Artificial Intelligence (AI) in Energy – Datamation

Innovative advances in artificial intelligence (AI) in energy are improving the way the world consumes, sells, and creates energy at a time when the environmental impacts of the global power grid are under constant scrutiny. 
Companies are using AI and machine learning (ML) to take deep dives into data that can drive better decision making, cost advantages, and predictions that can stave off energy disasters and expensive downtime. 
On the whole, the global energy sector produces an incredulous amount of data — so much so that it would be virtually impossible for humans to manually extract and analyze enough of these data stores to arrive at meaningful conclusions and predictions. AI, in particular, machine learning AI, is taking on a vital role in making sense of massive amounts of energy-related data. 
AI-enhanced software platforms and smart tools can dig into energy data to create predictions about potential outages and equipment failures and run energy companies more efficiently and profitably. 
AI in energy is being wholeheartedly embraced by companies around the world. Emergen Research reports that the global AI in the energy market reached a market size of $3.8 billion in 2020 and the market will grow by nearly 24% through 2028, when the research firm estimates a market value of $20.8 billion. 
Here, we take a look some ways artificial intelligence is being used in the energy, including within the oil and gas industry and renewable energy sector.
Equipment failure is a common, expensive concern across the global energy sector — one with potentially disastrous consequences. AI-fueled tools can help companies create ideal maintenance schedules by analyzing data from sensors used to monitor equipment and detect failures ahead of disasters. Ultimately, these advances are saving companies millions of dollars and adding efficiency and reliability to energy production and consumption.  
Companies like ABB apply AI to discover faults like cracks in pipelines and machinery through image analysis. As an example, the company points to a pilot project with one of the world’s biggest hydroelectric utilities. After using ABB’s platform, the company showed a 10% reduction in routine maintenance and a 2% increase in output, figures that the company says amounts to millions of dollars in cost savings. 
Schneider Electric uses machine learning through Microsoft to monitor and configure oil and gas pumps in the field for earlier detection of pump failures. One of the company’s largest clients, Tata Power, India’s largest power generator, saved $300,000 when the technology found a maintenance issue early. 
Schneider Electric leverages Microsoft’s machine learning capabilities to monitor and configure pumps in the oil and gas field remotely, since early detection of a pump failure can avoid weeks of the equipment being out of commission and repair costs of up to $1 million. 
AI powers automation across a wide range of industries, including the energy sector. In addition to automating mundane, repetitive tasks that were traditionally been conducted by human workers, AI insights are helping oil companies identify exactly where to drill, saving countless work hours and millions of dollars. 
Companies like BP are investing in machine learning platforms to find new oil stores faster and recover more oil altogether through Internet of Things (IoT) sensors. Ultimately, BP has reported improved profitability directly linked to these enhancements. 
The U.S. Energy Information Administration (EIA) reports that nearly half of U.S. energy users have smart electrical meters installed, typically placed at their homes by local utility companies. These meters provide data about personal energy consumption that utilities can use to make better predictions about upcoming energy usage levels and that customers can use to better regulate their consumption. 
Intelligent power consumption through AI-driven smart home solutions through companies like Google and Amazon are also being adopted more frequently, according to the EIA. These devices communicate with other household devices to identify energy waste. For example, consumers can figure out when the cheapest time is to charge their electronic vehicles or run their air conditioners. 
In deregulated energy markets like the U.S., consumers are able to select their energy providers. Tools like the Carnegie Mellon University-designed Lumator analyzes data about customer preference and consumption and compares it to available energy provider offers, including limited-time promotional rates. Consumers can use Lumator to select energy companies that offer the best deal on the type of energy sources they use the most. Over time, as Lumator “learns” more about customers, it can automatically switch energy plans as better deals arise without interrupting service. 
In addition to cost savings, tools like Lumator can help to increase the share of renewable energy being used by analyzing consumer preference for renewable energy sources and reporting on the demand to energy producers, which can adjust supplies accordingly. 
One of the more futuristic-feeling examples of AI in energy is the creation of autonomous robots that can take the place of humans in dangerous power-related situations. Self-driving machines can take on tasks like surveying high-voltage power lines or even search the seafloor for valuable resources, versus sending human divers into dangerously deep waters. 
ExxonMobile partners with the MIT Energy Initiative to develop autonomous robot capabilities, investing in the technology that will increase the robots’ ability to carry out complex tasks. The MIT team modeled their self-learning AI robots after the Mars Curiosity Rover, literally linking together technology that is exploring new horizons on a distant planet and at the bottom of Earth’s oceans. 
Looking ahead, it’s clear that AI will continue to play a pivotal role in the global energy sector — one that could help to address ongoing environmental issues related to power consumption across the world.
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