How Does AI Revolutionize the Energy Sector? Benefits, Use Cases, Implementation Tips

Key takeaways:

• Artificial intelligence will play a key role in transforming the energy sector, helping it overcome efficiency, sustainability, and innovation challenges.
• AI technologies are changing the way energy companies produce, store, and distribute resources, as well as optimize their processes.
• Common obstacles to AI adoption in the energy sector include high costs, compliance challenges, and lack of relevant expertise.
• To ensure data security and optimize implementation costs, energy businesses need a clear AI adoption strategy and the involvement of niche technical experts.

The energy sector is challenged with the need for more energy, sustainable drilling and electrical generation, and upkeep of aging infrastructure. With 12% of energy organizations worldwide already developing AI solutions and 73% piloting or planning AI activities, it’s clear that most industry leaders see AI as one of the answers. 

But how can this technology help transform the energy industry? In this article, we discuss the role of artificial intelligence in the energy and utilities businesses and this technology’s use cases for conventional, nuclear, and renewable energy. We also explore key challenges you can encounter when implementing AI and offer ways of solving them.

This article will be useful for technical leaders of energy organizations who want to know how to enhance their software systems with cutting-edge AI technology.

The role of AI in the energy industry

Changes in global energy consumption are outpacing the growth and speed of changes in the energy sector, forcing energy businesses to look for new approaches and technologies to enhance their routine processes. The most pressing issues the industry faces include high energy prices, grid maintenance and development, and a shift to green energy. Failing to address these issues can result in uncompetitive offers, fines, and legal consequences for non-compliance.

Artificial intelligence will play a key role in transforming the energy sector. With support from businesses and governments, AI investments in the energy market are projected to reach $17.7 billion by 2029, up from $5.9 billion in 2024.

Artificial intelligence has the potential to significantly enhance how we manage the grid, which is one of the most complex yet highly reliable machines on earth.

Beyond the grid, AI can also support a range of applications to help achieve our climate goals. Reaching net-zero greenhouse gas emissions across the economy requires addressing unique challenges across many sectors that have major impacts on energy and the grid – including transportation, buildings, industry, and agriculture. 

AI for Energy. Opportunities for a Modern Grid and Clean Energy Economy

US Department for Energy

AI is capable of automating and helping businesses with many tasks, from monitoring and predictive maintenance for outdated infrastructure to research of energy generation improvements to automation of supply and demand management in smart grids. AI’s role in the energy sector extends beyond standalone applications. AI acts as a strategic enabler, driving efficiency, sustainability, and innovation across the entire energy value chain.

Let’s take a closer look at the benefits of applying AI in the energy sector.

Benefits of applying AI in the energy sector

As AI technologies improve and become more reliable and accessible, companies hope to use them to build smart grids, reduce the rate of equipment failure, find new cost-effective ways of generating energy, and manage their carbon footprint.

Applying AI to improve their current processes and build new methods of extracting fuel and generating, storing, and delivering energy can result in these benefits:

The exact benefits you’ll receive after implementing AI will depend on your use cases and the problems AI solves in your business. In the next section, we overview seven key use cases for AI in the energy sector and take a look at companies that already use this technology.

Key AI use cases in the energy industry

The energy industry is vast and diverse, covering everything from traditional coal plants to renewable energy sources and smart grids. Each type of energy facility comes with its own tasks for AI. While there are many niche applications for this technology, we will focus on the most widespread use cases for AI in the energy sector that have demonstrated significant benefits.

Supply and demand forecasting. Accurately forecasting energy supply and demand is crucial for ensuring grid stability, minimizing costs, and optimizing energy distribution. Traditional forecasting methods often struggle to account for the variability introduced by renewable energy sources such as wind and solar. AI-powered forecasting models use historical and real-time data like weather patterns, market trends, and grid performance metrics to precisely predict resource demand and usage. In this way, AI helps to reduce energy waste by aligning production with demand and helps to efficiently integrate intermittent renewable energy sources.

