Top 10: AI applications in the energy sector

Applications of AI

Nonetheless, AI holds great future potential for the energy industry. Already key to optimizing energy generation, distribution, and consumption, AI will continue to do so in the future as it gets better trained and smarter. AI can and will continue to improve the efficiency of renewable energy sources, increase grid stability, and reduce greenhouse gas emissions. But it doesn't stop there: the opportunities in this field are, arguably, nearly limitless.

Here are 10 key ways AI is being deployed in the energy sector and how it will evolve as intelligence develops.

10.Nuclear power plant monitoring

According to IEA data, nuclear power provides about 10% of the world's electricity generation, solidifying its position as a key low-emission source of electricity. Advances in AI in nuclear power have significantly reduced human error, significantly improved safety, and contributed to transforming how power plants are monitored. “AI, combined with other technologies such as digital twins, can decisively increase the efficiency of nuclear power,” said Nelly Ngoyi Kubelwa, a nuclear engineer at the International Atomic Energy Agency (IAEA). Machine learning (ML), an application of AI, has been applied in the field for years and has proven useful along with algorithms used for real-time monitoring and predictive maintenance.

9. Oil and Gas Exploration

AI has a wide range of applications in the oil and gas sector: from reservoir analysis to drilling optimization, from anomaly detection to safety monitoring, AI can also reduce emissions in the sector, having a revolutionary impact on exploration, production, and the overall environmental sustainability of oil and gas.

Consultancy EY says the application of AI is improving efficiency across the entire oil and gas value chain, including early exploration activities, and “bringing new ways of approaching exploration, development, production, transportation, refining and sales.”

For example, AVEVA uses AI to optimize upstream process performance, safety, and sustainability. Their predictive analytics platform is AI-powered and leverages over 15 years of experience delivering AI-based predictive analytics at scale to help industrial users identify anomalies in their assets weeks or months in advance.

8. Smart Homes and Buildings

AI has had a transformative impact in transforming buildings into more energy-efficient environments. For example, smart meters and IoT devices work with AI to provide real-time insights into consumption, allowing data-driven decisions to optimize energy usage and reduce operational costs. “When it comes to energy efficiency in smart buildings, AI is the game changer. Through advanced data analytics, predictive algorithms, and real-time monitoring, AI optimizes various aspects of building operations, resulting in significant energy savings, cost reductions, and a greener future,” says Utilities One, an infrastructure solutions company. In a study by the IBM Institute for Business Value (IBV), 70% of executives surveyed said intelligent machines provide new categories of insights that enhance their decision-making.

7. Energy Trading

Used under the right conditions, AI has great potential for energy trading. A recent study by the Fraunhofer Institute for Energy Economics and Energy Systems Technology (Fraunhofer IEE) suggests that energy trading could become more flexible thanks to AI. In their analysis, AI managed to achieve results on par with human sellers. AI also understands complex market dynamics and can process real-time data on price, demand and supply trends. This allows energy companies to make informed and profitable trading decisions.

6. Carbon Capture, Utilization and Storage (CCUS)

AI-assisted CO₂ capture helps optimize CO₂ capture and storage from industrial processes and power plants. CCUS processes can be improved by AI, which AI seismic interpretation software developer Geoteric calls a game changer in the field. The company's case study report explores the possibility of using AI networks to quickly and accurately screen potential CCUS sites, focusing specifically on the Triassic Bunter Sandstone in the southern North Sea. Detailed AI fault analysis shows that the selected site has minimal faults, making it a promising location for CCUS purposes. AI-driven systems can identify the most appropriate way to utilize the captured carbon, whether for industrial processes or safe long-term storage, thus playing a key role in reducing greenhouse gas emissions and mitigating climate change.

5. Energy Storage

In this context, hailed as the concept of optimization, AI optimizes the storage and distribution of energy from renewable sources. Using algorithms that monitor and determine things like demand, supply, price, and grid conditions, AI can tell companies the best time to store energy, when to release it, and how much to distribute. This is increasingly evident with more intermittent renewables like wind, where AI can tell them to store excess energy generated during peak times and use it when power isn't being produced.

Dr Julian de Hoog, co-founder and CEO of Solstice AI, advocated for this approach during his tenure as Energy & IoT Technical Lead at the IBM Australia Lab. “In a project (funded by the state of Victoria) with two industry partners, inverter manufacturer Selectronic and energy storage provider Relectrify, we learned how to optimally combine batteries, solar panels, inverters and cloud-based services to extract the most value from these systems,” he said.

4. Renewable Energy Forecast

And when it comes to renewable and intermittent energy sources, AI can help predict when renewable energy is best generated and how much will be available at any given time, thereby more accurately balancing supply and demand.

VTT EnergyTeller, a solution from Finland's VTT Technical Research Center, is an AI-powered service for more accurately forecasting future energy demand and market trends. According to the research and technology company, a 1% error in wind power forecasts costs Finland around €300,000 (USD 326,700) per year in imbalance adjustments. “Today, more data is available than ever before,” VTT said. “With the right energy forecasting service, you can leverage market and grid data to create accurate forecasts. Forecasts can be used to optimize the transition to renewable energies and ensure reliable energy availability in any situation.”

3. Predictive maintenance

Given the predictive nature of AI, its capabilities allow energy companies to forecast when their equipment will break down or require maintenance. “Artificial intelligence, and in particular machine learning, has given rise to a process known as predictive maintenance,” power company E.ON said. “AI can quickly and accurately process and analyze vast amounts of data collected from equipment. It can detect subtle patterns and correlations that indicate an impending failure.”

ML can analyze large amounts of data from various sources to predict potential failures before they happen, minimizing downtime, reducing repair costs, and improving overall reliability.

2. Demand Response Management (DRM)

DRM is essential to optimize electricity consumption and ensure grid stability by adjusting consumers' electricity usage in response to signals from operators and providers. By leveraging AI, DRM can optimize and control electricity consumption in response to demand fluctuations through analysis of real-time data. According to the IEA, flexibility becomes increasingly important as the grid becomes increasingly dominated by variable generation such as wind and solar power, as demand response involves providing incentives to shift or reduce electricity demand in wholesale and auxiliary electricity markets to balance the grid.

1. Smart Grid

Smart grid technologies help regulate, store and distribute power from these sources into a steady, reliable flow. This is where AI comes in handy, as predictive analytics can forecast rising demand and distribute it across multiple power plants and substations, according to SAP. Cloud-connected AI technologies such as ML, data analytics and IoT improve the capabilities of smart grids to enable complex power generation and distribution. “AI, cloud and digital technologies enable all devices and assets in the grid to communicate, supporting better control and self-regulation,” explains the multinational software company.


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