In the energy sector, AI (Artificial Intelligence) is being used to optimize energy production, distribution and consumption. By analyzing large volumes of data, such as consumption history and weather conditions, companies can predict electricity demand in advance and distribute energy more efficiently.
AI can also monitor and control power grids in real time, ensuring more reliable power distribution. In the oil and gas industry, this technology is being applied to optimize oil extraction, identify new drilling sites and predict fuel demand.
In renewable energy segments, such as solar and wind, intelligent algorithms help maximize the efficiency of plants and predict intermittent generation from these sources. In short, from generation to final consumption, AI has been transforming several processes in the Brazilian energy sector.
Despite advances, AI adoption is still in its early stages in many energy organizations. Minsait’s recent Ascendant report showed that while AI is use of AI is still low, there is consensus on the need to promote and harness the value of this technology. In other words, companies recognize the potential of AI and seek ways to integrate this technology into their operations to gain efficiency and reliability.
Real-time demand forecasting and operations
One of the most established applications of AI in the electricity sector is demand forecasting and optimizing system operations in real time. Machine learning models analyze historical consumption data, usage trends, and weather information to anticipate peaks and valleys in electricity use with high accuracy.
By predicting fluctuations in demand, AI makes it possible to dynamically adjust energy generation and supply, avoiding grid overloads and blackouts, as well as reducing energy waste during times of low demand.
This predictive capability is especially important in complex and increasingly decentralized power systems. AI tools can handle this complexity by analyzing massive amounts of data in real time. With AI, utilities can combine up-to-the-minute consumption data with weather forecasts to optimally dispatch power plants or redirect power.
Integration of renewable sources and energy storage
The energy transition brings a rapid increase in the share of renewable sources in the electricity matrix, and AI plays a crucial role in enabling this change. AI systems are able to correlate meteorological data with historical generation from wind and solar farms, increasing the accuracy of forecasts and allowing better programming of backup plants.
In Brazil, this challenge is becoming more important as the energy matrix becomes cleaner. It is estimated that Brazilian electricity demand will grow by around 3,7% per year in the coming years, driven by economic growth and climate change.
Given this scenario, AI will be essential to integrate sustainable sources into the system. Another technology allied to renewable integration is energy storage. AI comes into play by optimizing battery charging and discharging cycles, learning from price and demand patterns the best time to inject energy into the grid or store it.
In smart grids, algorithms can coordinate hundreds of distributed batteries, making them act in an integrated manner as a “virtual power plant” of extra energy for the electrical system.
Smart grids, IoT and predictive maintenance
Smart grids represent the modernization of networks with automation, sensors and advanced control systems. AI acts as the “brain” of these networks, interpreting the data captured by millions of devices and making decisions to optimize supply. IoT sensors measure variables such as voltage, current, temperature and vibration in real time. This data is sent to the cloud, where AI analyzes and detects anomalies or imminent failures.
AI also helps combat commercial losses, such as energy theft, which s for around 20% of the electricity generated in the country. With AI, distributors can identify irregular consumption patterns and optimize monitoring. Another important aspect is automation in operations. Instead of large areas being left without power after a failure, smart grids can automatically isolate the damaged section and redirect the energy flow in seconds, increasing the reliability of the supply.
Consumer benefits and sustainability
For the public, the positive impacts come in the form of a more reliable, affordable, and sustainable service. An AI-powered system tends to have fewer power outages, faster responses, and more stable rates. There are also AI applications that are directly accessible to the consumer, such as smart thermostats and devices that optimize air conditioning use. AI-powered apps already allow you to monitor the energy consumption of each appliance in your home and indicate where to save.
The environmental impact is also significant. By enabling greater renewable energy use, reducing losses, and optimizing the use of equipment, AI contributes to sustainability. Although AI requires energy for its own computing systems, there is a growing movement to make the technology more efficient and powered by renewable sources.
Future challenges and opportunities
There are still challenges to overcome: data quality, integration of legacy systems, training of professionals and cybersecurity. But the opportunities are enormous. With cheaper sensors, cloud computing and generative AI models, it will be possible to democratize the benefits of AI in the sector.
In conclusion, artificial intelligence has already established itself as a strategic tool in the transformation of the Brazilian energy sector. Its impact ranges from operational gains to structural changes, enabling a cleaner energy matrix and a more empowered consumer. Maintaining this pace of innovation will be essential to fully reap the benefits of this technological revolution, ultimately ensuring safe, clean and affordable energy for all.
The opinions and information expressed are the sole responsibility of the author and do not necessarily represent the official position of the author. Canal Solar.