Engie, a global energy company, has partnered with Google Cloud AI to enhance wind power forecasting. Google’s DeepMind AI system analyzes meteorological data and turbine performance to predict wind energy output up to 36 hours in advance.

Efficiency of oil and gas production. Oil and gas companies face mounting pressure to improve efficiency, reduce emissions, and optimize resource extraction. AI is playing a critical role in achieving these goals by analyzing geological data, helping to discover new drilling sites, and managing drilling operations and equipment. 

Companies like AVEVA provide AI solutions that offer real-time monitoring of refinery operations, helping operators optimize production processes and reduce energy consumption. Market leaders also invest in AI algorithms. Shell has implemented AI-driven analytics for analyzing drilling data to identify the most productive oil reservoirs and improve well placement. 

Safety and efficiency of nuclear power plants. Nuclear power plants operate under stringent safety regulations, and maintaining efficiency while ensuring safety is a top priority. AI-powered systems are being used to detect anomalies and assist in early fault detection, improving both operational safety and cost efficiency. 

In addition, AI-driven simulations help nuclear operators predict how different operational scenarios might impact plant performance. Using AI for training and plant management allows companies to maximize the efficiency of nuclear generation and mitigate risks and safety hazards. 

Development and optimization of smart grids. Energy delivery organizations were building smart grids before the wide adoption of AI. However, artificial intelligence can take such grids to a new level by helping to manage the complexity of modern distribution systems and ensuring efficient energy flow and grid stability. 

AI algorithms analyze data from IoT sensors, smart meters, weather forecasts, and customer behavior to dynamically adjust electricity supply in response to real-time demand fluctuations. Companies like Stem build AI-based systems to enhance energy distribution by integrating renewable sources, managing peak loads, and reducing energy waste. Such AI-powered platforms can help to prevent grid overload and blackouts, improving the reliability of energy delivery services.

Predictive maintenance for equipment. Predictive maintenance is a trend that is quickly spreading across all manufacturing industries, from automotive to energy. AI-based maintenance systems use sensor data and historical performance metrics to predict equipment failures before they happen, allowing companies to schedule maintenance proactively and minimize downtime.

In the oil and gas industry, AI-powered predictive maintenance tools help monitor pipeline integrity by detecting leaks, corrosion, and pressure anomalies. This improves safety, reduces environmental risks, and lowers maintenance costs.

E.ON, a leading European utility company, has implemented AI solutions to monitor the condition of over 700,000 kilometers of power lines. By analyzing vast amounts of data from power lines, substations, and transformers, AI helps to detect potential faults, reduce unplanned outages, and extend the lifespan of critical assets.

Energy storage optimization. With the growing adoption of renewable energy, efficient energy storage has become a critical challenge. AI helps optimize energy storage by optimizing battery charge and discharge cycles. Optimizing energy storage is particularly important for grid-scale battery storage solutions, as it helps utilities maintain grid stability and reduce reliance on fossil fuel-based backup power.

One example of using AI for this purpose is products from Envision Energy that use AI-driven energy management systems to analyze grid conditions, weather forecasts, and patterns of energy use to determine the optimal times to store and release energy. This ensures a steady supply even during periods of low renewable generation.

Energy trading. Energy trading is a complex market influenced by demand fluctuations, fuel prices, regulatory changes, and other factors. When using manual or simple automation tools for energy trading, companies rarely can capitalize on price fluctuations and quickly respond to unusual situations. 

AI is revolutionizing energy trading by providing traders with real-time insights, risk assessments, and market predictions. Platforms like Findly AI use advanced analytics to help companies make informed buying and selling decisions. AI algorithms analyze historical data and external factors such as weather patterns and economic indicators to predict price movements and suggest optimal trading strategies.

Using AI in the energy industry creates many possibilities for transforming and making more efficient many business processes. Combining AI with other technologies can enhance its potential for any use case you have in mind within your AI-powered energy project.

