As the world is undergoing a profound transformation driven by the twin forces of artificial intelligence and digitization, every aspect of energy and energy-related activities is virtually affected on much shorter intervals than before.

Artificial intelligence and digital technology offer innovative solutions to optimize and improve energy consumption across various levels of the economy. The present article aims to explore and address the way these technologies are reshaping the landscape of energy efficiency and examining some of their applications as well as challenges, and the future path towards the best and optimum applications of AI.

Energy Efficiency 

Energy Efficiency refers to using less energy to provide the same level of output during a certain stage of operation and chain of operations. It plays a crucial role in lowering energy costs, reducing pollution and GHG, and leading to higher energy savings and vulnerability to energy shortages and security. According to the OPEC Secretariat, improving energy efficiency could account for around 40 percent of global CO2 emissions by the year 2024. This view was expressed during a joint climate action brainstorming session in November 2024. However, the view is shared by the IEA in its quarterly assessments. 

One of the most important applications of AI in energy efficiency is the development of smart grids. Smart grids utilize digital communications technologies to monitor and manage electricity flows from all generation sources to meet the varying electricity demands of end users. Artificial intelligence algorithms can analyze vast amounts of data from smart meters, optimize sensors and other devices to forecast energy distribution, and surely reduce waste in the system.

Artificial intelligence can therefore forecast peak demand times and adjust the energy supply accordingly. This technology thus not only ensures that energy is used or stored efficiently, but also helps prevent blackouts and reduce reliance on power plants, changing to other sources of energy such as gas, coal, or diesel.

Artificial intelligence can be supportive of demand response programs and enables consumers to reduce or shift their power usage during peak periods in response to time-based rates. By analyzing real-time data on electricity consumption and market conditions, artificial intelligence systems can identify opportunities for energy demand reduction. 

Having said that, for instance, during peak demand times, AI can be adjusted to automatically convert smart appliances in homes or businesses to reduce energy consumption without compromising comfort. This saves money for the consumers and helps stabilize the grid and reduce the need for additional power plants.

It is important to differentiate between traditional and non-AI-operated smart grids.

Smart Grids 

Among all other aspects of the way Artificial Intelligence helps the improvement of energy supply chains is the smart grid. Grids have been installed on all energy utilities for many years, and they have advanced over the years. A power plant, no matter how it generates electricity, from renewables or fossil fuels, is meant to transmit electricity over long distances and to a variety of energy users along the borders and under various circumstances. 

But smart grids have revolutionized the process in a way never imagined even a decade ago. Smart grids utilize digital communication technologies to monitor and manage electricity flows from generation sources to meet the varying electricity demands of end users. Artificial Intelligence algorithms can analyze vast amounts of data from smart meters, sensors, and other devices to predict energy consumption patterns, optimize energy distribution, and reduce waste. This is particularly important in countries where a huge amount of energy is wasted in transmission, due to older versions of cable networks.

Demand Forecast

Artificial Intelligence can forecast peak demand times and adjust the energy supply accordingly. This not only ensures that energy is used efficiently but also helps prevent blackouts based on schedule by the electricity providing authorities or due to breakdowns because of overcoming energy needs by certain households. This is all because of the smart grid application. However, as mentioned above, smart grids have many functions in the energy generation chain, including oil and gas.

Integration of diverse energy sources is an important feature of smart grids generated by AI. The system allows for a unified energy generation and storage system across the entire energy spectrum, regardless of the source of energy, such as wind, solar, gas, crude oil products, or nuclear. As such, it provides consumers with greater control over their energy use through real-time information and demand response. However, currently, the most widespread and developed smart grid applications are within the electricity sector. This is because electricity grids are complex and require sophisticated control systems to maintain stability. 

 Nevertheless, expansion to other energy sectors has begun in a big way. Below, I would like to name a few:

Smart gas grids can monitor and control gas pipelines, optimize distribution, and detect leaks. Smart grids can optimize the operation of district heating and cooling systems, improving energy efficiency and reducing costs. The future of energy may involve integrated energy systems that combine electricity, gas, and heat. 

Smart grids are also essential for the transportation sector, as electric vehicles increase in numbers and many governments provide incentives for the public to use electric vehicles. Smart grids will be used to control the charging of electric vehicles to the grid, when and where needed.

Grid Optimization 

As mentioned earlier, Artificial Intelligence is revolutionizing grid management by dynamically adjusting power distribution. This process helps to prevent the grid overload and ensures stable power supplies under increased system integration of decentralized renewable sources. The ability of Artificial Intelligence to process and analyze vast amounts of real-time data from various sources makes it a highly valuable tool for grid operators.

