How energy and utilities companies can greatly benefit from AI-powered predictive maintenance and computer vision?

Traditionally, energy and utilities sectors have relied mostly on personal know-how, intuition and experience for process optimization, cost management, maintenance, and any other area of decision-making. Now, people and companies are struggling because their systems have become too difficult for any human to grasp anymore. For artificial intelligence technologies, the growing level of complexity is no problem. 

“Artificial intelligence can significantly reduce production costs, speed up processes, increase efficiency, reduce labor costs, and support decision-making. However, both sectors are perceived as conservative, and their ability to transform has been relatively low. There is also some resistance on the shop floor to adopting new digital technologies,” says Artur Mroczkowski, Director, CEE Software Services, Etteplan. 

He points out that in the past, energy production was very centralized. Today, this has changed as production assets are distributed. Regulations make it necessary to prioritize renewable energy sources. At the same time, companies must be able to forecast energy supply and demand with high accuracy. 

“Wind and solar farms are heavily dependent on environmental conditions. If there is suddenly no wind blowing or heavy clouds, there is a great risk of massive network outages without forecasting. Energy companies must also avoid overproduction and be aware of demand peaks. Therefore, they must be able to predict supply and demand and ready to kick-start other energy supplies in time when the need arises”, Mroczkowski explains. 

He explains that AI-powered predictive analytics can deliver deep insights into grid performance, load balancing, pricing trends, and weather patterns. These insights help optimize electricity trading strategies and support compliance with environmental regulations. 

Regarding regulations, AI systems can ensure that fossil-based production is minimized to avoid paying high penalty fees for carbon emissions. They can maximize the use of renewable energy assets that are spread out geographically. 

When AI predicts low energy prices, it can recommend storing energy produced by renewable sources on large battery banks. Selling energy during peak demand hours results in the best price. 

The possibilities of AI extend to the utilities sector. While municipal water systems already use smart meters for billing, AI can transform this data into operational insights. By analyzing meter data alongside inputs from other sensors, AI helps detect leaks, improve system efficiency, and enhance service reliability. 

“AI-driven monitoring can detect irregular usage patterns in buildings and industrial facilities and indicate the place in the piping system that needs to be repaired. The system can generate an alert for a maintenance team, preventing larger problems in the network and generating significant cost savings”, Mroczkowski says. 

Similar use cases apply to other industries. A large chemical industry plant may have issues with leaks in complex piping installations, making it valuable to spot the location with ease. 

Computer vision is one of the most promising adoptions of AI in the energy industry, offering significant improvements in efficiency, safety, and cost-effectiveness. One key area of impact is powerline inspection, which has traditionally been labor-intensive, time-consuming, and costly. 

“With approximately 80% of transmission lines located overhead, they are highly exposed to environmental threats such as falling trees and corrosion. Accessing and inspecting these vast infrastructures—often spanning millions of kilometers—can be extremely challenging.” 

Drones equipped with AI-powered computer vision can autonomously scan large sections of the electrical grid, significantly accelerating inspections and supporting field maintenance teams. These systems analyze surrounding vegetation and detect early signs of structural issues, enabling utility companies to prioritize maintenance activities and prevent potential power outages. Advanced AI algorithms can identify subtle indicators of corrosion, structural fatigue, and emerging weak points with high precision. 

The integration of computer vision with AI-driven software is transforming the monitoring and management of wind farms, particularly in remote offshore environments. 

“This advanced approach reshapes how the industry operates and maintains offshore assets—enabling not only continuous condition monitoring of turbines but also the identification of physical degradation, such as surface corrosion, structural wear, or blade damage, which may not be detectable through standard sensor-based systems”, says Artur Mroczkowski. 

These intelligent platforms optimize turbine performance in real time based on environmental data, anticipate component failures, reduce reliance on manual inspections, and ensure reliable energy output. 

Want to learn more about how AI can boost efficiency and reliability in energy and utilities? Download our free guidebook Create value with Industrial AI for practical use cases and insights.