Fighting fire with data: AI and ML's role in utility wildfire prevention

Rising temperatures, drier conditions, and the spread of urban areas into fire-prone regions are forming a dangerous combination for wildfire surges. In 2023, the National Interagency Fire Center (NIFC) reported a staggering 56,580 wildfires that scorched over 2.6 million acres across the United States. Wildfires have become a relentless force of destruction, capable of incinerating entire communities with alarming speed and intensity. The devastating camp fire in California, which caused over $16.5 billion in damages, is a stark reminder of the havoc wildfires can wreak. These infernos not only annihilate homes and critical infrastructure but also degrade air quality, disrupt ecosystems, and impose staggering economic costs.

At a global scale, utilities are adopting innovative strategies through artificial intelligence (AI) and machine learning (ML) solutions to prevent and mitigate wildfire damage. By exploring wildfire mitigation use cases, we illustrate the breadth of potential with these technologies and propose considerations to help utilities evaluate and select the most suitable AI/ML solutions.

Jump to Section:

• Analytics for fire risk assessment 


• Public safety power shutoffs 


• Vegetation management 


• Fault detection and prevention 


• Smart Grid Management


• Emergency response and decision support

Analytics for fire risk assessment

Fire risk modeling, analysis, and assessment

Remarkable analysis of immense geospatial data through AI technologies are revolutionizing fire risk modeling, predicting high-risk areas, and identifying patterns and correlations that traditional methods overlook. Advanced supervised and unsupervised learning algorithms integrate data from sensors and real-time sources such as temperature, humidity, wind speed, smoke levels, and vegetation density to generate detailed risk maps—helping utilities prioritize preventive measures and resource allocation. 

Additionally, AI-enhanced weather forecasting provides hyperlocal predictions by leveraging ML algorithms to produce precise forecasts for specific areas. Integrating historical and real-time data offers accurate fire weather predictions, enabling utilities to anticipate and prepare for conditions that could elevate fire risks, informing smarter decisions and strategies to effectively mitigate wildfire threats.

Public safety power shutoffs

Optimized shutdown planning

Public Safety Power Shutoffs (PSPS) are preemptive measures taken by utilities to reduce the risk of wildfires during extreme weather conditions. When there’s a high fire risk—such as during strong winds, low humidity, and dry vegetation—power companies may turn off power in high-risk areas to prevent electrical equipment from igniting fires. AI analysis can optimize the timing and extent of power shutoffs by analyzing weather conditions, fire risk data, and grid status to determine the most effective timeframes and areas for fire prevention. Managing PSPS is a sensitive operation, balancing safety with minimizing disruption to customers and economic costs while also abating regulatory fines for the utility. AI offers the potential to increase the value of PSPS through improved accuracy, minimized disruptions, enhanced safety, and operational efficiency. 

Vegetation management

Image analysis and predictive growth models

AI processes remote sensing, satellite, drone, and radar imagery to identify fire-risk vegetation. This is done by tracking growth and detecting vegetation clearance violations to conductors or other grid infrastructure components—enabling utilities to distinguish high-risk zones and prioritize vegetation management efforts.  Models generated through supervised learning algorithms offer predictions of vegetation growth over time, allowing utilities to schedule proactive maintenance activities before vegetation becomes a fire hazard. These predictive growth models consider various factors such as species type, climate conditions, and historical growth patterns. AI utilized for vegetation maintenance significantly enhances safety by reducing the need for truck rolls and extensive fieldwork. By automating the detection and monitoring of vegetation risks, worker exposure to hazardous conditions is minimized, keeping them out of harm's way and ensuring a safer working environment. 

Fault detection and prevention

Anomaly detection

Anomalies within the grid can indicate potential faults in utility infrastructure. Identifying these events is challenging because they often occur in remote areas, so by continuously monitoring data from sensors and equipment, AI-integrated systems can detect unusual patterns or deviations from normal operating conditions. For instance, an AI-powered anomaly detection system might identify a sudden spike in electrical resistance that could signal a failing component, enabling utilities to address the issue before it leads to a catastrophic failure or ignites a wildfire. 

Predictive maintenance

Accurately forecasting equipment failures by analyzing historical maintenance records and live performance data through AI models is a major logistics advantage. This allows utilities to proactively perform maintenance, reducing the risk of equipment-related fires, enhancing safety, extending infrastructure longevity, reducing operational costs, and optimizing procurement strategies. 

Smart grid management

Dynamic load balancing

AI optimizes electrical load distribution through dynamic load balancing, which helps prevent overheating and reduces the risk of fires. By continuously analyzing the demand and supply of electricity across the grid, AI systems can adjust the distribution of electrical loads in real time. This ensures that no single component is overloaded, thereby minimizing the risk of overheating and potential ignition sources.

Automated fault location, isolation, and service restoration (FLISR) 

AI-powered systems for automated FLISR enable quick fault identification and isolation. When a fault occurs, these systems can rapidly pinpoint the location, isolate the affected section, and reroute power to minimize service disruption. This swift response not only reduces downtime for customers but also mitigates the risk of faults escalating into larger fire hazards. 

Emergency response and decision support

Simulation and scenario planning

Utilities are leveraging AI simulations of wildfire scenarios for developing response strategies. AI creates detailed models of potential wildfire events to simulate various scenarios and outcomes, allowing emergency planners to evaluate different response strategies and optimize their plans based on simulated performance, ensuring that they are well-prepared for actual events.

Decision support systems

AI provides real-time recommendations during fire events through advanced decision support systems. These systems analyze live datasets and use AI algorithms to generate actionable insights and recommendations for emergency responders. This real-time support can significantly enhance the response effectiveness, containing fires more quickly and minimizing damage. 

