Electricity generation

The use of data is not new in the electricity generation industry per se. Gas and wind turbines have been equipped with sensors for decades. Today, one of the most promising aspects of artificial intelligence lies in being able to look ahead of the curve.

Historically, this data has been utilized to check if assets were performing as planned and to get notifications when something went wrong. Now, thanks to AI and large amounts of data, it is becoming possible to go from preventive to predictive asset maintenance allowing the operator to detect anomalies at the generation asset in a timely manner before its failure, and thus avoid costly, and sometimes dangerous, unplanned maintenance.

Please refer to our case about SpaceTime Insight.

In transmission and distribution, SCADA systems and PMU's generate vast amounts of data. By augmenting these systems with AI capabilities, it becomes possible to predict failures in the grid and multiple other areas of the transmission and distribution of energy, similar to the abovementioned use-case.

AI can not only help predict failures, but the technology can also identify voltage instabilities and secure avoidance of blackouts on the grid just as AI can be applied in various energy trading scenarios.

Companies, such as Drift, which seeks to streamline electricity purchases, use AI to foresee energy consumption by factoring in things like the daily energy usage of individual customers over time and microclimate weather data estimates.

At the end of the supply chain, the fundamentals of current energy systems are changing; users are now able to store and produce energy with what is often noted as distributed energy resources (in short DERs) for which AI can be applied to predict new patterns linked to the overall energy systems.

Generally, AI can make the use of energy more efficient. An example of such is Bidgely, a company working to solve the challenges that utilities face in meeting their demand-side energy by engaging the end users directly. Based on live metering data, AI can engage the end consumers providing them with information about their energy consumption. Providing both the utility company and the end user with valuable consumption insights has the potential to both change the way electricity is sold as well as disrupt markets for the sale of home appliances.

Research projects are looking into various fields on how to double energy productivity. In the smart building space, the strategic Danish-American research project COORDICY that investigates energy efficiency of public and commercial buildings can be highlighted.