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Utilities benefiting from Big Data Tech
One sector that is particularly set to profit from increased use of big data analytics this year is utilities. Electricity, gas and water suppliers are all finding new ways to analyze their systems and monitor customer behavior to gain better insights about their activities.
This is according to Engineering and Technology Magazine, which observed that a new generation of smart meters, intelligent grids and improved customer relationship systems is presenting a huge opportunity for utilities firms to streamline their operations. It highlighted figures from GTM Research that forecast expenditure on data analytics tools from this sector will grow from $700 million in 2012 to $3.8 billion by the end of the decade.
It added companies will be expected to take on data management platforms based on tools such as Hadoop and other massively parallel processing big data technologies. These solutions will be deployed for a wide range of purposes, going far beyond traditional uses such as marketing.
For instance, the use of smart meters will generate a huge amount of new data – with some estimates suggesting these devices could create up to 1,000 petabytes a year globally once the technology is commonplace. As well as providing more frequent readings to boost the accuracy of customer billing, they can also help in areas such as forecasting demand and warning of potential outages when combined with smart grids.
Mark Osborne, from the UK National Grid’s electricity transmission future strategy team, told the publication: “We are trying to get more information and data to make [the grid] more efficient, data about transformers, overhead lines, cables, substations, engineers; these are the bits of information we need to know.”
He added factors such as how hot transformers are getting and how much charge there is on an overhead line can all be gathered and used to support decision-making.
This is likely to be hugely important in the coming years as demand for power grows and strains are placed on aging infrastructure in many countries. Therefore, being able to improve the efficiency of capacity management operations and more closely match supply to demand will be essential. This is particularly true in the electricity sector.
Mr Osborne said: “The big challenge is predicting it: real-time is good, but if you can extract what you need to do in ten or 12 days’ time, that is the key. The important thing about electricity is that you cannot really store it – it has to be generated and then used, and there are always inefficiencies in trying to change it into something else.”
Big data analytics solutions for this also need to take into account external factors, such as the weather. In hot climates, for instance, the sudden emergence of a cold front can have a huge impact on demand and utility companies need to be more proactive in how they respond to this.
“Also, as people start putting in more renewable energy equipment, there is a noticeable shift when cloud cover goes over them and that can create big changes in supply, particularly when you go from having surplus energy to when it just dips,” Mr Osborne said.
Scottish Power, a forward-looking energy company in Glasgow, Scotland has long valued analytics in their business. They have been using an in-memory analytical platform from Kognitio Cloud as their advanced analytics engine for a number of years.