More power to the data

By Tamsin Oxford
According to a recent report by Infosys entitled ‘More Power to the Energy and Utilities Business, From AI’, the current utility business...

According to a recent report by Infosys entitled ‘More Power to the Energy and Utilities Business, From AI’, the current utility business model is under pressure from customers, prices, competitors, regulators, renewables, and energy storage technologies. Questions around how this can evolve and adapt to address environmental impact while maintaining profitability are prevalent in the boardroom and sector as a whole. How can it address the challenges of competitor and customer, wooing the one from the other with intelligent insight and understanding? And how does the energy sector gain all this insight so that it can be used intelligently?

The answers to these questions lie within artificial intelligence (AI) and machine learning, and the ability of these touted technologies to transform industries by providing the toolkits required to translate yesterday into today. Infosys believes that the data-driven model focusing on customised energy management solutions will offer the competitive advantage that the energy industry requires to overcome these challenges and move towards a more sustainable future. It is a belief that many in the industry share.

“You don’t need artificial intelligence to realise that robots are entering the industry,” says Janette Marx, Global Chief Operating Officer at Airswift. “The sector has a unique blend of need and ability to lead the way in embracing automation and stands to benefit hugely from it.”

The Infosys report found that the energy sector leads the global way when it comes to the adoption and application of AI solutions, with 29% of organisations stating that these technologies are already deployed and working as anticipated. The key drivers behind the implementation of these technologies are to automate IT processes (62%), automate business processes (61%), and increase innovation (60%), with boosting productivity (52%), improving decision making (47%) and increasing revenue (46%) also playing a significant role. In addition, improvements in time to market, cost savings and customer experiences are also pushing the AI buttons.

“For many, automation is the biggest change to the workplace since the industrial revolution,” adds Marx. “The types of activity that are most susceptible to automation are those that are repeatable and programmable, as well as the tasks that pose a high risk to humans. And this industry has a wealth of roles that tick both boxes.”

AI and machines can minimise site visits through the use of remote monitoring, diagnostic data feeds, and analytical insight into real time data. This can not only limit the number of human inspectors to sites, but could reduce high risk incidents through preventative monitoring. The impact of both these factors on cost and human engagement are significant.

The industry stands to gain immensely in terms of personnel safety – think about the thousands of high-risk tasks where human intervention can be eliminated,” says Marx.

It’s an opinion voiced by the McKinsey Global Institute which has estimated that the growth of global productivity could increase by 0.8% thanks to automation – that’s a significant percentage when compared with the 0.6% gained from IT between 1995 and 2005. This shift in productivity could allow for the industry to create operational efficiency gains to maintain margins, providing a neat solution to the lean times within which the sector is currently operating.

Alongside worker safety and budget friendly savings, AI also slips some of those important green credentials into the energy industry’s pocket. In order to achieve a greener, smarter and more connected energy marketplace, technology is essential. One organisation, Limejump, has already invested into the harnessing of big data and deep learning to re-engineer the energy utility model.

“The amount of data already being processed is true Big Data,” says Joe McDonald, Vice President of Sales at Limejump. “We read and analyse the data every 0.1 seconds and our algorithms can now even begin to predict National Grid frequency to allow for an even faster response. This is integral for keeping the lights on in the UK.”

Limejump’s use of data and the Internet of Things (IoT) to create a platform that both generators and consumers can access has created a solution much like the energy equivalent of Uber or Airbnb. It allows for scalable growth and insight plus the ability to assess every site, improve forecasts over time per site, and offer all energy providers access to a plethora of market opportunities.

“The tool has levelled the playing field by removing traditional market constraints through sophisticated algorithms and automated processes,” adds McDonald. “So smaller generators, even those as small as 5kW, now have access to the same market opportunities as the large power plants.”

The ability for technology to learn and adapt means that energy providers can turn some machinery off when there is too much demand, or ramp generators down when the grid is over supplied – adapting supply to meet relevant demand in real time. A capability that has, until now, been something of an industry unicorn: nice to dream about, but near impossible to achieve.

In windier climes, where offshore renewable installations turn endlessly in their quest for power, automation and robotics could very well be the answer to an expensive question: maintenance. The cost of maintenance is significant for offshore renewables as they are built in conditions that aren’t easy to endure – vessels and staff sent to these farms have to deal with distance, risk and weather while the businesses carry the costs.  Fortunately, work on drones, blade crawlers and autonomous underwater vehicles has already begun. There are even plans to create autonomous boats that can travel to offshore developments with technology designed to specifically service an entire field.

This ubiquity can extend beyond just maintenance: AI and machines could take part in the search for new locations by assessing environments, tides and seabed conditions and relaying the data back for instant analysis and implementation. The potential of this technology has already started to show itself, flexing robotic muscles in reducing the risk to human life, improving productivity, cutting operational costs and finding new investment opportunities. For a sector surrounded by challenges unlikely to disappear any time soon, AI and machines look set to help it transform and grow and take advantage of opportunities that were once thought out of reach.


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