Artificial intelligence use cases in utilities and energy industry
While it’s necessary to recognize that for energy and utility companies, reliability and customer safety take precedence over being on the cutting edge of technology, the emergence of generative artificial intelligence (GenAI - so tools like ChatGPT, Claude, Gemini or Copilot) and machine learning technology advances present many opportunities for the entire sector.
Success stories from within the sector and other strategically important industries, including healthcare, finance, and logistics, demonstrate that AI-driven solutions are sufficiently mature to provide power and utility providers with tangible benefits while keeping the risk at bay.
In this article, I will discuss the potential benefits of leveraging artificial intelligence (AI) models in the sector, examine trends in AI implementation, and explore the opportunities and challenges that arise from AI technology.
Top 7 AI-driven solutions for energy and utility companies
Today, the top use cases of AI technology in the energy and utilities industry include power grid management, predictive maintenance and analytics tools, and energy storage optimization. However, AI/ML algorithms can also be used in other ways, such as enhancing the organization's defense against cyber threats.
Let's dive deep into some of the greatest AI opportunities for organizations in the energy and utilities sector.
1. Modernizing energy infrastructure
AI models can be used to improve the efficiency of existing infrastructure by optimizing many processes linked to the generation and distribution of electricity. It can also expedite the development of new technologies needed to modernize aging power grids and make them more resilient.
Moreover, AI solutions can be used to forecast the demand more accurately (even with limited or missing data), allocate resources more efficiently, and reliably respond to shifts in energy demand in real time.
Real-life examples
- The US Department of Energy has already implemented some AI-powered predictive tools to anticipate and mitigate grid disruptions and increase the cost-effectiveness of grid operations.
- Scientists from the University of Texas have developed an AI model capable of rerouting electricity in mere milliseconds to prevent power outages, contributing to the development of a self-healing grid that autonomously detects and repairs such issues.
- Scientists at Argonne National Laboratory leverage AI models to monitor health and enable predictive maintenance of strategic energy grid assets, including traditional energy and clean energy infrastructure components.
- Researchers from the Chinese Academy of Sciences use machine learning to study the performance limits of existing batteries and develop new materials for next-generation batteries.
- A UK-based company, Gorilla Technology, aims to leverage AI to drive Thailand’s nationwide energy infrastructure transformation initiative. The company plans to implement an AI-powered smart grid to ensure a resilient and optimized electricity network and use AI-based cybersecurity protocols to monitor the network for threats and proactively detect anomalies.
2. Facilitating green energy transition
Energy output from renewable sources, such as wind and sunlight, is more unpredictable than from traditional power plants, as it depends on weather factors that humans can't influence. By taking both weather forecasts and historical data into account, AI algorithms can provide more accurate estimations of renewable energy generation, therefore minimizing the impact of renewables' inherent variability.
AI solutions can also use this data to efficiently redirect the excess energy that wind farms and solar panels generate during peak periods to energy storage facilities and release it when these sources can't produce electricity. In this context, AI-powered solutions can be the missing link needed to make renewables more reliable and useful in our everyday power consumption.
Real-life examples
- According to BCG's report "Accelerating Climate Action with AI," AI can help reduce greenhouse gas emissions by 5% to 10% by 2030.
- Chinese and British scientists published a paper based on data from 2010 to 2022 that shows how the development of AI models stimulates renewable energy transition by addressing supply chain disruptions.
- An Indian renewable energy company, ReNew Energy, has leveraged AI to improve its solar and wind installations' energy output by 1.5%, prevent service interruptions, and streamline infrastructure maintenance.
- While the study commissioned by the EU's Artificial Intelligence in a Digital Age (AIDA) committee emphasizes the adverse consequences of the current state of AI development on the environment; it also confirms that AI solutions can be used to further the goals of the European Green Deal. According to the document, AI models can, among other things, contribute to energy saving by enabling energy profiling, demand estimation, and fault detection. AI tools can also facilitate increased energy efficiency and reduced waste in the construction sector.