Convergence of AI with other technologies in the energy sector

Not every task in the energy sector requires AI algorithms. You can collect and process information, monitor equipment in real time, secure data with other technologies, and use AI to analyze data and provide you with facts for decision-making. This way, you can reduce the cost of AI adoption and use fewer resources for your IT systems while still getting the best result.

Here are the key technologies used in combination with AI:

Internet of Things (IoT) devices, such as smart meters, sensors, and connected infrastructure, collect real-time data on energy consumption, grid performance, and equipment health.

AI processes this data to forecast demand, automate operations, and help organizations respond to unforeseen situations. This combination is the basis for smart grid management and supply and demand forecasting. 

Big data analytics platforms aggregate and structure information collected by IoT devices and an organization’s IT systems. Such platforms enable recognition of simple patterns, such as periods of peak demand, and help with some analytics tasks like cost estimation and project scheduling. 

AI can enhance big data with proactive capabilities: risk prediction and management, situation modeling, etc. You can combine these two technologies for use cases that require in-depth analytics, such as predictive maintenance and energy trading.

Blockchain provides a decentralized and tamper-proof record of energy transactions and grid data. Solutions that combine blockchain with AI can optimize trading strategies, detect fraud, and protect sensitive data.

Cloud computing platforms provide the storage and computing power AI needs to process vast energy datasets from IoT sensors and historical records. AI algorithms hosted in the cloud can analyze large datasets quickly and deliver insights globally. Cloud-based AI solutions are especially useful for remote production management in international companies and smart grid operations.

Challenges in implementing AI and how to overcome them

Implementing AI in the energy sector presents numerous opportunities, but it also comes with significant challenges that companies must navigate. From data-related issues to regulatory concerns, these challenges can impact AI adoption, performance, and overall business operations. Researching and preparing for them will improve your chances of developing efficient and optimized AI solutions for your energy business.

Issues of AI adoption	Ways to overcome them
Data availability for AI training	– Discover and prepare necessary data – Generate lacking records with AI
AI energy consumption	– Use energy-efficient AI algorithms – Optimize AI models with edge computing – Leverage cloud providers offering energy-efficient AI infrastructure
Outdated manufacturing and delivery infrastructure	– Use custom APIs – Plan for system modernization
Security of business data and smart grids	– Implement relevant security mechanisms – Consider additional security software
Lack of technical expertise in the industry	– Partner with an AI development company
Compliance with regulations on the energy industry and AI use	– Research relevant compliance requirements – Design your software with compliance in mind

Data availability for AI training. AI models require vast amounts of high-quality, relevant, and structured data to accurately respond to requests. While energy companies often have extensive data records, they may face issues with data collection and preparation. These records can be fragmented across departments and facilities, lack consistency, or be stored in diverse or proprietary formats. In old organizations, especially in conventional energy, much of the critical data is siloed in legacy systems or exists in non-digital formats.

Limited access to clean, consolidated data hinders AI’s ability to provide accurate and unbiased responses, which can lead to wrong decisions and missed opportunities.

How can you manage this challenge?

Only people from inside the organization can find and prepare data for AI training because they have industry-specific knowledge. When starting an AI development project from the ground up, we collaborate with a client’s stakeholders to locate necessary data records and prepare and annotate them. In some cases, we can also generate synthetic data that reflects real-life scenarios to increase your AI model’s accuracy. However, this synthetic data has to be based on real records and can’t constitute the majority of the training dataset.

AI energy consumption. Ironically, growing reliance on AI is one of the reasons for the global energy deficit, since this technology is very resource-hungry. Increased energy use from AI can raise operational costs and contribute to a company’s carbon footprint, potentially conflicting with sustainability goals.

How can you manage this challenge?

