Executive Summary:
1. AI in the energy sector is transforming power generation, transmission, and storage by enhancing efficiency, reliability, and sustainability through predictive maintenance, smart grid management, and optimized renewable integration.
2. Growing investments highlight the pivotal role of AI in clean energy, with venture capital funding for AI-driven energy innovations rising significantly as investors anticipate new business models and revenue potential.
3. Despite promising gains, challenges such as data quality, regulatory issues, and cybersecurity risks must be managed to fully harness AI’s potential in the energy industry.
AI is having a large and transformative impact across almost every industry, including financial services, real estate, healthcare and more. The energy sector is no exception, and the effects of AI for power generation, electricity, and transmission will be extraordinary. With the increasing need for electricity created by growing urbanization, increased global population, shifts to renewables (EVs), and the needs of AI itself, demands on the energy sector are expected to increase substantially. This rising demand calls for not only technological innovation but also a fundamental reimagining of energy resource management and international collaboration to ensure cross-border energy security. Efficient power generation and AI-driven solutions are both critical to meet these needs.
AI and the energy sector have even been described as the new power couple by the International Energy Agency, due to this growing need for electricity generation, continual decarbonization, and increasingly complex power grids and systems. AI, with its ability to process massive amounts of data in real-time, will act as the brain behind the energy systems of the future—capable of monitoring, predicting, and optimizing performance across the entire value chain, from energy production to end-user consumption. The major impacts of AI in the energy sector will be seen in relation to improved efficiency and reliability, better maintenance and reliability, smart integration of renewables, and optimized energy storage. Moreover, AI’s ability to integrate various energy sources—including traditional fossil fuels and emerging renewable technologies—into a unified, smart grid will be crucial in transitioning to a low-carbon economy.
As an example of the momentum in AI, the Wall Street Journal has reported that for European VC investment so far in 2024, the energy sector came out on top nearing $5.7 billion in funding, as shown in Figure 1 below.
Figure 1. European VC investment in first half of 2024
Source: Wall Street Journal
This trend highlights the growing investor confidence in the long-term potential and profitability of AI-driven energy innovations. Venture capital, increasingly drawn to clean energy technologies, sees AI as a pivotal force that will unlock new business models and revenue streams within the energy industry.
Moreover, the investment focus is evolving beyond simply adopting AI, toward integrating it deeply into the core operations and design of future energy infrastructure. This includes a wide array of applications, from smart grids to autonomous energy trading platforms.
McKinsey reports that the power needs of AI itself are going to increase from 3-4% of total demand to 11-12% by 2030, noting that the “power sector is rapidly becoming a protagonist in the AI story.” NASDAQ has described this as the “AI energy arms race,” and Allianz calls this an “exciting development for investors.” However, this race is not just about energy consumption—it’s about the strategic advantage that AI offers in optimizing energy use, enhancing system resilience, and driving down operational costs. Again, as an example of the demand for power generation due to AI, XLU (Utilities Select Sector SPDR Fund) has risen 30%+ over the last year.
The interplay between energy and AI are driving significant growth in this sector, particularly about renewables and demand forecasting. A market research report from MarketResearchBiz found that the market for generative AI in the energy sector is likely to grow from $527.4 million in 2022, to $4,261.4 million in 2032, shown in Figure 2 below:
Figure 2: Generative AI in the Energy Market
Source: MarketResearchBiz
In this regard, the International Energy Agency says there has been a “nearly 60% surge in clean energy investment” in the last 4 years. This investment surge has been driven by a confluence of factors, including stricter environmental regulations, the falling cost of renewable technologies, and a growing awareness of the risks posed by climate change. Reforms in the sector are also likely to lead to the share of renewable energy increasing from 22% to 34% by 2028.
Let’s look at the role of AI technologies in different aspects of the energy sector, and how the industry will be transformed over the coming years.
The Role of AI in Power Generation
AI has a particular role to play in numerous aspects of power generation. This includes equipment and plant maintenance, better performance and output, more accurate forecasting, and increased economic gains from power generation.
