Artificial intelligence is about to trigger explosive changes in our lives, work, and leisure, but few understand what the technology can do beyond Amazon

AMZN
’s Alexa or Apple

AAPL
’s Siri. These are examples of virtual assistant or ‘weak AI’ technology — the most common example of AI application. But in the data-driven energy sector, sophisticated machine learning is paving the way for ‘strong AI’ to improve efficiency, forecasting, trading, and user accessibility.

Electricity Trading

Electricity is a commodity that can be bought, sold, and traded in open markets. For these markets to function efficiently, massive amounts of data — from weather forecasting to grid demand/supply balance — must be constantly analyzed by power sellers, buyers, and brokers. Those best positioned to understand the data have a competitive advantage in the marketplace.

In 2018, IBM

IBM
’s DeepMind began applying machine learning algorithms to 700 MW of Google

GOOGL
’s wind power capacity in central US which is enough to power a medium-sized city. Utilizing a neural network that tapped into weather forecasts and historical turbine data, it could reasonably predict wind power output 36 hours in advance. In less than one year, DeepMind’s machine learning algorithms increased the value of their wind energy by roughly 20%, compared to baseline scenarios.

Intelligent Power Consumption

Nearly half of power users in the United States have electrical smart meters, providing data about personal energy consumption to enable informed consumer self-regulation of energy usage. New AI-fueled smart meters and smart home solutions are not yet widespread but represent a potential boon to end-user efficiency gains. These energy monitoring devices communicate with other household devices, saving owners money by reducing energy waste. Such examples would be these devices controlling air conditioning, advising the charging of electric cars during hours with lower electric costs, controlling lighting, and managing appliances.

With the ability to adapt and react to usage patterns and energy prices, these devices could represent massive energy savings  if applied to the broader population. Widespread implementation could contribute to a greener, more stable electric grid for everyone.

Intelligent Energy Storage

Artificial intelligence can improve existing energy storage technology by making it easier to integrate distinct technologies, including renewable-powered microgrids, utility-scale battery storage, pumped hydro, and more. The role of energy storage in modern grids is growing rapidly along with the proliferation of intermittent power sources like wind and solar which put a higher strain on power brokers to balance supply and demand. As the technology improves and costs come down, intelligent energy storage is playing larger role in the grid’s ancillary services – functions that help grid operators balance and support the transmission of energy from generators to consumers.

In times of demand/supply gaps, AI can allow for more efficient allocation and in doing so save otherwise wasted power for later use. Not only does the integration of multiple distinct storage systems maximize the bang for one’s buck, intelligent energy storage systems further enhance safety and security by improving the frequency and voltage control caused by intermittent power generation. The Berlin-based energy storage firm Younicos has been a global market leader in the development and deployment of such integrated energy systems since 2005.

Robots

For the electricity sector, one notable application of AI-technology is the creation of autonomous robots which can replace humans in otherwise dangerous situations. These self-driving unmanned machines can survey high-voltage power lines on land or patrol the seafloor for valuable  resources, logging and reporting their locations future extraction without risking human lives.

ExxonMobil

XOM
’s two-way collaboration with MIT through the MIT Energy Initiative is one such project intended to further develop the ability of autonomous robots to conduct complex tasks independently. MIT Professor Brian Williams and his team intend for their self-learning robots to mirror the Mars Curiosity Rover and open the seafloor for further exploration and utilization. Geoscientist Lori Summa, ExxonMobil’s former primary consultant on the MIT submersible robot project, regarded the innovations there as essential to “push the envelope of energy research to meet the challenges of the future.”

The Future of Energy

With the global energy system not immune to the COVID-19 pandemic, renewed emphasis has been placed on upping economic efficiency. To that end, market players are using machine learning to improve predicative capabilities, increase transparency in energy trading, integrate renewable energy sources, manage smart grids and storage, and give life to unmanned drones.

The convergence of strong AI and the energy sector will have dramatic and sweeping impacts for global consumers. As Bill Gates said to those graduating around the world in 2017:

“If I were starting out today and looking for the same kind of opportunity to make a big impact in the world, I would consider these fields. One is artificial intelligence. We have only begun to tap into all the ways it will make people’s lives more productive and creative. The second is energy, because making it clean, affordable, and reliable will be essential for fighting poverty and climate change.”

With Assistance from Regan Abner