3. Improving energy efficiency
In the enterprise sector, AI models are used to power dynamic pricing solutions, as algorithms can automatically adjust energy prices in real time, based on current demand and supply.
For household customers, AI tools can facilitate personalized recommendations for reducing energy consumption, leading to cost savings and enhanced customer satisfaction.
Furthermore, energy companies leverage AI solutions to facilitate process automation, which leads to significant improvements in efficiency. Consequently, more efficient operations utilize less energy and generate less waste.
Real-life examples
- DeepMind AI was used by Google to reduce energy usage for cooling their AI data centers by up to 40 percent.
- US electricity supplier NextEra Energy leverages AI solutions for real-time recommendations on weather-dependent issues and comprehensive land analysis to accelerate its transmission network development.
- Moreover, to address the AI energy consumption challenges, the Massachusetts Institute of Technology's Lincoln Laboratory Supercomputing Center (LLSC) developed a set of techniques aimed at helping data centers reduce AI-related energy use while having only a minimal impact on model performance.
4. AI-powered analytics and predictive maintenance
AI-driven predictive maintenance services allow utilities to develop more secure and stable systems and reduce the chances of power, water, or heating outages.
Energy and utility operators can enhance response times and improve the grid's resilience and overall efficiency by enabling a more proactive stance toward the upkeep processes with AI-powered real-time information about infrastructure assets' status.
AI-driven equipment monitoring and predictive analytics can be used to analyze complex data sets and forecast future maintenance needs, detect potential system failures before they happen, and recommend proactive maintenance action before issues escalate.
Real-life examples
- American utility company Duke Energy utilizes predictive asset analytics software solutions to monitor its power generation assets across its 60 plants, including renewables, coal, and other energy sources.
- In the 2024 sustainability report, NextEra Energy describes how the organization uses predictive analytics tools and algorithms to better understand data collected from about 1 billion endpoints. As a result, they are able to identify potential problems and fix them before interruptions happen.
- Thomas L. Wiese from SUNY Empire State University in New York describes some of the current applications of AI-driven predictive maintenance in an article published in the International Journal of Engineering, Business and Management.
- In water management systems, companies use AI models to help predict equipment failures in pumps, pipelines, and water treatment facilities by analyzing sensor data to detect anomalies. Therefore, the companies can preemptively perform necessary actions to prevent service interruptions and reduce downtime in the water supply.
- Within the energy sector, similar solutions are increasingly popular, with renewable energy production leading the way, as modern wind turbines and solar panels generate vast amounts of data that can be analyzed by AI models.
5. Optimizing energy storage capabilities
Energy storage solutions are simply crucial for every facility where an uninterrupted power supply is needed to guarantee people's safety and the ongoing operations of critical industries. Besides safeguarding our hospitals, waterworks, research facilities, and data centers, efficient ways of storing energy are needed to address the intermittent nature of renewable energy sources.
Integrating robust storage systems within existing infrastructure becomes key to building a more sustainable electricity sector, and AI solutions can help grid operators achieve that goal. Advanced AI algorithms can be used to analyze various factors that impact energy storage, identify patterns based on available data, and make recommendations on when to release stored energy and how to distribute it optimally.
Even with partial or unstructured data, AI is capable of taking into account historical demand patterns, pricing fluctuations, and the state of the surrounding grid. Such complex calculations can serve as invaluable support for decision-makers, resulting in more efficient management of resources and enhanced grid stability.
Real-life examples
- Several energy infrastructure investors and power producers utilize an AI-driven forecasting tool built by Capalo AI to optimize their power storage systems and facilitate swift responses to fluctuations in energy production and demand.
- Scientists from the University of Johannesburg in South Africa published a paper on AI-backed renewable energy systems optimization, where they describe how AI algorithms are used to optimize energy storage systems by analyzing and forecasting energy production and consumption patterns.