First, a number of factors particular to the energy sector lend themselves to significant improvement with AI. For example, the reliability and predictability of power generation equipment is critical. Predictive maintenance is one way in which analytics and data-driven approaches are used to ensure that equipment is performing well, and to determine in advance when components will need to be replaced.
Predictive maintenance with AI is particularly transformative in environments like wind farms and solar fields, where failures or downtime can result in dramatic drops in energy output. By analyzing real-time environmental factors, AI can predict the impact of natural events like storms or droughts on renewable energy production, helping grid operators prepare and allocate resources accordingly.
Research from the Utility Analytics Institute shows that the predictive maintenance market already increased for energy and utilities from $80.94 million in 2017, to $251.3 million in 2022, as shown in Figure 3 below:
Figure 3: North America Predictive Maintenance Market by Vertical 2017 -2022
Source: Utility Analytics Institute
Using AI to supplement this process leads to even better data analysis, predictions, and therefore predictability. This can significantly improve the reliability of power generation assets and extend the life of power plants, and is likely to lead to more growth in the market.
With regard to the generation of renewables, predictive maintenance using AI can do things such as analyze vibration data from wind turbines, look for hotspots on solar panels (which can signal failures), check water flow rates for hydropower, and improve grid integration (discussed more below).
In addition, power plants can be operationally improved through the use of AI, particularly with regard to efficiency. A case study carried out by McKinsey found that the heat rate (the thermal efficiency) of the power plant could be significantly improved using AI. The application of AI tools created recommendations for optimizing heat rate technology in the plant, allowing it to function “more than two percent more efficiently after just three months in operation, resulting in $4.5 million per year in savings and 340,000 tons of carbon abated.” This was rolled out on a much larger scale, with additional savings and abatement measured across the board, shown in Figure 4 below.
Figure 4: Annual value and carbon abatement from AI-enabled heat rate efficiency improvement
Source: McKinsey
These types of optimizations can be carried out also for nuclear power plants, as well as for renewables. The International Atomic Energy Agency notes that “there is tremendous interest in AI solutions in the industry”. However, the technology is so “fundamentally novel”, that it still needs to be determined whether existing or additional regulatory requirements will need to apply.
Finally, AI can also be used to improve forecasting accuracy for energy production and consumption, balancing supply and demand. This particularly applies to the integration of renewables, with AI able to use weather patterns to predict how much wind there will be, for example. Google applied its DeepMind technology for this exact purpose, predicting wind power output 36 hours in advance. They note: “machine learning has boosted the value of our wind energy by roughly 20 percent.” These forecasting abilities can not only be used by power generators but also consumers. ABB, a Swedish–Swiss multinational corporation, encourages their commercial and industrial building managers to use AI technology to predict peak times. This can then allow critical processes to be switched on or off to create “meaningful energy cost savings for commercial and industrial buildings.”
Several large electricity companies such as E.ON and Électricité de France in Europe, as well as Exelon in the US and National Grid in the UK, have all adopted AI technology. The Electric Power Research Institute (EPRI) has also begun researching the integration of AI and drone technology to monitor power plants. Several startups in this space are developing AI technologies for predictive maintenance, asset management, and performance optimization.
For example, SparkCognition (now Avathon), has developed sensor technology for power and utilities equipment, combined with an AI platform. This platform detects anomalies and signals indicating that maintenance might be soon required. The platform optimizes maintenance schedules and automates compliance reporting and can also detect anomalies in real time.
Another company called OgreAI has recently closed an investment round for €3 million, to support their AI-powered energy forecasting technology. Other startups such as these leave many opportunities for VC investors to take advantage of the significant and coming technological changes that will accompany growth in the energy sector.
AI in Electricity Transmission
Along with supporting power generation itself, AI technologies can also support electricity transmission processes, including managing electricity flows, smart grids, and fault detection.
