The Convergence of AI & Energy
By: Alison Salerno
Tags: Artificial Intelligence, Machine Learning, Renewable Energy, Data Science, Energy

A renewable energy love story

Almost three years ago, Bill Gates wrote an open letter to the graduating class of 2017 and it’s still very relevant today. He offered advice for those looking to make a big impact in the world, and three fields they should consider: artificial intelligence, energy and biosciences.

“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. The third is the biosciences, which are ripe with opportunities to help people live longer, healthier lives.”

Most people would agree that artificial intelligence and energy (especially renewable energy) are two of the most significant fields today. Both are vital factors in defining what the future looks like. They are each influential on their own and especially when used in tandem.

The convergence of the two is really exciting to me, particularly as I am studying data science and have experience in the renewable energy industry.

So, where do the two meet? Grid management, by the way of renewable energy forecasting and the Smart Grid.

Renewable Energy Forecasting

Machine learning is being deployed to predict weather, renewable energy production and electricity demand. Renewable sources such as solar and wind can be incredibly variable. This poses a major risk to the power grid and grid management. If the power grid’s supply and demand isn’t in perfect sync, system failures can arise. These are costly to the utility companies as well as to the consumers.

Being able to forecast how much wind and solar energy to expect at a given time means that supply and demand can be accurately matched. Historical data sets as well as data collected from wind or solar panels are used to train machine learning algorithms to effectively forecast weather and power shifts. IBM’s Watson, Google’s DeepMind and Xcel Energy have all created machine learning algorithms to forecast weather and renewable energy output.

“Significant accuracy improvements can be achieved if we combine the individual model forecasts using a machine-learning based approach.” — IBM Watson

The Smart Grid

Graphic credit: IEEE

Smart grids are power grids which combine the power of AI and big data to create a digital power grid that facilitates a two-way communication between consumers and utility companies.

With renewable energy usage and generation taking a front seat, the need for a power system that can adequately manage and accommodate this diversification of energy sources is imperative. Renewable energy is the fastest-growing energy source in the US, increasing 100% from 2000–2018.

With more and more private users and private companies generating and using energy from renewable sources, our old system falls short. There are now instances when a private user generates energy beyond their needs and that excess energy is sent back to the grid.

There are also instances when demand outpaces supply (perhaps on a stormy day) and backup fossil-fuel sources need to be activated and utilized. This is where the role of Smart Grids comes into play — using AI to efficiently manage that supply and demand of energy.

AI will be used to automatically collect and process overwhelming amounts of data to make timely decisions on how to best allocate energy resources. The Smart Grid will “move the energy industry into a new era of reliability, availability, and efficiency that will contribute to our economic and environmental health.”

Smart Grid benefits:

Efficient transmission of electricity

Quicker restoration of electricity after power outages

Reduced operations and management costs for utilities, and ultimately lower energy costs for customers

Reduced peak demand, which also helps to lower electricity rates faced by consumers

Increased integration of large-scale renewable energy systems

Improved security

Concluding Thoughts

When paired with a Smart Grid, improving the predictive capabilities of renewable energy generation and usage will radically change the energy industry as we know it.

Of course, there will be challenges to grapple with along the way, including the threat of cyber attacks on an automated power grid, which must also be addressed. I am excited to see the role machine learning continues to play in shaping the renewable energy industry and I’m inspired in my data science studies by all the strides taken thus far.