April 15, 2024

Top 10 applications of AI and robotics in energy

5 min read

Micah Horner, Product Marketing Manager at TimeXtender, outlines the top ten new technology applications and their benefits to the energy industry. The incorporation of new technologies is creating a wealth of new opportunities for energy companies to increase efficiency, optimize performance, drive innovation and accelerate growth.

However, rather than simply imitating solutions developed in other industries, it is important for energy companies to focus on energy-specific solutions.

According to Micah Horner, product marketing manager at TimeXtender, the energy industry is particularly well suited to take advantage of these next-generation technologies. Here, he outlines the top ten applications and their industry advantages.

10: Customer Engagement

The energy industry is starting to use artificial intelligence and machine learning to attract customers. By using artificial intelligence and machine learning, energy companies can provide customers with information specific to their needs. This includes using data analytics to understand customers’ energy usage and then providing customers with information on how to reduce energy consumption by changing usage habits.

09: Microgrid

A microgrid is a small energy grid that can operate independently of the traditional energy grid. Microgrid control systems use artificial intelligence and machine learning to manage energy flow and optimize energy use. Microgrids are becoming increasingly popular because they can provide energy security during emergencies and integrate renewable energy into the energy grid more easily than traditional grids.

08: Power theft and energy fraud detection

Globally, electricity theft and fraud cost the energy and utilities industry as much as $96 billion a year, including $6 billion in the U.S. alone. Stealing is the illegal harvesting of energy from the grid. Energy fraud is the deliberate misrepresentation of energy data or energy usage. AI and machine learning can automatically detect these anomalies and flag them for energy companies to address. This allows energy companies to protect their assets, reduce energy waste and save money.

07: Energy Trading

Trading energy is different from trading other commodities because energy must be delivered immediately. This presents a challenge for energy traders, but also an opportunity, as energy markets become increasingly liquid.

Artificial intelligence and machine learning can improve the efficiency of energy trading by forecasting energy demand and providing traders with real-time information on energy prices. Armed with this information, energy traders can make more informed decisions about when to buy and sell energy.

Blockchain technology has been used to create a power purchase agreement (PPA), a type of financial contract between energy buyers and energy sellers. Blockchain technology makes these contracts more efficient as it shortens transaction times, costs less to use than traditional PPA platforms, and is built on a highly secure platform.

06: Energy storage

By 2030, the global energy storage market will grow 20 times. A smart energy storage system is an energy storage technology that can be integrated into the energy grid to improve energy management efficiency.

Energy storage is also being used to create virtual power plants, enabling energy companies to provide energy when needed, even if their current energy supply is insufficient. This helps reduce the need for energy companies to build new power plants.

05: Predictive Analytics

Predictive analytics can be used to forecast changes in future energy demand. This information can then be used to plan for the future and build the necessary infrastructure to meet future energy needs.

By using predictive analytics, energy companies can also predict when machines or equipment are likely to fail. Not only does this help prevent unplanned outages, but it also allows companies to plan for the replacement of critical and expensive energy assets, saving money by avoiding unscheduled maintenance work.

04: Increased oil and gas production

AI and machine learning are also being used to improve production in the energy sector.

For example, oil and gas companies are using machine learning algorithms to improve well placement and increase production. By analyzing data gathered from seismic surveys and other sources, these companies can make better decisions about where to drill for oil and gas. This will increase energy efficiency and create a simpler, more efficient energy grid that is easier to maintain by energy companies.

03: Grid management and efficiency

AI optimizes the grid by managing the flow of energy between homes, businesses, storage batteries, renewables, microgrids and the grid itself. This reduces energy waste while increasing consumer participation in energy consumption.

Renewable energy sources such as wind and solar are gaining popularity, but they are intermittent. This means that these energy sources are not always available when needed.

This presents a problem for the energy grid, since energy must be managed in real time as electricity is generated. AI and machine learning can help energy companies predict when renewable energy will become available and manage energy networks accordingly.

Robots are also used in energy installations and grid maintenance, as well as monitoring energy generation and consumption. Robots can be used for tasks such as repairing pipelines, wind turbines and other energy infrastructure. By automating these tasks, energy companies can further improve efficiency and reduce costs.

02: Energy Cybersecurity

The energy grid is a complex system that is vulnerable to cyber attacks. AI and machine learning can improve the security of the energy grid by preventing cyberattacks before they occur.

This involves using data analytics to identify patterns in energy data that could indicate a cyber attack. Once a cyberattack is identified, artificial intelligence and machine learning can be used to counter the attack.

01: Smart Grid

Grids can now be integrated with sensors, data analytics tools, energy storage systems, energy management platforms, and other types of energy technologies to become “smart.”

By using a smart grid, energy companies can collect energy usage data from every device on the grid, and then use that information to develop energy efficiency programs for customers. It also allows energy companies to monitor energy flows and energy usage in near real time.

The energy company can then reduce energy consumption with an automated demand response system that shuts off energy during peak hours, saving energy for both the homeowner and the energy company.

Top 10 applications of AI and robotics in energy

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