AI has a lot of potential to significantly support the electricity grid, particularly in terms of grid load balancing and flow optimization. A recent study from the United Nations Economic Commission for Europe found that AI can improve load balancing through forecasting based on load profiles. Old models are usually used for this purpose, based on the behavior patterns of the power grid (heating vs cooling, night vs day, weekday vs weekend), and mathematical algorithms are used to calculate the expected load. AI technologies can be used for these calculations, often using larger amounts of data, and can close the gap between predictions and reality faster and more reliably than older mathematical models. One case study of such an AI program run by Hydro-Québec is discussed in the “Case Studies” section below.
Smart grid technologies are also becoming rapidly more popular, many of which also include AI integration. In Figure 5 below you can see how a smart grid functions:
Figure 5: Smart grid components
Source: RAND, Marris, 2008.
The U.S. Department of Energy is managing a $10.5 billion Grid Resilience and Innovation Partnerships (GRIP) Program, $3 billion of which is dedicated to “Smart Grid Grants.” These Smart Grid Grants are intended to increase the “flexibility, efficiency, and reliability of the electric power system” and are also intended to “demonstrate a pathway to wider market adoption.” In addition to improving the grid itself in these ways, RAND notes that smart grids will also generate a large amount of data that can also be leveraged for economic value.
AI and other smart tools in the grid can also provide real-time data, improving responsiveness to outages and energy distribution. In some cases, providers can be notified about outages before they happen, and more detailed data can be determined about whether any outage is for an individual household, street, neighborhood, or whole zone.
Finally, automated fault detection in the electricity grid can also be undertaken with AI-enabled tools, and is a key area of innovation in the energy sector. Power distribution grids can be monitored with AI, and in some cases remedies or suggested fixes can also be quickly deployed. A number of both large and small companies have filed significant numbers of patents related to AI fault detection technologies.
For example, companies like IND Technology are producing AI-enabled grid monitoring tools that use predictive analytics to determine whether a fault has occurred. For example, IND Technology’s EFD™ System can determine whether vegetation is touching a power line, or whether a fuse or conductor is damaged.
Other companies like Grid4C are focused on the demand-side aspects of electricity distribution. Grid4C uses AI to combine data from the usage of IoT devices and smart meter readings, and makes predictions about appliance-level energy use, allowing for optimization of distributed energy resources (DERs) and grid assets for consumers.
AI in Energy Storage and Distribution
Energy storage and energy distribution networks are other major areas of development and innovation. Battery storage is a particularly fast-growing technology, especially about the increasing adoption of renewable energies and the need to store excess energy during peak generation times. AI can radically enhance energy storage by analyzing usage patterns and predicting the optimal times to store and release energy. This can balance periods of high renewable generation, such as midday for solar or windy nights for wind power, against times of peak demand, ensuring the grid operates smoothly even during unpredictable supply conditions. AI’s ability to forecast these supply and demand dynamics can also help extend the life of batteries by minimizing overuse and inefficiencies. Remember, most modern grid-scale batteries can store electricity for only up to four hours at maximum output. (NREL) The battery storage market itself is expected to grow from $6.31 billion in 2023, to $86.87 billion in 2034, as shown in Figure 6 below.
Figure 6: Battery Energy Storage System Market Size 2023-2034 (USD Billion)
Source: Precedence Research
The application of AI can improve battery management processes, and (like power generation) can monitor whether batteries are functioning effectively, whether battery assets need to be replaced or if they may be about to fail. In addition, AI can determine the optimal times to charge or discharge power, and how much to release.
Furthermore, AI can contribute to the development of energy storage systems themselves, which are still quite innovative in their own right. For example, a company called Batalyse uses AI technology to collect, document and analyze test data from batteries. It can then determine whether combinations or arrangements of battery materials are functioning better or worse than other R&D technologies.
Some companies like Stem focus on integrating AI with energy storage, particularly for solar storage systems. Stem uses predictive systems to optimize when to charge and discharge storage assets added to solar generators, which increases the resilience and revenue for solar assets. Others like Fluence, with their Fluence IQ™ Digital Platform, use AI and data-driven analytics to examine battery performance for wind, solar, hydro and general storage assets. With this data, performance issues can be discovered, and energy production can be maximized.