- Researchers at the University of Bergamo analyzed how AI is being used in energy storage systems for electric vehicles. They found that the application of AI in battery management systems is increasing, as it is used to quickly analyze data from vehicle sensors to measure battery state-of-charge, ensure safe operation, and communicate relevant information to drivers.
6. Enhancing protection against cyber threats
In cybersecurity, AI models are increasingly often integrated with surveillance and sensor systems to continuously analyze data and automatically detect unusual events within IT systems.
With AI-enabled security tools, utility providers can receive instantaneous alerts whenever their software systems behave strangely, which allows them to take preemptive actions to maintain system integrity and minimize disruptions.
Real-life examples
- According to recent research, the global market for AI in cybersecurity was valued at USD 24.82 billion in 2024, and it is forecasted to exceed USD 146.52 billion by 2034. In North America, this market is expected to grow even faster.
- Siemens, a German technology conglomerate, employs machine learning algorithms in its power generation services to passively monitor the entire network, identify anomalies without complex configuration, and detect any internal or external threats in real time.
- As malicious actors already leverage AI in cyberattacks, businesses across industries adopt AI-driven preemptive cybersecurity measures for more effective defense against rapidly evolving AI-backed attacks.
7. Unlocking the critical role of customer data
Artificial intelligence and machine learning (AI/ML) technologies empower consumers by analyzing data on energy consumption patterns. This data analysis is then used to improve customer experience, assist customer service representatives, power robust online chatbots, and provide consumers with personalized recommendations, giving them more control over their energy usage.
AI's ability to provide tailored energy consumption recommendations is not limited to industrial processes and large power consumers like manufacturing companies. It's a benefit that extends to everyone, including low-income families and entire communities in developing countries, making energy efficiency more accessible to all.
Real-life examples
- Technology unicorn Octopus Energy Group is a British energy supplier that utilizes a proprietary software platform, Kraken, to automate its energy supply chain and develop innovative tariffs. The platform, driven by data analytics and machine learning capabilities, allows the company to offer personalized tariffs that support renewable energy, help in smart grid development, and decrease energy bills for businesses and households.
- Leading Italian energy providers Enercom Group and Wekiwi have successfully implemented AI agents to automate customer service tasks, personalize interactions, and improve customer satisfaction. Enercom utilizes AI to streamline customer onboarding and quickly respond to routine inquiries, and Wekiwi employs its AI-based virtual assistant to offer personalized product recommendations.
- Engie is a French electric utility company that uses AI-based software to perform various data analysis tasks on vast amounts of data collected from the organization's facilities around the world, such as solar panels and wind farms. AI is leveraged to determine the efficiency of Engie's renewable energy generation assets at any given moment, schedule maintenance, and monitor the company's decarbonization process. However, their machine learning solution is also used to assess private homes' solar production potential and allow private customers to monitor and remotely control their power consumption.
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SubscribeAI-related challenges in the utilities and energy sector
Today, organizations in the energy and utilities field already face significant challenges with automating their operations, decarbonization, decentralization of power generation, and regulatory compliance. While the adoption of AI in the energy sector presents numerous opportunities listed above, it is also not without challenges.
Just like in any other field, the development of AI tools comes with a number of new concerns, including the issues of safety and security, dependability, and ethics. Consult the list below to learn about several challenges organizations should be aware of when implementing AI solutions.
AI data centers and their impact on climate change
Data centers’ energy demand in the US is projected to increase to 260 terawatt-hours by 2026, a 30% rise from 2022. But even today, AI models greatly contribute to growing energy use, which raises understandable concerns linked to climate change. As long as traditional power plants remain in use, energy generation is responsible for massive amounts of carbon dioxide emissions released into Earth's atmosphere.
This is why American tech giants like Amazon and Google express interest in investing in the nation’s nuclear energy stock and why major AI players experiment with new ways of limiting AI-related energy consumption.
Significant implementation cost
Implementing AI solutions in the energy sector involves considerable upfront costs, which can be a barrier, especially for smaller organizations. However, energy and utility providers can limit these expenses by choosing self-deployed open-source solutions. Choosing smaller, more specialized models can also result in lower costs.
