By Jens Albom, Nordic Industry Lead, Utility



The technology train in the energy industry is picking up speed. Although we do not yet know exactly where the train is going, there is no doubt that we will stop at the station called "artificial intelligence". Many people have already started to explore the technology on a larger scale, but we are still at a very early stage.


In this article, which is the second in our series on the energy technology of the future, we look at the prerequisites, success criteria and opportunities for using artificial intelligence in the energy sector.


Artificial intelligence, real data


"Anyone" can use AI. But very few, if any, have the prerequisites to exploit the full potential. You will never get better forecasts, suggestions or answers from AI than the quality of the data you enter yourself. And there is a lot to gain from data processing.


The energy sector is an industry with low margins. If you want to be attractive to your customers, your pricing models also need to be attractive, and it's very much about quantity. Large amounts of data and small margins mean that even the smallest forecasting error can have major consequences. We believe that the greatest potential of AI in the energy industry is around predictability. This is where data management, data discipline and data structure are paramount.


The prerequisites for good and correct use of artificial intelligence are, therefore, very much focused on your data foundation. "Rubbish in, rubbish out", as they say. Jumping straight on the AI bandwagon now without having done the thorough groundwork of classifying and controlling your data is unlikely to be very profitable in the long term.


Artificial intelligence, real risk


Not only can incorrect use of AI lead to unnecessarily slow and imprecise processes, it also involves a significant risk. Because who really owns your data? And can you live with others using your data for their own calculations and forecasts?


It's about taking control of your own data and classifying it correctly. The vast majority of AI models use the data you feed them to learn, and you should be very aware of what you want them to learn from, and what you don't. Business-critical data must, of course, be kept secret.


If you've calculated the optimal production mix to meet a particular market's energy needs, it would be unfortunate if the formula were magically presented in your competitor's AI-generated forecasts. Here, Microsoft's own AI model, Copilot, can be a useful tool. It doesn't use the data to improve its own model, but only to give you forecasts.


Artificial intelligence, real people


"AI won't outperform you, but someone who uses AI will," has become a pretty well-known phrase. We definitely think there is something to it. As with all business development, artificial intelligence requires your organization to be ready for change. We usually call it being "AI ready".


Therefore, the use of AI is not just about making good data available and systematizing and classifying it. It is equally important to build an environment where the use of AI can flourish, and many are already well on their way. The first users in other areas of technology are probably already well equipped to implement AI as part of their everyday lives.


How well equipped is your organization? Remember that AI is an advanced technology. If you want to have any hope of becoming an AI-driven organization, you must first and foremost make sure that you are data-driven.


Artificial intelligence, real energy


For those who have built a technological and cultural foundation that is ready to apply AI on a large scale, the opportunities are enormous. Just think of how many wind turbines are constantly standing still because their owners are unsure whether it is worthwhile to build a battery park to store the energy. With good data and good people, artificial intelligence can give you forecasts at a glance.


What about consumption? Today, we measure every 15 minutes. Why not every minute? Or in real time? With AI on the team, you're in a much better position to balance the energy supply and distribute exactly the energy needed for optimal performance. This contributes to efficiency and profitability and, not least, a faster transition to a fossil-free future, as energy can be balanced much more effectively.


When is electricity cheapest? Are you going to produce, store or use? The forecasts we have today are good in every way, but they only leverage a fraction of the data you actually have. Producers, distributors and consumers all have a lot to gain from using the right energy at the right price and at the right time.


For the energy industry, AI is primarily about where the profitable investments are likely to be. Our challenge is quite simple: Don't stand by and wait while your competitors AI-calculate you out of the market!


This article is the second in a series on the energy technology of the future. In the next article, we look at the security aspect of the sector, both digital and physical.


Article 1: The energy technology of the future: The train rumbles along


Article 3: The Energy Technology of the Future: Are You Sure?

Want to learn more?

Contact our Sales Director, John T. Hummelgaard, for a discussion about your company's digitization.

John T. Hummelgaard