While AI has numerous promises and benefits for the energy sector, it also faces a number of key challenges and barriers to adoption.
First, all AI technologies require large amounts of high-quality data. In the energy sector, low quality or insufficient data could lead to inaccurate predictions or automated decisions that could result in grid unreliability, outages, or hazards. For utility companies, significant amounts of data are siloed in various IT and storage systems across their networks, and may not always be stored in the same format. Data is also not always (although increasingly it is) available in real time. In many cases, the data that AI would need to be able to function to improve efficiency or reliability in the grid, is simply not available. Or, when it is available, the quality is insufficient.
Furthermore, regulatory constraints such as due to the General Data Protection Regulation or California Consumer Privacy Act may classify certain types of data as “personal information”, which may leave energy sector companies subject to regulatory constraints. For example, the French Data Protection Authority (CNIL) made a formal notice to a French electricity supplier saying that consumption data from smart meters required the consent of customers before that data could be collected. If rulings such as this become more widespread, energy sector companies using smart meters or other smart technologies integrated with AI, will need to take steps to ensure privacy and other regulatory rules are complied with.
Along with the integration of AI technology into the energy sector comes cybersecurity risks. A research organization, Check Point Research, found that there had been a 70% increase in cyberattacks on utilities and power infrastructure, compared to 2023.
ISS Governance found that 100% of integrated oil and gas companies, and 100% of electric utilities, were at moderate or elevated risk of successful cyber-attacks in 2023, shown in Figure 7 below. Renewable energy and coal were least at risk. Overall, every type of energy sector was assessed to have at least 50% of companies at moderate risk or higher.
Figure 7: Energy Sector Companies at Risk of Successful Cyber Attacks (% of Companies), by Industry, May 2023
Source: ISS Governance
A report by EPRI notes some of the major cybersecurity risks inherent in adopting AI technology in the energy sector. For example, the compromise of analytics functions for predictive maintenance could result in reliance on results that are incorrect, leading to equipment failures. Even worse, the compromised application of AI-recommendation algorithms to power plant processes could lead to serious hazards including nuclear chemical release, equipment failures, unsafe conditions, or employee endangerment. EPRI notes that AI model analysis data, output, the model itself, or code elements could all be vulnerable to cybersecurity attacks. Training data (if compromised or corrupted) could also lead to vulnerabilities.
On the flipside, Intel notes that AI can also be used for increased threat detection, prevention, and response strategies. EPRI also explains that “Human in the Loop” approaches (Figure 8) can ensure that AI-enabled aspects of power plant functioning do not cause additional problems.
Figure 8: Human in the Loop Approach
Source: EPRI
In addition, regulatory and compliance frameworks may need to be established and adopted in the industry to a greater extent than they have been. For energy sector companies using AI, they will likely need to implement risk-based approaches to determine which technologies are lower risk to health, safety, privacy, or failure, and which technologies are high risk. New AI laws, such as the EU AI Act, provide early guidance for companies to a certain extent on what may be required of them going forward. All sectors integrating AI and AI-enabled technologies into corporate processes or physical infrastructure will need to implement organizational processes, testing approaches, review and audit processes, as well as educational frameworks.
Impact on Sustainability and Renewable Energy Integration
AI is also likely to have a large impact on sustainability and renewable energy integration. In particular, solar and wind can be integrated into the electricity grid with significantly more ease. By enabling more accurate weather forecasting and demand prediction, AI allows for greater reliance on renewable sources and better load management. This can reduce the dependency on fossil fuels and help countries meet their climate goals more efficiently. For instance, AI-driven forecasting in wind and solar energy plants can predict variations in generation capacity, allowing grid operators to adjust conventional power generation and storage, accordingly, thus lowering carbon footprints.
