10 AI In Energy Management Trends To Look Out For In 2024

A report by Accenture states that AI adoption in the energy sector could result in a 20% increase in energy efficiency by 2035.

What Role Does AI Play in Reshaping the Energy Management Industry?

In recent years, AI has become an increasingly important technology in the energy and power industries. It can automate and optimize a variety of energy-related activities, leading to more efficient and less expensive operations, improved energy management, and less adverse environmental impacts. Demand forecasting is one of the most important areas where AI is being employed in the energy business. Utility businesses may improve resource allocation and management with the help of AI systems that can more precisely predict energy usage by analyzing data on consumer behavior, weather patterns, and other variables.

With the help of AI, the generation and distribution of energy might be optimized.

Machine learning algorithms, for instance, may analyze data from solar or wind power plants to identify patterns and make predictions about future energy production. The sometimes erratic output of renewable energy sources may be more manageable for operators. One of the most important applications of AI in the energy industry is in the field of building energy management. Artificial intelligence-enabled devices may monitor and assess a building’s energy use, identifying wasteful practices and providing recommendations for improvement. This has the potential to save building owners and occupants a lot of money while also reducing their carbon footprint.

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Energy Intelligence

Utilities may benefit from artificial intelligence (AI) capabilities like machine learning, natural language processing, and computer vision in a variety of ways, including increased accuracy in demand forecasting, more efficient energy generation and distribution, and faster troubleshooting of malfunctioning machinery. The efficiency and quality of services provided by a facility can be improved while costs are reduced.

Since utilities are under increasing pressure to optimize energy production and distribution to meet rising demand while also ensuring that their systems remain reliable and cost-effective, the market for AI in the energy and power industry has benefited.

The U.S. Energy Information Administration (EIA) estimates that global energy consumption will increase by over 50% between 2018 and 2050.

By improving the grid’s capacity to incorporate renewable energy sources and controlling energy storage and distribution, artificial intelligence can help alleviate a variety of problems related to renewable energy use. This has the potential to improve the power system’s dependability and stability while also decreasing the cost and increasing the sustainability of energy generation.

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Top Companies in the Energy Sector

• Alpiq
• SmartCloud Inc.
• General Electric
• Siemens AG
• Hazama Ando Corporation
• ATOS SE
• AppOrchid Inc.
• Zen Robotics Ltd.
• Origami Energy Ltd.
• Flex Ltd.

Top 10 Trends of AI in the Energy Sector

Smart Grids

A smart grid is an idea made possible by the use of AI in energy management. To maximize efficiency in power generation, transmission, and use a “smart grid” incorporates existing power infrastructure with cutting-edge technology such as artificial intelligence.

The International Energy Agency (IEA) estimates that smart grids with AI applications may cut power consumption by 10 percent and greenhouse gas emissions by 15 percent.

AI algorithms can evaluate real-time data from smart meters, sensors, and IoT devices to discover abnormalities, forecast equipment breakdowns, and optimize energy flow. AI helps utilities find that sweet spot between supply and demand by smartly regulating energy distribution. Less energy wasted and a marked improvement in the effectiveness of the grid as a whole. Artificial intelligence is about to have a profound effect on the energy management industry.

Microgrids

Microgrids are smaller versions of electricity grids that may function autonomously from the larger, more central grid. Artificial intelligence and machine learning are utilized by microgrid control systems to regulate energy flow and maximize efficiency. Microgrids are gaining popularity due to their ability to integrate renewable energy sources into the energy grid and offer backup power in the event of an outage.

Detecting Energy Theft and Fraud

As much as $6 billion is lost annually in the United States due to electricity theft and fraud in the energy and utilities sector.

Energy theft occurs when someone illegally takes power from the grid. Misrepresenting energy statistics or use is considered energy fraud. Automated anomaly detection with AI and ML can alert utilities to potential problems. In doing so, energy providers may safeguard their assets, cut down on unnecessary energy use, and pocket the savings.

Grid Management Energy Efficiency and Demand Response

Sustainable energy management relies heavily on improving energy efficiency, and AI is crucial in this regard. Artificial intelligence systems can analyze consumption habits and construct energy models to pinpoint inefficiencies and provide solutions to cut down on waste.

Artificial intelligence makes possible demand response schemes that pay people to reduce their energy use during high-demand times. Consumers may help alleviate grid congestion and support a cleaner energy environment by using AI-enabled smart devices and home automation systems to engage in demand response efforts.

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Energy Trading

Due to the time-sensitive nature of energy delivery, trading in energy is distinct from trading in other commodities. For energy dealers, this poses a difficulty but also an opportunity since the energy market is becoming more liquid. Predicting energy demand and giving traders real-time information about energy pricing are two ways in which AI and machine learning might improve the efficiency of the energy trading market.

Energy brokers can use this data to better time their purchases and sales of energy. Power purchase agreements (PPAs) are a new kind of financial contract that may be executed on the blockchain. The adoption of blockchain technology improves the effectiveness of these contracts since it speeds up transactions, lowers associated costs compared to more conventional PPA platforms, and is built on a more robust and reliable infrastructure.

Grid Safety

Because of its complexity, the electricity infrastructure is susceptible to cyberattacks.

By thwarting cyberattacks in advance, AI and machine learning can make power systems safer for everyone. Data analytics is used to look for indicators of a cyberattack in energy usage data. Artificial intelligence and machine learning can be used to counteract cyberattacks once they have been detected.

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Predictive Analytics

AI’s use of predictive analytics is a significant addition to the field of energy management. Predicting energy consumption patterns, weather conditions, and equipment performance are all areas where AI systems thrive through the analysis of massive amounts of historical and real-time data.

A recent report predicts that global AI in the energy sector is expected to reach $7.78 billion by 2025, driven by the increasing adoption of predictive analytics.

For instance, utilities may improve electricity generation and distribution by using AI algorithms to forecast peak energy demand. In addition to saving money, this improves the reliability of the power grid. AI aids energy suppliers in making wise choices and optimizing resource allocation through precise predictions of energy usage.

Customer Engagement

AI and ML are being put to use for the first time in the energy industry to improve interaction with customers. Companies in the energy industry may better serve their consumers’ demands by applying AI and machine learning. Data analytics are used to learn about customers’ energy consumption patterns, and those patterns are then used to tell consumers about how they might cut their energy use through behavioral changes.

Boosted Output

The energy industry is likewise making use of AI and ML to boost output. Machine learning algorithms are being used by the oil and gas industry, for instance, to optimize well location and boost output. Companies may make more informed judgments about where to drill for oil and gas by analyzing data gathered from seismic surveys and other sources. This will improve energy efficiency while also making the electricity grid simpler and more efficient.

Energy-Storage Devices

By 2030, the energy storage industry is expected to have expanded by a factor of 20. Integrating smart energy storage devices into the electric grid is a step toward more effective energy management. Virtual power plants, which are made possible by energy storage and allow utilities to meet peak demand even when supplies are low, are another example of this trend. As a result, fewer new power plants will need to be constructed by the energy industry.