"10 year vision on the Energy Trading industry, robotics and more..." [Interview with Andrew Singleton, Partner, Baringa Partners]

October 2, 2018

 

 

"It’s critical to get the foundations right today, otherwise in 5-10 years from now, organisations will find themselves materially behind the game"

The energy trading industry has been going through a lot of changes recently and it is anticipated that more will come our way, whether the change will come from disruptive technology or changes in consuming habits, just to name a few. Adapting to or preparing for it is crucial so as a company you are not left behind. We had a chat with Andrew Singleton, Partner at Baringa Partners, about his views on what energy trading will look like in the future, as well as his dealings with robotics, and data. Find out more below:

As part of ETOT 2018, you’ll provide the audience with your 10-year vision on the industry. Can we get a sneak peek on the big changes and events you see happening in the near (and further) future?

 

As we’re all very aware, the Energy Trading world is currently going through a major shift in dynamics. Last year, at ETOT 2017, I touched on some of the changes in IT Architecture that this is driving (and I’ll briefly mention those again to set the scene), but this year at ETOT 2018, I’m going to project a bit further forward to understand what’s coming down the road.

 

Of specific note, I’ll spend a bit of time looking at the increasing business and consumer awareness of the value of energy flexibility – the rise of the ‘Prosumer’.  I’ll also touch on the linked, but separate, market related to Electric Vehicles, and finally, I’ll have a quick canter through changes in the Data Analytics world. I’ll close with a few thoughts on the “So What” – i.e. how people can start adapting now – it’s critical to get the foundations right today, otherwise in 5-10 years from now, organisations will find themselves materially behind the game.

You’ve been doing a lot of work around robotics with clients. In your opinion, what’s the biggest challenge when it comes to implementing RPA in energy trading?

 

We see a couple of key challenges in this area:

 

1) People sometimes try to use technology alone to solve problems. Robots can be fantastically useful – we have helped organisations select and implement them for everything from back office automation tasks to automating bidding tools around critical auction windows. The risk is that people implement RPA by just putting technology in the middle of an existing process, rather than taking a look at the end to end process. By understanding how the process as a whole can change, you can efficiently and effectively use robots without just baking in bad behaviour and inefficient processes for the long term.

 

2) Upskilling your team is critical. It is important to ensure a level of upskilling of the internal teams to be able to manage and adapt the robots – process will change, and you will want to re-deploy your robots as your business changes – having the internal capability to do this, rather than requiring external help is going to be critical to maintain a low cost and rapidly evolving business – taking time in the initial roll-outs to ensure that the business can truly manage and run the robots will reap rewards in the future.

The amount of data collected and reported by energy trading companies has never been so high. Could you give us some pointers on key areas where this data could be – and should be – used in a meaningful way?

 

There are multiple use-cases in the energy trading space for data and AI, increasingly so as the market focusses on the short term. Whilst there are a number of common themes and ideas, a number will also be dependent on a specific organisations strategy – are they prioritising customers? Are they becoming the most innovative and optimal wind energy provider? Are they optimising a complex end to end portfolio? and so on. For a number of organisations, we have identified powerful use-cases for each of the three areas of AI – Supervised Learning (e.g. electricity price forecasting); Unsupervised pattern recognition (e.g. understanding your customers better) and Re-enforcement learning (although we’re yet to see an Energy Trading company implement a fully automated re-enforcement AI trading algorithm yet!) – there are lots of use cases out there, but choosing the ones that best suit your company and your strategy will be key to help your businesses extract the most value out of data and AI.

Andrew Singleton will be present at the 10th ETOT summit this October in London, to find out more on his session, click here

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