AI-enabled solar forecasting, for example, through a collaboration between IBM and The SunShot Initiative, showed a 30% improvement in forecasting. This allows solar to be more easily integrated into the grid. Solar is subject to peaks and troughs of activity, which with this technology can be anticipated in advance. As a result of these fluctuations, the grid still needs to rely to some extent on power produced from coal or gas (which has “grid inertia”, i.e. stable, constantly rotating equipment). The more effective forecasting of initiatives like SunShot, supports the integration of renewables as it allows more reliance on solar during predictably-available times, while ensuring grid stability and reliability.
The World Economic Forum found that the energy industry (shown in blue in Figure 9 below) can benefit from digital use cases, including the application of AI. The application of digital use cases such as AI brings emissions from the energy sector closer to net zero trajectories (and should be combined with other measures, as well).
Figure 9: Digital solutions can accelerate net zero trajectories in high emission industries
Source: World Economic Forum
AI is also being used to improve the carbon emissions of power plants themselves, by improving power plant design and increasing fuel conversion efficiency. For example, NVIDIA combines the use of digital twins and AI to analyze power plants and the physics taking place within the plant. These models are then used to integrate with sensors throughout the plant, to monitor conditions, and to scale up systems that improve decarbonization. For example, when plants have carbon capture, and storage processes integrated as part of their design, these can also be optimized using AI.
Furthermore, decentralized energy systems such as microgrids are poised to expand, with AI-focused solutions able to improve the efficiency of such systems. Microgrids make up only a small percentage of the US electrical grid but have grown in capacity by 11% over the last four years. They are becoming more popular for their ability to tap renewable energy, and also remain resilient in the face of natural disasters such as hurricanes, that may take the macrogrid offline. For example, Hurricane Milton knocked out power to over 3 million homes in Florida in 2024, while Hurricane Helene reduced service to over 5.5 million customers.
Research conducted in 2020 found that AI applied to the energy sector for decentralized energy networks could produce a significant GDP gain, and a significant greenhouse gas reduction image, as shown in Figure 10 below.
Figure 10: Global impact of environmental AI in the energy sector on GDP and GHG emissions in the “Expansion” scenario
Source: Gillham, PricewaterhouseCoopers (2020)
Companies like AutoGrid (acquired by Uplight), use AI to optimize energy use and manage demand, manage distributed energy system asset performance, and ensure the smooth and reliable functioning of distributed energy systems when connecting to the macrogrid. Their platform, AutoGrid Flex™, allows real-time monitoring and optimization of the grid’s functioning. Other companies like Splight, which provides AI-enabled monitoring and decision optimization technology, focus particularly on solar and wind plant asset management. EDP, a Portuguese electricity utilities company, used their corporate venture capital vehicle EDP Ventures to invest €1.8 million in Splight earlier this year.
The Asian Development Bank is currently conducting a pilot study to enable AI to monitor a large, distributed microgrid network. This monitoring can be used to collect data for forecasting and analysis, determine actions that need to take place throughout the grid using neural networks, and monitor reliability. The role of microgrids and distributed energy systems is important in places where large renewable energy infrastructure cannot be built, due to economic issues or ecological or environmental concerns. They can operate independently or can be integrated into the macrogrid, depending on the situation. In either case, they can contribute significantly to a more reliable, resilient grid system.
One key area in which AI is likely to enable additional future growth opportunities is in relation to energy supply. Brookings notes that “more intelligent energy supply systems, in effect, shift outward the supply curves.” AI technologies can analyze maps and geographical locations from the surface and use satellite data to determine where gas and oil might be located. In addition, AI can significantly improve the efficiency of solar and wind farms, both in terms of location and performance. This enablement of greater energy supply can feed into the greater efficiency of power generation and electricity transmission mentioned above.
AI’s role in expanding energy supply can also extend into the realm of energy exploration. By analyzing geological and satellite data, AI can identify potential sites for renewable energy farms, optimize drilling operations for geothermal energy, or even improve the extraction processes in oil and gas, making these industries more efficient and less environmentally harmful.
