In light of the ongoing COVID-19 crisis, the energy industry saw surplus supply and a change in usage patterns in 2020, to which it had to adapt painfully. Utility crews had to deal with a tedious hurricane season, with socially distant, limited staff. Artificial intelligence (AI) could have played a role in prioritizing essential tasks for more satisfied employees. AI-driven vegetation management can reduce unplanned power outages, and reduce millions of dollars in operations and maintenance costs, while bolstering customer satisfaction. 

Given the widespread changes in our grid infrastructure, databases will not only fail to extract value from the 21st-century data streams, but they might fail to function. The industry has seen record numbers of sensors, solar panels, electric cars, stronger ransom, Internet of Things, and advanced metering infrastructure data floods through the 5G networks to the utilities.

The number of parallel and orthogonal advances—whether it is in computing, data processing, or sensors—is hard to keep up with, especially if one is an energy professional, which itself is very demanding. The simultaneous profusion of new technologies makes us wonder which to choose appropriately, a sort of buyer’s dilemma. 

Thus, there is a need to streamline the AI applications for energy industry users. Such focused AI for energy professionals can be called Energy AI. The complex development and operations (DevOps), and strenuous data science, can be automated and tucked behind the scenes. A user from the energy domain can use AI without becoming a cloud-computing cybersecurity expert while losing sight of why she or he began working on the cloud-computing project in the first place.

Why Widespread AI Adoption Is Taking Longer Than Anticipated

The lack of explainability is one thing holding back widespread AI adoption in heavily regulated industries, such as the energy industry. Regulators and lawmakers need to know what’s inside the black box because they are the ones liable for every loss, answerable for any adversity. As human decision-makers, we all prefer complete, understandable, unambiguous information, and develop rationale and reasoning around it.

The power industry is a universe of its own. It is an entanglement of precision systems, lock-stepped and synchronized to serve the rest of the world. It has taken centuries of human enterprise and genius to build the energy industry of today. It is robust and reliable, though very sensitive to perturbations. AI’s lack of transparency on how it works scares off practitioners in the power industry. Also, it means that AI will not function within the enormous diversity of contexts within which energy professionals work. There needs to be an AI solution tailored to every role and responsibility of an energy industry worker.

AI, however, is fundamentally only accurate as its training data sources, scenarios, and use-cases are extracted. Thus, models trained to work in the Arctic cold deserts will not fly in the sub-Saharan energy networks. Human data-labelers’ pre-existing biases, their experiences (or limitations thereof) can seep into the training process. Data might be over-sampled or under-sampled. The natural language processing that transcribes meeting notes or translates Mandarin to Swahili does not parse “CAN not working—stop production now,” where CAN stands for control area network in the car manufacturing industry. Nor does it understand that a line worker wants specific types of cable configurations when she or he requests “Osprey” and “Dove.”

Another major bottleneck in AI’s integration with the energy industry is unclear boundaries between automation and artificial intelligence. Many industry workers—especially workers in more manual and physical work roles—fear that robots, with their mechanical arms, AI-minds, above-human capabilities, and of course, unsolvability—will take billions of jobs away. Unions harbor their fears and slow down the adoption of new processes. There needs to be a shift in the mindset—that AI is an empowering tool, like a car or a pen, that can uplift humans by being an extension of their brain. It must also be highlighted that AI and all its ever-evolving data-labeling tasks has created more manual jobs in the computer industry instead of eliminating them.

Implementation of Digital Solutions Are Instrumental

Hurricane Ivan hit the Southeast in 2004. In the state of Alabama alone, 800,000 customers lost power for more than two weeks. The following year, Hurricane Katrina left 550,000 customers in Alabama in the darkness for a week and a half. In 2020, Hurricane Sally affected 300,000 customers, but only for three days. What made the difference between Katrina and Sally in decreasing the customer outage time from weeks to days? The power company went fully digital, making the before-, during-, and after-the-storm preparations more efficient, and most importantly, providing a quicker turnover rate.

Energy AI is artificial intelligence demystified, attenuated to energy-related problems, and accessible for power and energy professionals. Every business that is dependent on energy can use Energy AI confidently to prognosticate power interruptions, achieve peak productivity as individuals and teams, and in general, improve continuity of power supply to all downstream customers and business processes.

AI does not have extraterrestrial origins. On the blue marble, AI was born here to solve real-world challenges that limit human abilities—whether personal or professional. It is now upon us, across several professions, to look beyond AI’s silver bullet expectations and think beyond insurmountable computer science chops; one needs to leverage AI.

How the energy industry adopts AI today will shape human lives for the next hundred years. Just as steam engines led to power plants in the James Watt-era, who knows if silly Snapchat AI filters will one day correctly predict a power outage months in advance, helping a hospital avert a backup power crisis and save a life.

Sayonsom Chanda, PhD is CEO of Sync Energy AI. Sync Energy AI is an analytics software suite that can be customized for different engineering or decision-making roles within electric utilities.