AI Is Key to Our Energy Future

Renewable energy is our future. But renewable energy sources beyond a fast-moving river or centuries-old geothermal reservoir can be unpredictable at best. After all, some days are decidedly windier than others. And, even in Africa and the Middle East, the sun shines only half the time.

Add to this the heavy daily and seasonal shifts in energy use – from summer cooling and winter heating to ongoing fluctuations in electricity demand related to national and regional economies, customs and expectations – and we are left with a challenging distribution system.

Certainly, the data is there to allow us to take the guesswork out of responsible energy management. Ongoing energy usage trends can be blended with weather forecasts, birth and employment rates, migration and traffic patterns, and even cultural and holiday event schedules to create a realistically accurate energy delivery plan. And artificial intelligence, specifically machine learning, can help make power grids smarter and considerably more reliable.

Science and academia also are working to advance the cause. The U.S. Department of Energy SLAC National Accelerator Laboratory, operated by Stanford University, is testing the Grid Resilience and Intelligence Project (GRIP) to help the power grid manage power fluctuations, resist damage and recover faster from storms, solar eclipses, cyberattacks and other potentially catastrophic disruptions.

These AI-fueled models can have a very tangible impact on daily power delivery. For example, when long-term weather forecasts predict a major storm will hit the U.S. East Coast, potentially affecting not only power demand and availability but the generation of specific renewable sources like solar and wind, this intelligence can arm energy managers with actionable probabilities of grid damage and disruption to allow them to take preventative action. 

Beyond these short-term solutions, which include stockpiling certain renewables and rerouting the power path around vulnerable distribution points, is the promise of an automated energy delivery system accounting for all potential real-world scenarios.

The importance of machine learning to the energy sector can’t be underestimated, especially as the production of solar and other renewables becomes more widely distributed. Our energy future depends on a data-driven power grid configured for both maximum daily efficiency and minimal disruptions in times of emergency. 

Artificial intelligence will make this happen, and CSDR International is helping to lead the way.

Allie Collins