TIM is expanding its energy management strategy by combining its own renewable energy generation with tools for... IA (Artificial Intelligence) aimed at optimizing consumption.
Currently, the operator produces approximately 70% of the electricity used in its operations through 136 solar, hydroelectric, and biogas power plants distributed across 23 states and the Federal District.
These projects supply more than 20 of the company's antennas and have an estimated annual generation of 474 GWh, a volume equivalent to the energy consumption of a city with approximately 770 inhabitants, such as Uberlândia (MG) or Ribeirão Preto (SP).
TIM's distributed generation strategy began in 2017, with five power plants located in Minas Gerais responsible for supplying approximately 1.200 antennas. Since then, the project has gained national scale and become one of the main pillars of the company's energy strategy.
Since 2021, the operator has exclusively used energy from renewable sources. In addition to its own generation, the company supplements its supply by purchasing energy on the free market and acquiring international renewable energy certificates (I-RECs).
In an interview with Canal SolarAlisson de Sousa, Executive Manager of Energy at TIM, detailed how the company is using artificial intelligence to improve the energy management of its operations and increase the efficiency of electricity consumption in its antennas.
Check out the main excerpts from the interview below:
When did the company start using artificial intelligence to enhance energy efficiency, and how does it work?
The use of AI to support energy management at TIM began between April and May 2025, with two main focuses: identifying outlier consumption patterns and mapping situations where consumption is significantly lower than expected.
In the first case, we use AI models to estimate what the "normal" consumption of each unit would be, based on characteristics such as equipment type and operating profile.
Units with similar profiles are grouped into sets, and a comparison parameter is defined based on this. When actual consumption deviates significantly from this pattern, the system flags a deviation, which may indicate, for example, an incorrect charge or some other anomaly.
In the minimum consumption project, we use the same analytical logic and the same artificial intelligence model, but the objective is to look at the reverse scenario: to identify units with consumption below expectations, which may indicate measurement failures, incorrect readings, or operational inconsistencies.
In these cases, preventative action allows the problem to be corrected before retroactive charges or penalties are imposed by the utility company. In practice, these two perspectives allow for more proactive energy management, based primarily on the analysis of bills.
It is important to emphasize that this is not real-time monitoring of the antennas, but a structured analysis of consumption and billing data.
How many consumer units are currently being monitored by TIM using AI?
Artificial intelligence is applied to all of the company's active consumer units, considering the analysis of energy bills. This includes monitoring consumption, charges, and other billing items, which allows for the identification of deviations, inconsistencies, and optimization opportunities.
In other words, from an energy management perspective, the coverage is broad. But, again, this is an analysis based on billing data, not real-time operational monitoring.
Has there been a measurable reduction in energy consumption since then? What energy savings and financial impact have been achieved so far?
Since implementation, concrete results have already been observed, both in reducing consumption and correcting billing. Identifying deviations – especially consumption outside the norm – allows for adjusting inconsistencies and preventing improper payments.
We estimate a fourfold increase in savings when comparing 2025 to 2026. These results reinforce the role of artificial intelligence as an important tool for improving energy management, bringing greater precision to the analysis and a greater capacity for preventive action.
Can the system identify losses, thefts, or operational failures? Does it act automatically or only suggest corrections?
The system can identify signs of inconsistencies, such as potential losses, incorrect charges, or even situations that may be associated with energy theft, by comparing expected consumption with billed consumption.
Since the analysis is based on billing data, the focus is primarily on deviations of an energy and financial nature. Failures directly related to the operation of the antennas are not part of this scope, as there is no real-time monitoring of the equipment.
Currently, the model does not make automatic corrections. It runs periodically, flags deviations, and directs the analyses to the responsible teams, who then take the necessary actions. However, an architecture is already evolving to make the process more automated, with faster alert generation and greater efficiency in response.
Does the project also generate a reduction in emissions?
Not directly. The main objective is to correct inconsistencies in consumption and billing, and not necessarily to reduce the physical energy consumption of the units.
However, there is an indirect effect. By correcting inaccurate measurements and adjusting billed consumption to actual levels, there is a reduction in the volume of energy accounted for. This can decrease, for example, the need to purchase energy on the market, including the purchase of renewable energy certificates such as I-RECs.
In this way, although it does not directly affect the efficiency of the equipment or the reduction of actual consumption, the project indirectly contributes to more efficient energy management, with repercussions also in environmental indicators, such as reported emissions.
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