Artificial Intelligence for addressing the energy industry’s biggest challenges

Artificial Intelligence (AI) is the greatest catalyst for technological innovation in history, with countless applications in the energy sector. Key examples are the optimisation of production and storage, smart market trading, consumer savings and efficient consumption models for reducing carbon footprints, among others.

Smart Grid
Written by:
David Purón

The energy market is currently going through a particularly tumultuous time. The need for decarbonisation to curb climate change and the complex geopolitical situation have come together to create a perfect storm that points to a historic transformation in the industry. All the challenges and problems facing the energy sector can be traced back to a single issue: the imbalance between supply and demand.

The energy industry is moving toward an increasingly decentralised network that is ever more difficult to manage. With the advent of new technologies for autogeneration and energy storage by the end users themselves, new concerns are arising: when to store energy in batteries, when to use it, and how much to feed in from self-consumption?

These are matters dealt with using extremely complex decision-making trees involving hundreds of variables relating to price, weather, consumption patterns and network data obtained from hundreds of thousands of collection points spread out over thousands of miles. This is where Artificial Intelligence (AI) comes into play.

The democaratisation of AI

The good news is that today the evolution and democratisation of artificial intelligence or computer vision allows all this information to be analysed faster and on a larger scale, including historic data. This means more accurate results - and forecasts - can be achieved, without the risk of human error or imprecision.

Given the criticality and volumes of data handled, energy companies are opting to run these AI algorithms on Edge computing infrastructures. This means that huge amounts of data from sensors and plants can be processed with real-time results and without compromising the OT network.

However, applying AI to a sector such as energy is not without its challenges.

Companies need to bring in data scientists under the command of a Chief Data Officer (CDO) and provide the necessary budget and executive powers within their organisation.

We are talking about a 180º cultural shift to integrate computing with industry, using enabling technologies such as IoT, cybersecurity and edge computing. All this is accompanied by regulatory changes that allow these energy companies to innovate with new business models without the risk of penalisation.