How Will Big Data And Machine Learning Change The Global Energy Industry?

- Jun 17, 2019-

How will big data and machine learning change the global energy industry?

The latest news: machine learning, big data and automation are revolutionizing the global industrial system, and the energy industry is no exception.

All kinds of innovation achievements drive technological progress, improve economic efficiency, create more intelligent business operation mode, and provide infrastructure

Machine learning, big data and automation are revolutionizing the global industrial system, and the energy industry is no exception.

All kinds of innovation achievements promote technological progress, improve economic efficiency, create more intelligent business operation mode, and provide stronger resilience level for infrastructure.

That is why companies and institutions around the world are actively looking to advanced technologies, particularly artificial intelligence, as a top priority.

In the energy industry, many companies are implementing big data and AI technologies in a variety of ways, and the overall enthusiasm of the industry is also growing rapidly.

The oil and gas industry's total AI software market is expected to reach a staggering 28 percent by 2022.

Half a billion dollars.

The resulting predictive analytics capabilities will monitor miles of underground pipelines, while machine learning technology will help fossil fuel companies drill more cheaply and efficiently to gain insight into the geology buried deep below the surface.

New "smart" grids are also using machine learning to integrate computers, automation and sensor devices to monitor and even predict rapid changes in energy demand in real time.

Technology competition on a global scale

In February this year, the United States released the American AI initiative, which aims to promote federal spending and resource allocation across industries, academia and other non-federal entities, so as to achieve technological breakthroughs in AI and maintain the United States' leading position in AI technology.

In may, U.S. energy secretary rick perry announced that the department of energy (DOE) was working with clay and AMD to build Frontier with three new machines.

These machines will improve American AI technology.

Frontier is regarded as the world's fastest computer, with speeds about 50 times faster than current supercomputers.

In this regard, China has also joined the AI race early and is ahead of the United States in some indicators.

In 2017, China released a new generation of artificial intelligence development plan, which Outlines specific research and development funds and targets for AI development, hoping to make China a global leader in AI technology by 2030 and contribute about 150 billion us dollars to the national economy.

While the United States dominates most ai-related metrics, China has a clear advantage in initiating equity financing and data collection.

China also has 17 of the world's top 20 universities for AI research.

AI is reshaping the energy industry

The use of artificial intelligence in energy is proliferating.

XTO Energy, a subsidiary of exxonmobil, for example, is working with Microsoft to gather data from its 1.6 million acres of oil fields using machine learning, business intelligence applications and cloud technology.

The real-time data will be able to improve leaks and repair conditions in drilling and monitoring infrastructure.

This partnership also makes XTO the largest oil and gas company in the cloud.

It is estimated that Microsoft's technology implementation could help XTO increase oil production to 50,000 barrels a day by 2025.

In addition, AI can help improve the security of energy infrastructure.

Pg&e is already adopting machine-learning technology to deal with California wildfires exacerbated by climate change.

In fact, some wildfires have even been completely spontaneous, causing billions of dollars in damage.

California power is now trying to use drones to inspect its power towers, and then use AI to transform the images into data points -- up to a billion of which it can now collect.

This data is fed into an algorithm that determines where the company should concentrate its resources to reduce the risk of potential wildfires.

At the same time, smart energy systems will increasingly allow customers to use renewable energy to power their homes and businesses.

Renewable energy is intermittent in nature because it comes mainly from solar radiation, cloud cover, wind and waves.

Modern technology can help utilities change the flow of these less stable sources of energy through smart grids, and the smarter the better.

As energy storage capacity improves, companies will be able to transfer more of their surplus power to utility-grade batteries -- including lithium-ion and hydrogen fuel cells.

AI technology isn't perfect

Although AI technology can make great contributions to our economy, it can also bring very terrible risks.

The actual effectiveness of computers and machines is limited by the level of programming that is done entirely by humans, so devices are as far from perfect as the humans who developed them.

At the same time, increased digitization and interconnection increase the possibility of devices being manipulated or even damaged.

The more we rely on big data and machine learning, the more formidable the cybersecurity risks to our users -- a problem already being addressed by the grid and other utilities.

Ironically, they often need the help of artificial intelligence to solve such problems.

Therefore, governments and regulators around the world must establish appropriate policy mechanisms to address the specific problems and risks associated with scaling.

For example, the United States recently met with members of the organization for economic cooperation and development (oecd) to determine principles and guidelines for the international use of ai technology.

This undoubtedly marks an important step forward for governments towards responsible AI implementation.