What impact does artificial intelligence have on energy demand?
When discussing business energy management, AI emerges as one of the most important resources, since, through its multiple applications, it allows the extraction and analysis of a large amount of data and information that can help with energy efficiency , the energy transition, and the sustainability of power plants.
What is AI in the business environment?
There are many definitions of artificial intelligence, each suited to its intended use and the environment in which it is applied. In the business environment, AI can be defined as a set of algorithms and technologies that allow industrial equipment and systems to learn and make decisions based on large volumes of data .
Among the “capacities” of AI, one could list: identifying patterns, making predictions, problem-solving, speech recognition, and the ability to use past experiences as a means of learning to correct mistakes and adapt them to new projects.
AI has evolved rapidly, becoming a fundamental tool for process automation across multiple sectors. In the energy sector, its implementation is changing how companies manage their resources, enabling more accurate and efficient decision-making that will allow them to anticipate future energy needs with increasing precision.
AI for businesses: analyzing data on energy consumption
AI's ability to collect and analyze large volumes of energy consumption data in real time, thanks to intelligent systems, allows companies to identify patterns and trends that help them understand more accurately when and where resources are being used most . This constant monitoring will help detect opportunities for savings and efficiency.
The responsible use of energy and natural resources has become a key objective, as well as a requirement, for any organization seeking to reduce costs and improve its reputation with regulatory bodies, consumers, and business partners. Furthermore, effective energy management helps to reduce the carbon footprint and optimize resource consumption, resulting in a more profitable and environmentally responsible production system.
AI algorithms can predict future energy consumption with high accuracy , facilitating better planning. These predictions allow companies to anticipate their needs, adjusting energy use to minimize waste and maximize available resources. This predictive capability is essential for maintaining consistent and sustainable operations.
Energy management: AI to manage energy demand
Artificial intelligence allows for real-time monitoring of energy demand, optimizing resource utilization based on current needs . This is achieved through integrated AI systems that track and adjust consumption according to the flow or movement of business activity, ensuring optimal energy use and cost reduction in operational processes.
Another key contribution of AI is its ability to anticipate equipment failures, known as "predictive maintenance," through daily or periodic monitoring of machinery, capturing patterns that could lead to potential failures. This will help plan preventive system maintenance, extend machine lifespan, and avoid disruptions to company productivity, resulting in long-term cost reductions in both energy consumption and equipment replacement.
The integration of renewable energies with AI
Companies, through the analysis and efficient management of the energy they generate, can optimize storage and distribution, allowing them to make the most of sources like solar and wind power, whose availability can be variable. This smart management facilitates the transition to a more sustainable energy model .
In addition to analysis and efficient management, automation is another key contribution of AI . Actions such as turning systems on and off based on usage optimize energy consumption without interrupting operations, resulting in reduced operating costs and greater energy efficiency.
This impacts the entire energy supply chain. From production to distribution, artificial intelligence helps optimize every stage of this process. Again, thanks to predictive analytics, companies can anticipate demand and adjust their processes to ensure efficient distribution and reduce energy waste . It also allows for the accurate calculation of return on investment (ROI) in energy efficiency projects. Thanks to the data that AI can precisely obtain on consumption and savings, companies can evaluate the profitability of their investments and, based on that evaluation, make informed decisions and plan initiatives.
Implications of AI in companies: resistance to change and lack of knowledge
Implementing artificial intelligence within a company's energy strategies could also encounter some obstacles. Resistance to change from the team, from management to the front lines, as well as a lack of knowledge in the use of these technologies, are some of the main hurdles that the application of AI in industrial processes can face.
Investing in training and adequate infrastructure is essential to successfully implement an intelligent system. Furthermore, leveraging the expertise and experience of specialized technology partners will further expedite the process of adapting and applying AI within the organization.
Training must be ongoing, as AI is constantly evolving and changing . Today, some of the emerging trends in its use include the development of smart grids that optimize energy flow in real time, the application of blockchain to improve transparency in the energy supply chain, and other advancements.
Data centers, including those that power generative artificial intelligence, are increasingly using electricity. Yet they are expected to account for only a small share of overall electricity demand growth through 2030.
The Price of Magic
Using ChatGPT, Perplexity or Claude, one can only be amazed at the speed of calculation of generative artificial intelligence (AI). This "magic" that seems to reason, search the internet and create content from scratch requires computer data centers to function. And who says computer centers says significant electricity consumption.
Business Logic
Martin Deron, project manager for the Chemins de transition digital challenge, a research project affiliated with the Université de Montréal, notes that a few years ago, the carbon footprint of digital came mainly from the manufacturing of devices such as phones, tablets and computers. “The impact of the data centres where we store our data was less significant in our total digital footprint,” he says. “Also, the companies that own these centres have a business logic. They try to minimize costs, particularly energy costs.”
6%
This dynamic has led to data centers becoming much more efficient. From 2010 to 2018, they increased their capacity by more than 550% worldwide. However, the total energy they consume has only increased by 6%, according to a study published in 2020 in the journal Science . “So even if our digital uses have increased, the carbon footprint of data centers has not increased that much because of innovation and technical improvements,” says Martin Deron. “However, generative AI is challenging this.”
Demand on the rise
The demands for training models, as well as generating new data, require the establishment of more data centers. "And the centers are reaching the limit of available energy. We hear that companies like Microsoft, Google or Amazon are going to launch or restart power plants to produce the electricity they need. Everything suggests that the demand for energy in this sector will increase in the coming years."
By 2030
The world’s data centers account for about 2% of electricity demand today. The International Energy Agency (IEA) projects that data center electricity demand will account for about 3% of the increase in global electricity demand by 2030, partly due to AI. Other uses, such as industrial needs, buildings, electric vehicles, and air conditioning and heating, are expected to account for a much larger share of electricity demand growth.
Local demand
In a recent analysis , 1 Oxford University data scientist Hannah Ritchie noted that data center demand for electricity is highly localized and is likely to affect certain locations more than overall electricity consumption. “For example, Microsoft has made a deal to reopen the Three Mile Island nuclear power plant. But Three Mile Island can only produce 0.2% of the electricity produced in the United States each year, or 0.02% of the electricity produced globally each year,” Ritchie wrote . “There is still a lot of uncertainty. The demand for energy from AI will increase, but perhaps less than we think.”
