When EIT InnoEnergy proposed me to join forces to bring more awareness on the opportunities of Artificial Intelligence in the energy market, I felt like I was going back home. I devoted my professional life to make people and businesses aware of the potential of AI, but my main academic background is in Energy Engineering: bringing the two areas together with an organisation such as InnoEnergy makes me incredibly excited.
We’re going to kickoff the initiative with a public webinar on the 19 Apr 2018 at 13:00 London time, you can register for the webinar at this link.
In the meantime, we’re going to publish one blogpost/week on a different topic related to AI and Energy, and this one will cover what are the opportunities that we foresee. Looking forward to hear your feedback and have you at the webinar!
“If I were starting out today and looking for an opportunity to make a big impact in the world, I would consider three fields. One is artificial intelligence (AI). We have only begun to tap into all the ways it will make people’s lives more productive and creative. The second is energy, because making it clean, affordable, and reliable will be essential for fighting poverty and climate change. The third is the biosciences, […]”.
This is how Bill Gates kicks off his essay “Dear Class of 2017…”, calling on the newest college graduates seeking for advice on which path to take to maximise their impact in the world.
It’s not a big surprise that AI and energy are both in this list. Many people are conscious that something big is happening in AI, and the awareness of the need for a radical change in how we produce and consume energy to fight climate change is more pronounced than ever. What is often found unexpected, is that AI and energy are going to be always more related in the future, and AI will play a key role in the revolution of the Energy sector.
Energy challenges & opportunities
To understand why this is the case, we need to understand what are the biggest challenges that the energy market is facing and why AI is the right tool to address them.
A substantial transformation is undergoing in energy, pushed by a convergence of technological, economical and environmental factors that are making sustainable energy also a sustainable business. In fact, savvy political incentives and decreasing costs are driving the boost in renewable energy production, EV vehicles are spreading out in many countries, and smart sensors are getting cheaper and more present in buildings and factories.
Like every major change, these shifts bring new challenges. A bigger part of the energy production is produced with renewables, which are blowing up the existing grid and energy markets paradigms. Also, the increasing electrification (EV vehicles penetration for instance) and decentralised energy production are contributing more and more to volatile loads,difficult to manage. Utilities are struggling to keep up with this scenario of increasing complexity, increasing risks and management costs. These challenges are often referred to as the “3D’s of the energy transition”: decarbonisation, decentralisation, digitalisation (necessary to face the decentralisation problem).
At the same time, growing competition caused by markets deregulation is making utilities’ bottom lines thinner. They now need to focus more on keeping costs down and pay more attention on retaining customers, who are demanding always more from their energy providers.
Utilities are not the only ones that need to keep their energy-related costs down. In a cash-strapped economy, businesses and individuals look more closely at new ways to cut their energy bills, that don’t involve heavy infrastructure investments.
Luckily, we don’t just have new challenges, we also have new tools to face them, and this is where AI kicks in. Investments in digital technologies by energy companies have risen sharply over the last few years, with global investment in digital electricity infrastructure and software grown by over 20% annually since 2014, reaching $47B in 2016. This digital investment in 2016 was almost 40% higher than investment in gas-fired power generation worldwide, amounting to $34B (source: International Energy Agency http://www.ia.org/digital/).
Notice how everything we talked about earlier can be seen as a prediction/optimization challenge rather than an engineering one: the instability of the grid can be fought with better forecasting, costs can be cut optimising maintenance, and energy can be saved with a better understanding of its usage. This means that we are looking at optimization and prediction challenges for which we own massive amounts of data.
This is the classic situation where Artificial Intelligence shines. Artificial Intelligence can leverage the huge datasets that the energy industry is producing to build models that can greatly impact how we consume and produce energy. It can model how energy is produced by renewable resources, improving forecasting, it can also understand how energy is consumed, optimizing resources and achieving a better demand-response management. In general, AI is the tool that can help us transforming the ocean of data the industry is collecting into new solutions to its growing pains.
There’s a ton more to say about this topic, and a single blogpost is definitely not enough. Subscribe to read the next ones, and register for the 19 Apr webinar at this link!