In the renewable energy sector, accurate forecasts of resources and timing for wind and solar are crucial. Weather forecasting isn’t always accurate. As such, solar and wind turbines may face difficulty in running for adequate power generation. Although there can always be a breakdown and failure of grid systems in any country.

As such, smart grids are programmed to supervise the timings and duration for a certain volume of inward and outward energy generation systems. This system is particularly important when a country or region has surplus power or shortages on a seasonal basis. To be more precise, in certain regions of Iran, energy consumption is lower during winter, whereas a neighboring country has a higher energy demand for the same season of the year. Grid optimization supports and regulates the schedule so both the supplier, in this case Iran, and the receiving country over the border are at their full advantage without interruption.

Grid Integration 

Artificial Intelligence plays a pivotal role in integrating renewable energy sources into the smart grid. With the increasing deployment of solar panels and wind turbines, managing the variability of all resources becomes crucial. AI algorithms can forecast renewable energy production based on weather patterns and even include gas injections once necessary, and have access to a variety of sources of inputs to produce the required electricity based on the generation capacity of the given power station.

In the meantime, AI can be modeled to optimize the operation of battery storage that stores excess energy for later use. By determining the optimal times to charge and discharge, batteries based on the AI-aided grid model maximize the utilization of renewable resources while minimizing the reliance on a single resource of energy source.

It is noteworthy that digitization in energy efficiency is the key to achieving a sustainable AI-aided grid design. This has been referred to as the Internet of Things (IoT) in recent years. This refers to the network of interconnected devices that communicate and exchange data over the internet. 

In the context of energy efficiency, IoT devices such as smart meters, sensors, and connected appliances play a crucial role in monitoring and managing energy consumption. Smart meters provide real-time data on electricity usage, allowing consumers in households or businesses, and industries, to track their consumption patterns and make informed decisions about their energy use and the price that they pay.

Way Forward

As the demand for electricity keeps growing, direct usage of fossil fuels is marginalized. The share of petrochemical industries in oil and gas consumption has increased dramatically, and gas-powered power plants are becoming a thing of the past. Power-generating systems increasingly need to support multi-directional flows of electricity between the grid users. Meanwhile, links between power stations and systems and transportation are rapidly increasing. As such, it is becoming inevitable to distance itself from smart grids and devices in any country that aspires to advance.

In the meantime, the capabilities of Artificial Intelligence are rapidly progressing. 

According to a 2024 International Energy Agency (IEA) report, AI already serves more than 50 different uses in the energy systems. IEA estimated that the size of the market share for Artificial Intelligence in the energy sector could easily surpass $ 23 billion in 2025. 

Challenges Ahead

However, it has to be noted that training and installing large models can consume significant amounts of energy. Some models are estimated to consume around 1287 kWh, which is equivalent to the energy used by an average American household for about 44 years. Running inferences on AI models requires less energy than training, but can still consume substantial amounts of energy, especially for applications requiring real-time processing or large volumes of data. Estimates suggest that inference can consume 0.1 to 1 kWh per prediction, depending on the model’s complexity. 

Overall, data centers around the world are estimated to consume around 200 terawatt-hours (TWh) annually, which is roughly 1 percent of global electricity consumption. As for the oil sector, approximately 10 percent of a barrel of oil is consumed in the production of the oil, although it varies depending on the field, crude type, and technology. However, Artificial Intelligence and energy are going to be a new power couple. They will not be able to survive without one another. There’s no decoupling between energy and AI.

The spread of AI operating systems and the ability to run billions of data, requires heavy cooling stations that rely on a variety of energy sources, including oil and gas. This is ironic, but a fact that needs to be addressed. It is needless to say that technology keeps advancing and will find remedies for the limitations. However, for now, the countries that are rich in energy resources are better positioned to advance in AI technology.

Conclusion

In conclusion, while I fully subscribe to the development and globalization of AI training, it is the right time for the OPEC member countries (MCs) to discuss the idea of networking and formulating a common AI algorithm. The idea of a cross-border network has potential advantages.

A network could facilitate knowledge sharing and innovation practices among member countries. As such, it allows for faster energy integration and tackles complex challenges. Such collaboration could help promote standardization on several fronts, including climate change, zero carbon emissions, managing market volatility, and capacity-building policies.

Perhaps, the OPEC MCs can begin with an expert meeting to examine different approaches and discuss options. An expert meeting can examine diverse perspectives and viewpoints. OPEC has had experience in meeting and working together on a variety of issues of common interest and concerns, such as exchanging data, examining long-term strategies, common climate policies, and unifying stances toward issues of common and mutual interests. It is obvious that a common AI and energy model for an organization that is created for a different purpose sound different to absorb, but it can be tested. An exchange of views on where each MC stands is quite an achievement.

By Fereydoun Barkeshli 

Senior Energy Expert 

Iran Petroleum