Furthermore, AI is likely to accelerate decarbonization in both power generation and distribution, due to the higher integration of renewables into the grid, and the application of AI technologies more broadly. When “non dispatchable power” (i.e. wind and solar) is able to be used more flexibly in response to supply and demand through the use of AI, these renewables can become cheaper and more effective. McKinsey explains that getting to 80-90% decarbonization of the power system by 2040 means that “storage would have to be used for longer periods, and demand might need to be managed more tightly, including through active management of building heating and cooling and industrial-load shifting.” All of these needs can be supported through AI improvements to storage, demand management, and AI-enabled plant management systems.
In addition, another potential growth area to consider is the energy trading market itself. A report from Capgemini in 2024 notes that AI can be used for predicting energy prices, optimizing those predictions, optimizing energy trading portfolios, and predicting future trends. Complex market patterns can be analyzed by AI to determine the best choices. Capgemini surveyed energy traders in 2021 and 2024, and found that the use of AI in the energy trading market has increased from 73% to 87%, shown in Figure 11 below:
Figure 11: AI Participation Rate in Energy Trading
Source: Capgemini
One challenge in this area is that only 30% of the companies surveyed had implemented strong governance systems to manage this transformation and use of AI in this way.
Several existing case studies show the impact of integrating AI technology into the energy sector, although many pilots and tests are still underway due to the innovativeness of the technology. One survey conducted by the North Carolina Department of Commerce found that the utilities sector was one of the slowest sectors to adopt AI technology, especially when compared to others such as real estate, finance, or information technology. Nonetheless, some companies and providers are experimenting with AI models.
For example, Hydro-Québec, one of the world’s largest hydroelectricity producers, has been developing a series of AI-based load demand forecasting models since 2018. These models use data from both smart meters and power meters spread across the province of Québec to optimize load demand forecasting. In 2023, their project was improved with the use of deep neural network-based AI models, a feat that is (as of October 2024) “believed to be a first in the context of 24/7 + real-time + security zone.”
Another pilot in Finland run by the company Exaum is experimenting with using AI for “near-instantaneous grid balancing”, to allow more wind and solar to be integrated into the electricity system. They note that the AI technology can allow the “transmission system operator to adjust and control electricity consumption in the grid, while ensuring that sudden changes in production do not cause blackouts or grid imbalance. It can direct and channel excess power production to areas where it’s needed.” After the pilot is expanded, Exaum hopes to expand it further across Finland.
A wide range of government projects and programs are also proposed or are funded to begin such as the “Grid of the Future”, funded with Smart Grid Grants. These show the increased interest in smart grid projects and the likely expansions into this area. Additional projects are listed on the website of the Grid Deployment Office, such as projects integrating AI to deal with the additional load of electric vehicles on the electricity grid. The outcomes of this spate of new projects will be seen in the coming years.
The impact of AI on power generation, transmission, and distribution will be significant. In particular, its application can provide major reliability gains in power plants and generation capacities, as well as increased economic and operational efficiency in plant operation and transmission. The application of AI technology to the integration of renewables can help in particular with increasing sustainability and reducing carbon emissions. Global Corporate Venturing notes the major opportunities in this, reporting that “We haven’t seen yet the full impact of what some call ‘Industry 4.0’,” and highlighting the increasing integration of AI into industrial sites, including in the energy sector.
Data from the International Energy Agency in Figure 12 also show increasing VC investment in the energy sector since 2016, with significant growth from 2021 onwards. Both renewables and energy efficiency, which AI will continue to contribute to, show growth trends in VC investment.
Figure 12: Venture capital investment in energy start-ups, by technology area, for early-stage deals, 2004-2023
Source: International Energy Agency
However, the path forward is not without challenges. Cybersecurity concerns, along with issues surrounding data availability and quality, need to be addressed to fully realize AI’s economic and environmental benefits in the energy sector. In addition, the regulatory and operational safety environment needs to be examined to determine whether existing power generation and industry frameworks apply, or whether new regulations are necessary.
Despite these potential challenges and barriers, AI is likely to act as a major catalyst for transformation in the energy sector, providing significant improvements to efficiency, sustainability, and clean energy, as well as more efficient storage. These technologies show broad promise for VC investment, with numerous companies beginning to capitalize on these new opportunities.
