The Multiplying Power of AI in Energy Technology
Since the launch of ChatGPT-4 earlier this year, artificial intelligence has very quickly emerged as a publicly-transfixing topic across social channels, industries, and business leaders.
The reality is that AI has been around for many years already, gaining momentum as computing power, language, infrastructure, programming, and use cases have all evolved. AI’s impact on energy technology investments in the United States is no exception in that evolution, reshaping the energy sector by enhancing efficiency, optimizing operations, and driving innovation in renewable energy sources. Likewise, within traditional carbon energy markets, AI has already delivered profound impact, using predictive modeling to increase operational safety and efficiency across the value chain.
AI Accelerates Carbon Operation Optimization
The American Mid-continent has long been a hub for fossil fuel exploration and extraction. Think of the Permian Basin, the highest producing oil field in the US — or the Hugoton gas area — one of the largest in the US spanning Oklahoma and parts of both Texas and Kansas.
These areas, which still today produce massive quantities of energy for the US, have undergone numerous cycles of technology evolution in the modern energy era. Today, AI integration is rapidly revolutionizing operations along the stream, powering algorithms that optimize drilling processes to predict equipment failures, improve reservoir management, and protect human workers through tech-assisted fixes in-field. Real-time data analysis has also helped reduce operational downtime, optimize production levels, and even minimize environmental impact through faster recognition of problem areas along development and distribution infrastructure.
AI contributions to predictive maintenance are particularly interesting considering the thousands of miles of aging infrastructure across the US. This safety use case reduces accident and spillage risk, mitigating both human and ecological disaster. This type of human-assisted automation also plays an important role in managing energy consumption within oil and gas operations, delivering cost savings across the operational value chain.
For Cortado Ventures in OKC, investing in Senslytics was a deliberate stake in AI energy infrastructure safety. The Senslytic model meshes precision large language models (LLMs) with domain expertise to deliver causation-based, early warning signals on a range of carbon client use cases, such as line corrosion prevention and contamination warning.
The Smart Grid Gets a PhD
Did you know: US electricity demand is expected to increase 25% by 2050.¹
It is no longer a question but a certainty: AI is revolutionizing smart grid management in both traditional and alternative energy markets. AI-powered algorithms can analyze real-time data from smart meters, sensors, and other IoT grid components, enabling utility providers to optimize energy distribution using precise data whether on a gas pipeline or a wind farm.
Mckinsey & Company believes AI-enabled smart grid management can “increase asset productivity by up to 20% while reducing maintenance costs by 10%.” At scale, this represents potential billions in cost economies of scale, increasing adoption at commercial and consumer levels.
A prime example of this evolving tech is AutoGrid, a California-based company that offers a flexible and scalable energy management platform. AutoGrid’s AI-driven solutions help utilities predict demand patterns, identify potential grid issues, and implement demand-response programs, resulting in improved grid stability and reduced energy wastage. Both of these smart grid “stability” improvements create downstream commercial and consumer demand as the technology becomes more reliable — and more price-competitive for consumers in particular — resulting in increased probability of clean energy scale.
Renewable Energy Algorithms Create Security
AI is also changing the field of renewable energy sources, where a major challenge for solar and wind is intermittent origin access. In other words: sun and wind aren’t always on! AI algorithms, however, can forecast weather conditions, cross-use case demand fluctuations, and most importantly energy supply, allowing for better integration of renewables into regional and national grids, and much more efficient capture/storage of renewable energy.
A now well-known example: Google’s DeepMind has developed AI technology that optimizes the energy output of wind farms, a growing component of renewable energy in the American Midwest, particularly in state leaders like Texas and Oklahoma. By analyzing vast amounts of data, DeepMind can increase the overall efficiency of wind farms across the grid, leading to a more stable and reliable supply of clean energy, which encourages downstream consumption and investment.
Startups and Corporates Alike Bet Big on AI-Driven Clean Energy
AI startups like Grid AI have seen substantial growth in recent years, attracting significant funding for its platform to streamline and accelerate machine learning workflows, which are critical in ever more efficient modeling and use case execution. Energy companies are leveraging Grid AI’s technology to enhance the performance of renewable energy systems, reduce maintenance costs, and optimize power generation…all necessary components of a “circular” energy system that renews and distributes across supply, demand, and geography seamlessly.
Even traditional energy companies are incorporating AI to optimize operations, reduce costs, and minimize environmental impacts. ExxonMobil, one of the world’s largest oil and gas companies, has invested heavily in AI technologies for its fossil-fuel business. EM utilizes AI-driven analytics to improve drilling efficiency, reduce downtime, and enhance safety measures, making the overall process more sustainable and economically viable. And one adjacent benefit: more efficient operations lead to more divertable capital EM can use for future energy infrastructure like renewables.
AI-Efficient Buildings and Batteries
AI is also revolutionizing energy efficiency in buildings. Building management systems equipped with AI algorithms can intelligently control heating, ventilation, and lighting, based on real-time occupancy and weather data. This results in substantial energy savings and reduced carbon footprints. A notable example is the company Carbon Lighthouse, which employs AI to optimize energy usage in commercial buildings, delivering substantial cost savings and emissions reductions.
On the battery side of efficiency, AI is facilitating advancements in storage technology, a critical component for renewable energy integration. What good is a smart grid that cannot safekeep supply? Companies like Tesla are incorporating AI into battery management systems to extend battery life, improve charging efficiency, and optimize energy storage capacity. These innovations are crucial for scaling renewable energy deployment and mitigating challenges associated with intermittent and unreliable energy supply.
In conclusion, the impact of AI on energy technology investments in the United States is undeniable. From carbon infrastructure safety to smart grid management to renewable energy integration, AI-powered solutions are transforming the energy sector and attracting significant investments. As AI continues to evolve, it holds the potential to drive even more profound changes, making the energy sector safer, greener, more efficient, and sustainable for the future.
