Artificial Intelligence in the Energy Industry

Artificial intelligence continues to surprise many, due to its enormous capacity to process data and deliver results astonishingly close to professional analyses performed by human specialists. We are in an era where a considerable amount of knowledge is generated simultaneously, an era where the speed of information processing must go hand in hand with data analysis tools and artificial intelligence.

Currently, universities, research centers and data analysis departments of major corporations are working on the development of these data models to take advantage of them commercially. According to IDC, the market around artificial intelligence totaled  US$64 billion and will grow to almost US$251 billion in 2027. The reason for this boom can be found in all sectors of industry and daily life. Those who use AI can save resources, avoid waste, and even forecast their operations with greater certainty. 

Since the massification of artificial intelligence tools, the amount of information that is generated with these tools, which is fed by millions of styles, forms, sets and diverse opinions on a daily basis, has facilitated the quantitative analysis of data. Undoubtedly, this information can significantly help to reach greater certainty in forecasts, projections and models based on consumption habits – as long as it is processed in the right way, through its review and implementation by specialists in the field. 

AI in the Energy Sector

Artificial intelligence in the energy sector is a big bet, where the most important stakeholders are seeking to obtain the innovative and transformative benefits AI can offer them, such as optimizing operational efficiency, generating demand predictions, creating data for intelligent infrastructure management and cost reduction, among many other applications. 

“A recent estimate suggests that AI already serves more than 50 different uses in the energy system, and that the market for the technology in the sector could be worth up to US$13 billion.“ (www.iea.org)

In the energy sector, the decisions that are made have a rigorous technical basis, where factors such as supply, demand, consumption, habits, climate, price, storage capacity, distribution and dozens of additional elements make the design of strategies to develop efficient energy projects very complex, mainly due to the fact that the enormous amount of information that is analyzed must be contextualized, analyzed and contrasted with other moments with similar characteristics, which is why it is important to keep in mind that the success in the use of artificial intelligence for quantitative data analysis depends on the effective collaboration between experts in the field and technological tools. 

One of the enormous advantages demonstrated by the use of artificial intelligence models in this sector is the capacity for real-time analysis of all the information and data that allows accurate forecasting of power generation needs, consumption peaks based on historical analysis and variable factors, such as weather and other conditions, which translates into maximizing efficiency levels, thereby helping the environment by reducing any energy losses. According to Jeremy Renshaw of the Electric Power Research Institute, this technology enables grid operators to make crucial decisions in real time. For example, it can collect hundreds of thousands of images of the transmission and distribution system, which can be used to develop algorithms that integrate data and provide management solutions. 

AI as a Tool for Sustainability

Intelligent energy management can become a vital tool for mitigating environmental impacts and moving toward long-term sustainability. Many companies are developing tools that allow them to be much more efficient in managing their energy consumption, which not only allows them to reduce operating costs, but also to minimize their environmental footprint.

In the field of sustainability, an outstanding example of its implementation in power generation is the predictive capacity that renders it possible to anticipate and efficiently manage variations in electric power production from renewable sources. By analyzing weather patterns, historical production data and environmental conditions in real time, it is possible to maximize efficiency and mitigate the challenges related to the intermittency of these sources.

Much remains to be explored regarding the possibilities of implementing this technology in the energy sector. The convergence of artificial intelligence and sustainability represents not only an innovative response to current challenges, but also a significant step toward a more promising future.

These technological advances must become tools that allow us to conceive and build a future where energy and innovation move hand in hand toward a brighter and more environmentally friendly era.