What Trends Are Shaping AI In Energy This Year? 9 Experts Share Their Insights

  • 19 September 2019
  • Sam Mire

Though there's a broad trend in the energy sector towards renewables, that's just a single broad brush stroke within the bigger pictures. What other trends are shaping the future of energy extraction, refinement, and consumption. How will these trends impact our daily lives?

These industry insiders provided their takes on the #1 trend shaping energy this year, and into the future.

1. Shuli Goodman, Executive Director of LF Energy

“Some areas where we see nascent AI is in predictive maintenance and asset monitoring. There are a few who are beginning to look at utilizing AI to analyze images from drones for surveillance and also for acoustic listening.”

2. Larsh Johnson, CTO of Stem 

“Advances in the ‘time-series' AI world (as opposed to AI for images or audio) are shaping the energy industry today. These include techniques for time series forecasting, anomaly detection, optimization etc. Specifically, probabilistic techniques and algorithms are showing significant improvements and becoming the driver of the next wave of optimization and value creation. These techniques augment today's unilateral AI predictions with additional information about the confidence in these predictions. This is not unlike the trend shaping the peer to peer transportation industry.”

3. Mark Chung, co-founder and CEO of Verdigris

Mark Chung“Predictive analytics is perhaps the most important trend.  I think GAMs and maturing time-series forecasting like Facebook Prophet enable more powerful predictions that will displace standard statistical approaches. Because energy is also money, predicting energy is similar to the kinds of trading opportunities you see in other real-time markets.

The macro-transformation towards electricity also increases the complexity and a natural fit between information, communication tech and energy.   Energy and electrification is going to create complex datasets requiring increasing predictive analytics.”

4. Jason Kram, Executive Vice President at Adapt2 Solutions

“Our energy portfolio is diversifying and by applying AI to the massive amounts of available market data, we are driving the development of new features, functions, and capabilities that will become the standard (if not mandatory) in the next 2-3 years. Whether automating back-office billing or educating front office bids, AI is digitally transforming the energy industry as we see new sources of energy including wind and ocean.”

5. Jeff McGehee, Director of Engineering at Very

“AI is starting to make its mark in optimal control, predictive maintenance, and generative mechanical design. However, the previously mentioned talent shortage means it will be a while before these practical applications of AI become ubiquitous across the industry.

Digital twin technology is emerging as a new practical application of AI. In the energy industry, where so much revenue and capital is tied to physical assets, it’s extremely valuable to deploy technologies that can provide insight into the state and behavior of these assets. Energy companies are interested in leveraging digital twins for predictive maintenance, identifying future design enhancements, and modeling the behavior of large unpredictable systems.”

6. Greg Slater, General Manager at Flutura Decision Sciences and Analytics

“The number one AI trend in energy right now is the in-house understanding that enterprises must seek out and engage with specific AI providers that will provide business benefit. Today, companies have a clearer focus on their end goal and what they want to achieve.  They have a vision as to what they want from technologies like AI to achieve, and have specific internal goals and objectives.

Initially, AI platforms were used for very small and simple use cases (i.e. asset monitoring) but we are now seeing very complex use cases (i.e. entire process optimization), because the value of AI is now better understood. The industry appreciates the promise of technology that has the purpose of solving larger problems and we are seeing much larger and complex problems.  Companies now have trust in AI and are expanding on their use cases.”

7. Edwin Chen, AI Solution Consultant at Dynam

“The prevalence of using IoT (Industrial IoT) and cloud-based data processing technologies are transforming energy companies’ operations by connectivity across entire assets and generating a tremendous amount of time series data for analysis.  As a result of this plethora of data, AI machine learning solutions are developed to process, interpret and recommend actionable insights and actions in a fraction of the time previously taken with human operators.”

8. Binu Parthan, Principal Consultant at Sustainable Energy Associates

“I believe with a number of high-profile energy blackouts around the world and suspected cyber attacks which were profiled prominently in the media, the current trend is about enhancing cybersecurity of energy systems using AI. There are a number if efforts on currently in using AI to identify and repel cybersecurity threats to energy infrastructure.”


9. Morgen Henderson, Journalist at Solar Power Authority

“One of the biggest trends over the last few years, not just 2019 alone, is Intelligent Energy Storage (also known as Smart Storage). AI manages energy from various sources and makes data-based decisions on how to store and use energy. This can help conserve energy in many cases and efficiently distribute energy when and where it's needed.”

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About Sam Mire

Sam is a Market Research Analyst at Disruptor Daily. He's a trained journalist with experience in the field of disruptive technology. He’s versed in the impact that blockchain technology is having on industries of today, from healthcare to cannabis. He’s written extensively on the individuals and companies shaping the future of tech, working directly with many of them to advance their vision. Sam is known for writing work that brings value to industry professionals and the generally curious – as well as an occasional smile to the face.