficiency or power reliability.

A solution to this dilemma facing businesses that do not know how to process big energy data, may lie in energy management software. Energy management software?s have the capability to analyse energy consumption for, electricity, gas, water, heat, renewables and oil. They enable users to track consumption for different sources so that consumers are able to identify areas of inefficiency and where they can reduce energy consumption, Energy software also helps in analytics and reporting. The analytics and reporting features that come with energy software are usually able to:

? Generate charts and graphs ? some software?s give you an option to select from different graphs

? Do graphical comparisons e.g. generate graphs of the seasonal average for the same season and day type

? Generate reports that are highly customisable

While choosing from the wide range of software available, it is important for businesses to consider software that has the capacity to support their data volume, software that can support the frequency with which their data is captured and support the data accuracy or reliability.

Energy software alone may not make the magic happen. Businesses may need to invest in trained human resources in order to realise the best value from their big energy data. Experts in energy management would then apply human expertise to leverage the data and analyse it with proficiency to make it meaningful to one?s business.

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Sources of Carbon Emissions

Exchange of carbon dioxide among the atmosphere, land surface and oceans is performed by humans, animals, plants and even microorganisms. With this, they are the ones responsible for both producing and absorbing carbon in the environment. Nature?s cycle of CO2 emission and removal was once balanced, however, the Industrial Revolution began and the carbon cycle started to go wrong. The fact is that human activities substantially contributed to the addition of CO2 in the atmosphere.

According to statistics gathered by the Department of Energy and Climate Change, carbon dioxide comprises 82% of UK?s greenhouse gas emissions in 2012. This makes carbon dioxide the main greenhouse gas contributing to the pollution and subsequent climate change in UK.

Types of Carbon Emissions

There are two types of carbon emissions ? direct and indirect. It is easier to measure the direct emissions of carbon dioxide, which includes the electricity and gas people use in their homes, the petrol burned in cars, distance of flights taken and other carbon emissions people are personally responsible for. Various tools are already available to measure direct emissions each day.

Indirect emissions, on the other hand, include the processes involved in manufacturing food and products and transporting them to users? doors. It is a bit difficult to accurately measure the amount of indirect emission.

Sources of Carbon Emissions

The sources of carbon emissions refer to the sectors of end-users that directly emit them. They include the energy, transport, business, residential, agriculture, waste management, industrial processes and public sectors. Let’s learn how these sources contribute carbon emissions to the environment.

Energy Supply

The power stations that burn coal, oil or gas to generate electricity hold the largest portion of the total carbon emissions. The carbon dioxide is emitted from boilers at the bottom of the chimney. The electricity, produced from the fossil fuel combustion, emits carbon as it is supplied to homes, commercial establishments and other energy users.

Transport

The second largest carbon-emitting source is the transport sector. This results from the fuels burned in diesel and petrol to propel cars, railways, shipping vehicles, aircraft support vehicles and aviation, transporting people and products from one place to another. The longer the distance travelled, the more fuel is used and the more carbon is emitted.

Business

This comprises carbon emissions from combustion in the industrial and commercial sectors, off-road machinery, air conditioning and refrigeration.

Residential

Heating houses and using electricity in the house, produce carbon dioxide. The same holds true to cooking and using garden machinery at home.

Agriculture

The agricultural sector also produces carbon dioxide from soils, livestock, immovable combustion sources and other machinery associated with agricultural activities.

Waste Management

Disposing of wastes to landfill sites, burning them and treating waste water also emit carbon dioxide and contributes to global warming.

Industrial Processes

The factories that manufacture and process products and food also release CO2 , especially those factories that manufacture steel and iron.

Public

Public sector buildings that generate power from fuel combustion also add to the list of carbon emission sources, from heating to other public energy needs.

Everybody needs energy and people burn fossil fuels to create it. Knowing how our energy use affects the environment, as a whole, enables us to take a step ahead towards achieving better climate.

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Migrating from CRM to Big Data

Big data moved to centre stage from being just another fad, and is being punted as the latest cure-all for information woes. It may well be, although like all transitions there are pitfalls. Denizon decided to highlight the major ones in the hope of fostering better understanding of what is involved.

Accurate data and interpretation of it have become increasingly critical. Ideas Laboratory reports that 84% of managers regard understanding their clients and predicting market trends essential, with accelerating demand for data savvy people the inevitable result. However Inc 5000 thinks many of them may have little idea of where to start. We should apply the lessons learned from when we implemented CRM because the dynamics are similar.

