In recent years, I have observed the increasing volatility in the energy market and how this impacts the entire industry chain, from consumers to large investors. I recently read the article “Seven areas where AI can increase efficiency in the energy market”, by Raphael Saueia Bueno, and some points caught my attention.
The current dynamics, combined with the greater complexity of the energy mix and the increasingly urgent demands for sustainability, make artificial intelligence a strategic ally for dealing with uncertainties and accelerating decision-making processes.
It is in this context that AI emerges as a powerful solution to face challenges of volatility, price predictability and the search for renewable sources. After all, we are talking about a market in which every decision needs to consider climate, regulation, geopolitics, consumption patterns and a series of other variables.
More than replacing human labor, it works as an analytical extension that improves processes, making them more agile and accurate. In my experience leading Ecom, I see more and more opportunities to use predictive algorithms, natural language models and Big Data tools to optimize not only financial results, but also long-term sustainability.
Below, I highlight three points that AI is transforming the sector.
1) Price forecast and volatility analysis
Energy is a unique product: its prices vary depending on factors such as weather, availability of sources, government policies and even geopolitical events.
AI, through machine learning algorithms and data analysis, Big Data, is capable of processing large amounts of information about price history, weather events and macroeconomic variables, identifying patterns and correlations that would often go unnoticed.
Automated data analysis increases the ability to predict short- and medium-term fluctuations, allowing trading desks to adapt to sudden changes. I have seen cases where this type of forecasting has offered competitive advantages, ensuring better margins in negotiations and contributing to supply security.
It is important to note that AI does not make these decisions entirely independently. It provides valuable insights, but it is up to specialized professionals — such as traders, risk analysts, and portfolio managers — to interpret them and define action strategies. It is the balance between technology and human insight that results in robust decisions.
2) Forecasting demand and consumption patterns
Another major challenge for the energy sector is predicting demand, which varies according to weather conditions, industrial activities, social trends and even long weekends. Getting this estimate wrong can mean significant losses, either due to a lack of available energy or surpluses that cannot be bought.
With artificial intelligence, we can map these factors and update projections continuously. AI systems can correlate historical consumption data with weather forecasts and consumption habits, identifying very specific usage patterns.
In practice, this can translate into more efficient resource allocation strategies, avoiding waste and ensuring energy availability during peak times.
From a consumer perspective, this technology also brings benefits in of energy efficiency. There are already initiatives in which utilities offer discounts to those who consume energy outside of peak hours, encouraging changes in behavior that reduce overall costs and pressure on the grid.
In this sense, AI allows not only predicting, but also influencing demand, pointing out paths towards smarter and more sustainable consumption.
3) Risk management and optimization of investments in renewables
The third point that I consider essential is the ability of AI to deal with risks and assist in optimizing investments, especially in renewable sources. The transition to clean energy is an irreversible reality, but it still carries uncertainties regarding regulation, availability of incentives and economic viability in the medium and long term.
AI tools can help build scenarios that take into variables such as environmental policies, carbon emission forecasts, costs of implementing new technologies, and even social acceptance. When we think about large-scale projects, such as solar or wind farms, having detailed production simulations that take into climate and logistics issues is essential to reduce investment risks.
AI also plays a relevant role in analyzing risk scenarios in energy trading processes, as it simulates factors such as regulatory changes and geopolitical events, which can directly impact the value of each contract.
This way, companies are better prepared for unexpected events, adopting strategies hedge and portfolio diversification that soften the effects of specific crises.
Therefore, my vision is clear: Artificial Intelligence is not a replacement for human work, but rather a strategic tool. It allows professionals to focus on higher value-added activities, while algorithms take care of more repetitive tasks and large-scale data analysis.
By integrating AI with well-structured, sustainability-oriented management processes, we can not only improve financial results, but also strengthen the sector's commitment to the environment and social demands.
I believe we are just at the beginning of a true revolution in the energy sector, in which AI is proving to be a fundamental pillar for price forecasting, demand optimization and risk management in renewable investments.
In such a dynamic scenario, those who know how to take advantage of the opportunities brought by artificial intelligence will not only have immediate competitive advantages, but also the chance to lead the way towards a more efficient, resilient and sustainable energy market.
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.
