PG&E Adopts Machine Learning for Wildfire Prevention
Machine learning for wildfire prevention is at the forefront of Pacific Gas and Electric Co.’s (PG&E) latest efforts to increase safety and reliability across its vast electrical network. With the threat of wildfires looming larger each year in California, PG&E has taken a proactive approach by launching a state-of-the-art Continuous Monitoring Center in San Ramon. This initiative leverages advanced technology to identify and address potential hazards before they escalate into power outages or devastating wildfires.
Continuous Monitoring Center: A New Era in Grid Safety
The Continuous Monitoring Center is the heart of PG&E’s new wildfire mitigation strategy. By collecting data from tens of thousands of sensors installed throughout its service region, as well as from 5.5 million smart meters, the utility is able to monitor the electrical grid in real time. This massive influx of data is processed using sophisticated machine learning for wildfire prevention algorithms, which are trained to detect abnormalities that could signal an emerging issue.
According to PG&E spokesperson Paul Moreno, these tools enable experts to dispatch repair crews rapidly, often before customers are even aware of a problem. The system’s ability to predict potential ignitions and outages marks a significant shift from the traditional, reactive approach of responding to emergencies after they occur.
Proven Results: Reducing Outages and Saving Costs
PG&E’s machine learning-driven system has already demonstrated tangible benefits in its first year of operation. In 2025 alone, the monitoring center intercepted 17 potential wildfire ignitions in areas at high risk. This proactive intervention helped the utility avoid 12 million minutes of unplanned customer outages, reduce emergency response time by 2,620 hours, and save approximately $6 million in operational costs.
By integrating technologies such as early fault detection sensors, GridScope devices, downed conductor detection, and SmartDetect—each enhanced by machine learning—the center can rapidly pinpoint and address hazards. This machine learning for wildfire prevention approach ensures that issues are caught earlier and fixed more precisely, ultimately improving safety for both customers and communities.
Key Technologies at Work
The Continuous Monitoring Center draws on a suite of advanced technologies. Early fault detection sensors and GridScope devices monitor for abnormal activity, while downed conductor detection tools help quickly identify fallen power lines. The SmartDetect system uses machine learning for wildfire prevention to analyze grid performance in real time, identifying subtle signs of trouble that could otherwise go unnoticed.
Additionally, distribution fault anticipation sensors and line sensors feed even more data into the grid’s analytics platform. This multi-layered approach allows PG&E engineers to maintain a comprehensive view of the electrical system’s health at all times.
From Reactive to Proactive: Real-World Impact
One notable example of this new approach occurred on the Brunswick 1106 circuit in Nevada County. The machine learning system detected a wiring anomaly, prompting a crew to investigate. Upon inspection, they found melted insulation on a transformer—damage caused by severe weather. Because the issue was caught early, crews were able to replace the transformer and related equipment before a fire could start. Since 2025, the center has identified 1,484 similar potentially dangerous situations, preventing untold damage and service disruptions.
Part of a Broader Wildfire Mitigation Effort
The Continuous Monitoring Center is just one element of PG&E’s comprehensive wildfire mitigation plan. Other initiatives include undergrounding power lines, enhanced safety settings for powerlines, targeted Public Safety Power Shutoffs, and the deployment of AI-enabled wildfire cameras. Together, these strategies are transforming how the utility manages risk during California’s wildfire season.
Looking Ahead: A Safer, Smarter Grid
As wildfire seasons grow longer and more intense, the role of machine learning for wildfire prevention will only become more critical. PG&E’s investment in these technologies demonstrates a commitment to not only keeping the lights on but also safeguarding lives and property. By moving from reactive crisis management to proactive risk reduction, PG&E is setting new standards for utility safety and reliability in the age of climate-driven threats.
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