Be More Results Oriented

Denizon believes the key is focusing on the results we expect from Big Data first. Only then is it appropriate to apply our minds to the technology. By working the other way round we may end up with less than optimum solutions. We should understand the differences between options before committing to a choice, because it is expensive to switch software platforms in midstream. data lakes, hadoop, nosql, and graph databases all have their places, provided the solution you buy is scalable.

Clean Up Data First

The golden rule is not to automate anything before you understand it. Know the origin of your data, and if this is not reliable clean it up before you automate it. Big Data projects fail when executives become so enthused by results that they forget to ask themselves, ?Does this make sense in terms of what I expected??

Beware First Impressions

Big Data is just that. Many bits of information aggregated into averages and summaries. It does not make recommendations. It only prompts questions and what-if?s. Overlooking the need for the analytics that must follow can have you blindly relying on algorithms while setting your business sense aside.

Hire the Best Brains

Big Data?s competitive advantage depends on what human minds make with the processed information it spits out. This means tracing and affording creative talent able to make the shift from reactive analytics to proactive interaction with the data, and the customer decisions behind it.

If this provides a d?j? vu moment then you are not alone. Every iteration of the software revolution has seen vendors selling while the fish were running, and buyers clamouring for the opportunity. Decide what you want out first, use clean data, beware first impressions and get your analytics right. Then you are on the way to migrating successfully from CRM to Big Data.

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What Heijunka is & How it Smooths Call Centre Production

The Japanese word Heijunka, pronounced hi-JUNE-kuh means ?levelling? in the sense of balancing workflows. It helps lean organizations shift priorities in the face of fluctuating customer demand. The goal is to have the entire operation working at the same pace throughout, by continuously adjusting the balance between predictability, flexibility, and stability to level out demand.

Henry Ford turned the American motor manufacturing industry upside down by mass-producing his iconic black motor cars on two separate production lines. In this photograph, body shells manufactured upstairs come down a ramp and drop onto a procession of cars almost ready to roll in 1913.

Smoothing Production in the Call Centre Industry

Call Centres work best in small teams, each with a supervisor to take over complex conversations. In the past, these tended to operate in silos with each group in semi-isolation representing a different set of clients. Calls came through to operators the instant the previous ones concluded. By the law of averages, inevitably one had more workload than the rest at a particular point in time as per this example.

Modern telecoms technology makes it possible to switch incoming lines to different call centre teams, provided these are multi-skilled. A central operator controls this manually by observing imbalanced workflows on a visual system called a Heijunka Box. The following example comes from a different industry, and highlights how eight teams share uneven demand for six products.

This departure from building handmade automobiles allowed Henry to move his workforce around to eliminate bottlenecks. For example, if rolls of seat leather arrived late he could send extra hands upstairs to speed up the work there, while simultaneously slowing chassis production. Ford had the further advantage of a virtual monopoly in the affordable car market. He made his cars at the rate that suited him best, with waiting lists extending for months.

A Modern, More Flexible Approach

Forces of open competition and the Six Sigma drive for as-close-to-zero defects dictates a more flexible approach, as embodied in this image published by the Six Sigma organisation. This represents an ideal state. In reality, one force usually has greater influence, for example decreasing stability enforces a more flexible approach.

Years ago, Japanese car manufacturer Toyota moved away from batching in favour of a more customer-centric approach, whereby buyers could customise orders from options held in stock for different variations of the same basic model. The most effective approach lies somewhere between Henry Ford?s inflexibility and Toyota?s openness, subject to the circumstances at the moment.

A Worked Factory Example

The following diagram suggests a practical Heijunka application in a factory producing three colours of identical hats. There are two machines for each option, one or both of which may be running. In the event of a large order for say blue hats, the company has the option of shifting some blue raw material to the red and green lines so to have the entire operation working at a similar rate.

Predictability, Flexibility, and Stability at Call Centre Service

The rate of incoming calls is a moving average characterised by spikes in demand. Since the caller has no knowledge whether high activity advisories are genuine, it is important to service them as quickly as possible. Lean process engineering provides technology to facilitate flexibility. Depending on individual circumstances, each call centre may have its own definition of what constitutes an acceptably stable situation.

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