How AI will transform the energy sector – Sifted

Back in 2017, Bill Gates co-founder of Microsoft and an avid philanthropist penned a short blog post in the form of a commencement address for that years graduating cohorts. He outlined three fields that he thought graduates should apply themselves to if they truly wished to have an outsized impact on the future of civilization. The first two picks were artificial intelligence (AI) and energy (ahead of biosciences).

Interestingly, AI and energy are far more interrelated than people appreciate. AI is poised to transform the entire energy sector in the coming years, by helping overcome energys inherently variable and uncertain nature, and by accelerating the adoption of renewables.

One need only look at the power outages that crippled much of Texas in February of this year attributed in part to freezing temperatures to see that our electricity infrastructure is failing us, not to mention the uptick in extreme weather events is revealing the shortcomings of our current climate change strategy.

AI broadly speaking the use of modern computing power to perform tasks that have traditionally required human intelligence is the true enabler of Industry 4.0. It will allow physical industrial assets to be interconnected and communicating with each other through the flow of vast amounts of data in real-time.

There are two main areas in which the implementation of AI methods can substantially improve the effectiveness of current solutions in the energy sector, and support faster integration of renewable energy sources:

Machine learning (ML) algorithms can identify patterns and insights within large data sets, and predict outcomes given certain data inputs. This would allow energy companies to:

The next step is capturing the output of all these predictions and acting accordingly, independent of human guidance.

The holy grail is achieving the full autonomy of energy systems.

Substantial advances in ML algorithms are opening possibilities beyond the mere automation of decisions based on the improved recommendations of AI-based models. The holy grail in the energy industry is achieving the full autonomy of energy systems particularly of power grids, some of the most complex mechanised systems in the world.

They are becoming even more challenging to operate due to the advent of distributed energy assets (e.g., personal photovoltaic panels) and the rise of the prosumer, which are shifting supply and demand dynamics and turning the traditional energy value chain upside down.

AI-based deep learning models have the potential to automate the optimisation process of energy grids by analysing heaps of historic and real-time data, acting independently upon the output, and using feedback loops to self-learn and become even more accurate. This could be key to reducing grid congestion, integrating intermittent renewable energy sources, and enabling quick recovery in the wake of natural disasters.

During the Texas winter storms, AI-based models could have prepared for the subsequent outages.

For example, during the Texas winter storms, AI-based models could have predicted and prepared for the subsequent outages, autonomously triggering alternative energy source generators and swiftly dispatching the power to neighbourhoods that needed it the most.

GreenCom Networks a leading provider of white-label solutions for distributed energy management focuses on extending autonomous capabilities within energy systems. Its platform can independently optimise decentralised energy generation and consumption, and reduce overall grid congestion.

We know for a fact that AI can accelerate our shift towards renewable energies, but the road there is not clear of obstacles. First, these technologies are likely to face initial mistrust from sceptical consumers both at the organization and individual level due to their inherent black-box nature.

It is difficult to find entrepreneurs with the required expertise.

Second, there is currently a lack of in-depth knowledge of AI, given that it is still a relatively new technology. Today, it is strikingly difficult to find entrepreneurs with the required expertise to build holistic AI-powered software solutions that have real practical value to the energy industry.

Cyber-attack vulnerability and fear of critical infrastructure decentralisation will be challenges.

Third and most importantly, challenges will crop up on the regulatory side cyber-attack vulnerability and fear of critical infrastructure decentralisation are set to be the main culprits. Just last February, the European Commission released a whitepaper calling for the regulation of AI in the energy sector flagging its high-risk status, as well as inherent data security and governance issues.

Overall, the next few years are expected to witness an explosion in the number of new use-cases and areas for the application of AI models within the energy space. As costs plummet across all types of renewables, energy companies will hunt for technologies that can provide sustained competitive advantages over rivals. Interestingly, it is possible that the biggest opportunities will arise from developing countries where underlying infrastructure may not yet be built, and the lack of entrenched industry incumbents could help drive a relatively higher rate of adoption.

Aaron Israel is an investment analyst at Future Energy Ventures.

Bidgely Inc., Jungle.ai, eSmart Systems and GreenCom Networks are portfolio companies of Future Energy Ventures.

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How AI will transform the energy sector - Sifted