While an AI-based solution’s high energy consumption is unavoidable, you can ensure that the AI works with maximum efficiency to avoid wasting resources. Here’s what you can do:

• Use efficient AI algorithms, such as reinforcement learning, to minimize processing power while maintaining accuracy.
• Optimize AI models with edge computing to process data closer to the source, reducing energy-intensive cloud processing.
• Leverage cloud providers offering energy-efficient AI infrastructure, such as Google Cloud with its carbon-neutral data centers.

Outdated manufacturing and delivery infrastructure. Aging equipment and IT infrastructure are among the energy industry’s key concerns. Outdated infrastructure can limit AI’s potential by preventing real-time data collection, slowing down analytics, and increasing maintenance costs. It can also lead to integration challenges when modern AI solutions must interact with old systems.

How can you manage this challenge?

Apriorit’s development team can implement custom APIs to integrate new AI solutions and features into your software. When developing custom integrations, we assess AI needs, your system’s capabilities, and data security and processing requirements. Alternatively, we can help you design and implement gradual infrastructure modernization, such as retrofitting existing equipment with IoT sensors to enable data collection.

Security of business data and smart grids. AI adoption increases the attack surface for cyber threats, especially in critical infrastructure such as smart grids and energy management systems. The vast amounts of sensitive data generated by AI-driven systems can be targeted by hackers, posing risks to business operations and customer data. Cyberattacks can lead to service disruptions, financial losses, regulatory penalties, and loss of clients’ trust.

How can you manage this challenge?

As part of our secure SDLC approach, we start considering a product’s security at the earliest development stage. We implement data encryption, access control features, threat detection, and other security mechanisms relevant to your business and applicable compliance regulations.

You can further secure AI-based solutions with dedicated AI security software and other technologies like the blockchain.

Lack of technical expertise in the industry. The energy sector traditionally focuses on engineering and operational expertise, with limited in-house knowledge of AI technologies. The complexity of AI solutions requires engineers to combine skills in AI development, data science, and integrations with low-level technologies and custom devices. Considering the global talent shortage in IT and the high cost of recruiting, onboarding, and employing AI development specialists, forming a dedicated development team is out of the question for many energy companies.

How can you manage this challenge?

As an outsourcing company with many years of experience in AI development, we can provide you with a dedicated team or independent specialists for your project. We have a proven track record of successful projects in AI, IoT, and embedded development, as well as data management and integrations of complex systems. We can provide you with any specialists you need, from business analysts to QA engineers and developers to UI/UX designers. 

Compliance with regulations on the energy industry and AI use. The energy sector is already highly regulated, with stringent compliance requirements regarding safety, environmental impacts, and data privacy. AI solutions must adhere to industry-specific requirements such as NERC CIP in the US and the GDPR in the EU for data privacy, and AI-specific regulations like the EU AI Act. Non-compliance can result in hefty fines, legal challenges, and reputational damage. 

How can you manage this challenge?

With the help of our BAs and project stakeholders, we define relevant laws, standards, and regulations at the beginning of each project. All requirements from these documents are compiled into a list and implemented during development. 

Delivering an AI-based solution in the energy industry presents significant challenges, but with the right strategies and a structured approach, you can overcome these obstacles and harness AI’s full potential.

How Apriorit can help you develop an AI-based energy solution

With a strong track record in AI-powered projects, our team helps energy companies harness the full potential of AI while ensuring regulatory compliance, data and system security, and integration with elements of other IT systems.

Here are the key reasons to trust us with developing your AI-powered project for the energy industry:

Our experience in AI development, generative models, and data analytics puts us in a unique position to deliver complex AI-based systems and features within the energy industry and other highly regulated industries.

Conclusion

AI makes many energy-related operations more efficient, enabling predictive maintenance for grid systems and equipment, delivery and storage optimization, and data-driven decision-making. However, to benefit from AI in the energy sector, you need to sort out technical, organizational, and compliance challenges.

To successfully adopt AI, energy companies need a strategic approach that balances technological advancements with operational realities. Apriorit’s dedicated development teams can help organizations overcome these challenges by offering specialized expertise, developing security-focused software, and building complex products that adhere to your industry and business requirements.