Edge Computing Fuels a Sustainable Future for Energy

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Each day, energy flows throughout our lives – from the fuel that powers cars and planes, to the gas used for stove top cooking, to the electricity that keeps the lights on in homes and businesses. Oil, gas, and electricity are mature commodity markets, but AI is transforming the processes used to produce, transport, and deliver these resources. 

Enter AI deployed at the edge: on oil rigs, within power plants, riding along utility trucks, even embedded in smart buildings. Oil and gas enterprises and utilities are using AI and edge computing to improve operational efficiency, protect worker health and safety, integrate renewable energy, increase grid resiliency, and provide more reliable and affordable sources of energy to consumers.

Image of smart camera pilot deployment from Noteworthy AI used by FirstEnergy to automate inspections of utility poles at the edge.
Figure 1. Noteworthy AI, a member of NVIDIA Inception, put smart cameras on FirstEnergy’s trucks in a pilot that showed how edge computing can monitor millions of pole-mounted assets. Image courtesy of Noteworthy AI.

As companies and countries race to decarbonize and meet net-zero emissions goals, edge AI will play a key role managing distributed energy resources such as electric vehicles, home batteries, solar panels, and wind farms to enhance power grid resiliency and accelerate the energy transition. The following examples highlight the top AI use cases across the energy industry, including:

  • Software-defined smart grids: Future smart meters will use edge computing to optimize power flow, detect grid anomalies, deliver more reliable energy at a lower cost, and unlock opportunities for new energy applications. Utilidata, a leading grid-edge software company, is developing a software-defined smart grid chip with NVIDIA that will power next-generation smart meters to increase grid resiliency, decarbonization, and consumer value. 
  • Autonomous operations: Industrial sites, such as oil rigs and power plants, require extensive monitoring for efficiency and safety because liquid, steam, or oil leakages can be catastrophic, costly, and wasteful. Global energy leaders, such as Siemens Energy, are using AI and machine learning to deliver a path to autonomous power plants. The company trains AI models using thousands of images and video streams from millions of onsite cameras and sensors to detect process anomalies. These models are deployed at the edge in power plants and use real-time inferencing to identify leaks. Rig operators are using computer vision, deep learning, and intelligent video analytics (IVA) to monitor heavy machinery, detect potential hazards, and alert workers in real-time to protect their health and safety, prevent accidents, and assign repair technicians for maintenance. 
  • Pipeline optimization: Oil and gas enterprises rely on finding the best-fit routes to transfer oil to refineries and eventually fuel stations. Edge AI can calculate the optimal flow of oil to ensure reliability of production and protect long-term pipeline health. Using IVA, these companies can inspect pipelines for defects that could lead to dangerous failures and automatically alert pipeline operators. Further downstream, NVIDIA ReOpt uses GPU-accelerated solvers for logistics and route optimization, which can efficiently route fuel to fueling stations.
  • Power grid maintenance: With proactive maintenance, utilities can accurately detect defects and reduce unplanned outages to better serve customers. FirstEnergy worked with Noteworthy AI, a NVIDIA Inception member, on a pilot project to automate utility pole inspections. Fixed camera systems powered by NVIDIA Jetson were secured to the roof of service trucks and collected standardized, high-resolution images of their utility poles, power lines, and pole-mounted assets. The images were analyzed at the edge to determine if repairs or vegetation management was needed. Edge computing can help monitor the estimated 185 million utility poles in the United States, and reduce the tens of millions of dollars spent each year by utilities to manually track and maintain poles. 
  • Power grid simulation: Intelligent forecasting using GPU-accelerated grid simulations combined with historical data on energy usage and weather can inform more efficient generation, distribution, and management of energy resources to consumers. AI helps manage the bidirectional flow of power in a grid, delivering reliable energy to residents and enterprises while automating the process for consumers to sell their additional energy back to the grid.

Thanks to edge AI, the future of energy is more sustainable than ever. Explore how NVIDIA is building an ecosystem to accelerate the energy transition.