See how our AI experts can help.
Data security concerns
The energy sector handles large quantities of sensitive data, including its customers' personal information. Protecting this data and ensuring its appropriate handling by AI is essential, especially in light of evolving compliance requirements.
Learn about proven information security solutions.
Talent shortage
The demand for AI specialists is rapidly increasing. The energy industry is forced to compete for data scientists and AI experts with organizations in other industries, as 76% of senior IT decision-makers in large companies across the US, UK, India, and Spain report insufficient AI experience among staff.
Fortunately, working with expert AI consultants and specialized IT recruiters can be a great alternative to costly hiring efforts.
Complex regulatory landscape
The ongoing evolution of AI technology-related regulations creates an increasingly complex network of regional, nationwide, and international laws that can significantly complicate AI implementation and put it at risk in the energy and utilities sector.
For instance, an AI Act in the EU outlines restrictions on certain AI practices that have to be universally followed in all member states. At the same time, the complexities of introducing comprehensive federal legislation in the US allow individual states to introduce their own AI-related bills.
AI's impact on the energy and utilities industry
AI models had already advanced significantly since the public release of ChatGPT in 2022, when they first came into the spotlight. Artificial intelligence solutions have become quicker and more efficient, allowing widespread adoption across many industries, including energy and utilities. Today, intelligent tools can be tailored to perform more specialized functions, from writing text and code to analyzing complex data to enabling automation.
We can expect this evolution to continue, allowing future AI models to be even more easily customized to meet the specific demands of various users. Analyzing recent trends in AI development confirms that assumption.
Read further to learn about key artificial intelligence and machine learning trends.
Trend 1: Smaller models
While large language models bring impressive capabilities, they require significant resources and energy, making them less accessible for smaller teams and organizations.
Meanwhile, research has shown that smaller models trained on extensive datasets can sometimes even outperform larger models while reducing costs and enhancing computing speed. Further development of smaller models has the potential to increase AI adoption rates throughout all market sectors.
Trend 2: Increased resource efficiency
AI is becoming more resource-efficient than it was. Challengers recognize that minimizing their models' energy consumption can become an important market differentiator for their products. Established tech giants like Google and Microsoft also experiment with solutions that optimize energy consumption despite growing workloads.
Developing new hardware, such as advanced cooling systems, investments in low-carbon materials, and increasing the role of nuclear power plants and renewable energy sources are also part of this effort.
Trend 3: Tailor-made solutions
Another trend seen as crucial by many business users is prioritizing data-based customization options. AI developers seek ways to give companies more control over their models by customizing content filters and introducing safeguards designed to minimize business risk, eliminate ungrounded responses (known as confabulations or hallucinations), and ultimately meet the demands of specific industry environments.
A tailored approach to implementing AI is further strengthened by the increasing availability of open-source solutions and community-sourced innovations.
Trend 4: More realistic expectations
As generative AI gains traction, the business community becomes more aware of its limitations. Organizations now have more realistic expectations compared to the initial hype fueled by marketing and media.
While many leaders anticipate the significant impact of AI on their businesses in the coming decades, they tend to exhibit a balanced view of this technology, recognizing that generative AI provides unique opportunities without being a one-size-fits-all solution.
Expedite AI and machine learning adoption with specialized consultants
After the initial hype, real-world implementations of AI technologies today focus on incorporating these solutions into existing systems rather than overhauling them. A recent IBM survey highlights that the main drivers for AI adoption are the accessibility of advanced solutions, the pursuit of operational cost savings, and the growing integration of AI tools within standard business applications.
While these general goals may be similar for a majority of businesses regardless of the industry, organizations in the energy and utilities sector should prioritize sustainable and responsible investments in AI adoption that address the specifics of the utilities and energy landscape.
Maxima Consulting provides tailored managed AI services to business partners around the world to support their digital transition and empower them to harness the power of AI solutions. Contact us today to gain instant access to the expertise you need and achieve your adoption goals in record time.
