Five Application Directions and Corresponding Cases of Artificial Intelligence in the Energy Industry

Since 2012, the news that AI and the energy industry are reporting together has begun to increase. This article briefly describes the five application directions of artificial intelligence in the energy industry and their corresponding cases.

The energy industry generates a lot of data. In order to turn this data into a driving force for increasing productivity and cutting costs, major energy companies—oil and gas giants and renewable energy companies—have turned their attention to artificial intelligence.

Since 2012, the news that AI and the energy industry are reporting together has begun to increase. This article briefly describes the five application directions of artificial intelligence in the energy industry and their corresponding cases.

According to a recent report by Greentech Media, the United States’ energy storage reached a new milestone in the fourth quarter of 2017: In the period from 2013 to 2017, cumulative reserves have exceeded 1,000 MWh. The report also predicts that this figure will double this year. With the increase in storage capacity and the emergence of new technologies, artificial intelligence is improving the efficiency of this market.

Case 1: Stem

Stem, based in California, developed a project code-named Athena, which uses artificial intelligence to map energy usage and allows customers to track energy price fluctuations to use the stored energy more efficiently.

Stem has absorbed more than $37 million in funding from a number of investors including the US Department of Energy, GE Ventures and Singapore’s sovereign wealth fund Temasek Holdings.

Today, there are usually many sources of energy for the grid. In addition to conventional power generation, there is also wind energy and solar energy, which makes the process of operating the power grid system more complicated. Through artificial intelligence to analyze large-scale data sets, this multi-source collection process is more stable and efficient.

Case 1: US Department of Energy

In September 2017, the U.S. Department of Energy issued a research award to SLAC researchers at Stanford University to reward them for using artificial intelligence to improve the stability of the power grid. By using past data to program power fluctuations and grid weaknesses, the new "smart grid" will automatically respond quickly and accurately to major events.

Case 2: Siemens

Smart grids can also better manage different types of energy at the same time. Siemens released a software package to operate the network, the so-called "active network management" (ANM, active network management).

The principle of ANM is to adjust its adjustable components by tracking how the grid interacts with different energy loads, so as to achieve the purpose of improving efficiency. Although this was manually adjusted before, when new energy producers (such as solar power plants) start working, or when new energy consumers start to access the grid, ANM will make corresponding adjustments to the grid. Therefore, ANM also lays the foundation for electric vehicles using smart grids for charging.

Case 3: British National Grid

In March 2017, DeepMind, an artificial intelligence company acquired by Google, and the British National Grid jointly announced that they plan to add DeepMind's artificial intelligence technology to the UK's power system. The project will deal with massive amounts of information such as weather forecasts and Internet searches to develop predictive models of a surge in demand.

Case 4: Grid Edge

A British company called Grid Edge (providing cloud-based power management software services) claims that they use artificial intelligence technology to predict and optimize energy allocations, enabling control to be returned to electricity users. The specific method is that Grid Edge operates a VPN to connect and analyze the energy consumption data of the building where the user is located. Using this information, Grid Edge communicates with the connected power grid and formulates corresponding scheduling strategies. The purpose of these strategies is to save energy and avoid overloading.

In November 2017, a coal-fired power plant in northern India exploded, killing 32 people, due to the blockage of the gas pipeline and the explosion of the boiler. This is a type of fault that often occurs in the energy industry. The cause of the accident is that there is no regular inspection of the equipment, and there are no strict regulations in many parts of the world. Therefore, equipment failure is very common.

Using artificial intelligence to observe equipment and detect failures before an accident can save time and money and even save lives. At present, many startup companies are trying to provide this service to the energy industry.

Case 1: SparkCognition

In December 2017, the US Department of Energy awarded SparkCognition a prize that uses artificial intelligence to increase the amount of power generated by coal-fired power plants. The company combines analytics, sensors, and data from operations to predict when critical infrastructure will crash.

Case 2: AES Corporation

In September 2017, the American energy giant AES Power announced plans to enter artificial intelligence as a means to increase the company's vigilance, efficiency, and protection of corporate assets, mainly for their solar power plants and power grid systems.

Case 1: Beyond Limits by BP Ventures

BP Ventures (BP Ventures) has invested in an artificial intelligence company called Beyond Limits. The company had participated in exploration tests conducted in outer space. When investing in Beyond Limits, BP Ventures said it plans to use Beyond Limits' oil and gas exploration technology to find new oil reserves.

Case 2: Chevron (Chevron)

The oil giant Chevron is using artificial intelligence to find new wells throughout California, as well as old wells with extra value.

By monitoring the energy consumption behaviors of individuals and companies, artificial intelligence can provide solutions to optimize the energy consumption process.

Case 1: Alphabet's Nest

Nest, a subsidiary of Alphabet, developed an intelligent thermostat that can automatically reduce energy consumption by automatically adapting to user behavior. Once Nest is installed in the user's home, it will begin to learn the habits of the occupants and adjust the temperature accordingly. According to Nest, the company’s technology has saved its users 10% to 12% of heating costs.

Case 2: Nnergix

Nnergix, Spain, uses machine learning techniques to predict the effects of atmospheric and weather conditions on renewable energy production, such as calculating the hourly output of photovoltaic power plants.

Case 3: Google Sunroof (Google Skylight)

Google released a tool called Sunroof to calculate the impact of solar energy on American families. The project uses several factors to calculate how much money can be saved using solar energy. These factors include weather data, electricity bills, 3D modeling, and shadow calculations.