Key considerations for utilities

To enhance infrastructure resiliency through AI, selection of the best solution requires a tailored approach. The decision-making process must account for impactful factors to ensure the chosen solution combines business value and feasibility, aligning operational goals, organizational capacity, and budgetary constraints.  

Data storage capacity

AI solutions, particularly those aimed at enhancing wildfire resiliency, demand robust data storage capabilities due to the syphoning and computing of vast amounts of data from various sources. Utilities must assess their current data infrastructure to determine if it can handle the influx of data required for the AI to function effectively or consider third-party cloud solutions.  

Questions to ask:

• Do we have sufficient data storage capacity (virtual and physical) to support the AI solution?
• Is our data infrastructure scalable to accommodate future growth?
• Are there any data security measures in place to protect sensitive information?
• What are our internal policies regarding on-prem vs. cloud solutions?
  

Operational & organizational impacts

Implementing an AI solution will invariably impact both operational processes and organizational structure. Utilities must evaluate how the integration of AI will affect day-to-day operations and whether the existing workforce is prepared for this technological shift.  

Questions to ask:

• How will the AI solution integrate with current operational workflows?
• What training or upskilling will be necessary for employees to effectively utilize the AI technology?
• Are there any changes required in organizational structure to support the AI implementation—such as a formal AI committee or advisory council?
• Do we have clear and effective process documentation?
  

Cost impacts: Build vs. buy

One of the most significant decisions with major security implications utilities face is whether to build a custom AI solution in-house or purchase an existing solution from a vendor. Each approach has its own set of cost associations:

• Building in-house: This option allows for a tailored solution that meets specific needs but requires substantial investment in development resources, time, and expertise. Utilities must consider the long-term maintenance and potential updates needed.
• Buying from a vendor: Purchasing an off-the-shelf solution can expedite deployment and leverage the vendor’s expertise. However, it may involve ongoing licensing fees and less flexibility in customization.
• Hybrid vendor built in-house: Opt for a hybrid approach where the AI solution is developed by an external vendor but implemented and operated within the utility's own environment. This method ensures that sensitive data remains within the utility's secure infrastructure, mitigating data security concerns associated with purchasing a fully external solution.
  

Questions to ask:

• What is the total cost of ownership for building vs. buying?
• How do the timelines for deployment compare between the two options?
• Are there any hidden costs associated with either approach?
• Do we have the right skill sets in house to support the development of a customer solution? If not, what is the cost and timeline to source and onboard?
• If a vendor is pursued, do they allow for bulk or pre-pay discounts that would make this decision advantageous?
• Do we know how to structure a contract to protect the utility and the utility’s data?
  

Resourcing: Internal capability vs. contracting

The management and maintenance of the AI solution post-implementation is a significant decision. Utilities must know whether they have the internal capability to manage the solution or if contracting external experts is a more viable option.  

Questions to ask: 

• Do we have the necessary internal expertise and availability to manage and maintain the AI solution?
• What are the costs and benefits of hiring external contractors versus training internal staff?
• What level of access would external contractors require across the various networks and tools for data retrieval, and what support requirements would be necessary to facilitate that access?
• How will ongoing support and maintenance be handled to ensure the AI solution remains effective?

Sample AI solution selection criteria

Accuracy and reliability

Precision and consistency of AI models are forefront factors when it comes to predicting outcomes and maintaining performance over time. Reliable systems ensure consistent performance under various conditions, reducing false positives and negatives that can be costly and dangerous. Accurate models capable of ingesting data across various formats and applications allow utilities to swiftly mobilize efforts and minimize costly iterations.  

Scalability

Scalability refers to the ability of the AI solution to handle increasing amounts of data and expand its functionality as needed. Utilities require scalable solutions that can grow with their data and operational needs, particularly as they integrate more inputs and data sources for comprehensive wildfire monitoring. A scalable solution ensures that the AI/ML system can adapt without losing performance or requiring significant overhauls as a utility's infrastructure and data requirements grow. 

Integration capabilities

The ease with which the AI solution can integrate with existing systems and infrastructure is critical for seamless operation to ensure efficient data flow and operational coherence. Effective integration across the many complex networks that utilities operate such as SCADA, ADMS, EMS, and GIS platforms maximizes live data utilization. 

User-friendliness

AI solutions should be accessible to operators and analysts without requiring extensive training, ensuring quick adoption and effective use. User-friendly interfaces and clear outputs enable utility personnel to make informed decisions quickly, which is especially important during critical situations like wildfire outbreaks. 

Cost-effectiveness

Utilities must balance the cost of the AI solution—including initial investment, maintenance, and operational expenses—against the potential savings from prevented wildfire damage, avoided fines, and operational efficiencies. Cost-effectiveness ensures that the utility can sustain the solution in the long term without financial strain. 

Security and compliance

Ensuring data security and regulatory compliance is critical to protect sensitive information and avoid legal and financial penalties. Utilities must choose AI solutions that adhere to stringent security protocols and comply with relevant regulations and industry standards to safeguard their operations and customer data. 

Vendor reputation

Partnering with reputable vendors with proven success in the utility sector can provide confidence in the solution's effectiveness and support. A reliable vendor offers transparency into product performance through routine audits and is likely to offer robust support, updates, and innovation over time—ensuring the solution remains effective and relevant. 

Conclusion

As utilities strive to bolster wildfire resiliency, the adoption of AI solutions presents an opportunity to enhance their preventive strategies. But given the sensitive nature of this initiative, thorough evaluation of the various options is necessary to ensure seamless integration into existing operations and effective utilization of current resources. By carefully selecting AI solutions that align with their needs and operational capabilities, utilities can amplify their wildfire prevention efforts while not only improving resilience but also establishing a sustainable and proactive approach to wildfire management.