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Successful energy transitions require more data, and more data scientists

We see an unprecedented amount of data created every day: currently 2.5 quintillion bytes and the pace is only accelerating with the advent of the Internet of Things. Between 2017 and 2018 alone 90 per cent of all the existing global data was generated. Some estimate by 2020 the entire digital space will reach a mind-blowing 44 zettabytes; 40 times more bytes in cyberspace than there are stars in the observable universe.

At the same time, the advent of big data allows us to better understand the challenge of future energy supply and climate change. Without ongoing digitalisation, both in terms of data and AI, the sustainable energy transition simply will not happen. But at the same time, we need data scientists to make sense and proper use of the numbers we produce.

As we integrate more renewables, and reduce our use of centralised plant, generation shifts from being fairly predictable, to fairly unpredictable. A great deal more intelligence is required to match supply and demand, and new data sources are required too, whether that be greater use of historic data, weather patterns or consumption profiles. We need data scientists to create the insights we need to make the system a whole lot less ‘random’.

By 2030 we’re expecting millions more distributed energy resources (DERs) such as photovoltaic arrays, electric vehicles and smart thermostats to be connected to the grid globally. Along with renewable generation, greater control of these devices is a key enabler of decarbonisation. But they also add another layer of complexity to the grid.

As a massive data network, these DERs also create millions of points of potential weakness. We need data scientists to help armour this network against hack and attack, and unleash the benefits they could provide.

Data scientists continue to be in short supply and despite the sector’s substantial and pressing needs, we find ourselves in competition with other industries to attract and retain a skilled workforce. It is imperative that new routes into the role are opened up to help fill the void.

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The good news is that, thanks to the big tech – Silicon Valley hype, lots of students are interested in training in social media, app design, artificial intelligence, data science and so on. With more courses than ever before it then becomes a question of how the energy sector differentiates itself to students so that they’ll want to come to work for it.

The incumbent model is slowly dissolving to reveal a pioneering new face for industry. Whatever innovations you expect to come out of industries like healthcare and finance, you can now expect from energy too. In some cases, energy innovations are potentially even more compelling, more cutting-edge, more impactful.

Blockchain for trading? Check.

Mobile apps based on gamification theory? Check.

The intersection of artificial intelligence and ethics? Check.

You can have a truly exciting career in data science, IOT or cyber security in energy – the difference is in energy it has real life applications that will better the planet – whether that’s reducing carbon emissions or bringing light in the evenings to an African village for the first time. You only have to look at what start-ups in the sector are doing, it is just as cutting edge as any Silicon Valley tech company. And that’s because if you’re invested in the energy transition then you’re invested in data science and AI – whatever size company you are.

One recent example of how data can revolutionise a process is that of InnoEnergy start-up Gradis. Poland-based Gradis creates energy savings by “hacking” a Google Street Car to photograph and map neighbourhood street lighting. AI identifies the bulb types in the photos and creates a digital twin of all the bulbs in the neighbourhood. It is then able to identify inefficient bulbs and mark them for replacement – this in itself delivers energy cost savings of 30-40%.

Going one step further Gradis also combines multiple data sources such as weather, traffic flow and pedestrian footfall to inform dynamic control of the lights, turning them off when they are not needed to save a further 15-25%.

In the past the lights would have simply been on from dusk to dawn regardless of usage and bulbs would have been replaced when they broke. The use of AI helps to customise lighting down to each bulb ensuring that each lamp is optimised to its individual environment.

Gradis is just one of thousands of examples of companies that are combining data science skills with energy technology expertise; the top two skills needed for the transition. When you combine the two, we really see magic happen.

In the energy sector having data skills makes people more efficient at their jobs but it can also make students more attractive as candidates too. If a student can say “I have an electrical engineering degree, and I studied a few modules of data science” then they become more hireable. That’s because they have learnt to appreciate how data works in the context of the world, and have gained practice in applying it in real-life situations, such as product development for example.

At InnoEnergy, our Masters School students don’t just graduate as sustainable energy engineers. Through workshops, real-world challenges and teaching cases that include data and AI problems we prepare them to solve questions by combining their energy engineering and data/AI knowledge.

Data is ubiquitous and today’s students need to be able to interpret it to understand what it means at a business level. At the next stage, students can gain technical insight and understanding behind the underlying algorithms and mechanisms. Some of our students have an interest in progressing far beyond this, but all will leave with basic abilities to query data sets and understand how to solve problems using data.

At the very heart, our mantra is to ensure that every one of our Masters School students learns how to learn. Data morphs the world every millisecond, we have no time to stand still. Our approach to teaching, learning and understanding must adapt to suit.

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