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AI could materially change how electric utilities operate, but EPRI’s October 19, 2023 congressional testimony presented possible benefits and risks—not proof that those benefits have already occurred. The testimony focused on utility operations such as asset management, wildfire response, storm recovery and energy-use optimization.
What EPRI told Congress about AI and energy
Jeremy Renshaw, an Electric Power Research Institute (EPRI) senior technical executive, told the U.S. House Energy and Commerce Subcommittee on Energy, Climate, and Grid Security on October 19, 2023: “Clearly, AI will have a significant impact on the energy industry, likely both positive and negative.”
EPRI framed AI as one tool that could support an affordable, reliable transition to cleaner energy. Its testimony described anticipated outcomes involving safety, affordability, reliability, efficiency and environmentally friendly energy production. It did not quantify those outcomes or claim that they had been achieved.
How could AI affect electric utilities?
The examples in EPRI’s testimony concern company and utility operations rather than consumer-facing AI products.
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Improving existing-asset efficiency
Models could analyze operating data from power-sector equipment and help identify inefficient performance or maintenance needs. Better analysis might help operators use existing assets more effectively, but the testimony supplied no measured savings, reliability improvement or deployment result.
Evaluating wildfire risk and detecting fires early
AI could support wildfire-risk evaluation and early-stage detection. Earlier or more precise alerts could give utilities and emergency personnel additional time to inspect infrastructure and respond. EPRI did not report a quantified improvement in detection, response time or avoided damage.
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Managing vegetation around infrastructure
Utilities could use AI to assess vegetation conditions near lines and other equipment, helping prioritize inspection or clearance work. The testimony did not establish how accurately such systems perform or how much they reduce outages.
Supporting storm recovery
After severe weather, AI could help utilities analyze incoming information, prioritize field work and coordinate restoration. These were proposed applications, not evidence of a particular restoration-time reduction.
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Optimizing energy use
AI could help analyze demand and operating conditions to optimize energy use across utility systems. EPRI presented this as a potential efficiency and affordability benefit, without providing a measured effect on bills, demand or emissions.
What evidence did EPRI cite about adoption?
EPRI said it had been involved in more than 70 AI application projects in the energy sector as of October 19, 2023. That is EPRI’s reported figure on that date, not an independently audited current project count. The testimony and announcement do not establish how many projects reached production, their costs or their results.
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What risks and implementation problems did EPRI identify?
Privacy and data security
AI systems may use operational, customer or other sensitive information. EPRI said organizations need to address privacy and security of the data used by models before relying on their outputs.
Cybersecurity threats and defenses
AI can support cybersecurity work, but it can also create new attack opportunities. Utilities therefore need to consider both AI-assisted defenses and threats against AI systems, their data and their surrounding infrastructure.
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- SAFETY YOU CAN TRUST WITH UL CERTIFICATION: With Emporia Energy, your home energy monitoring is safe, reliable, and certified. The Emporia Vue is UL Listed, meaning it has met rigorous safety standards for electrical products in the U.S. and Canada. This certification ensures that every component has been thoroughly tested to prevent hazards, such as overheating, short-circuiting, or fire, offering you peace of mind as you manage your home’s energy consumption.
- INSTALLS IN CIRCUIT PANEL of most homes with clamp-on sensors. Supports Single phase, Single-split phase, and 2-wire systems. 3-wire systems; 3-phase, 4-wire Wye systems with earthed (TN or TT) neutral (no-Delta) are supported with an additional 200A sensor (sold separately).
- 24/7 ENERGY MANAGEMENT AND MONITORING: Automate, manage and control your home's real power anywhere, anytime to prevent costly repairs, conserve energy, and save costs. Monitor solar / net metering. PROTECTED BY A 1-YEAR WARRANTY.
- LOWER YOUR ELECTRIC BILL: Configure settings in the Emporia Energy App to automate energy management for time of use, peak demand, excess solar, and rewards programs. You can even see live reporting and invaluable savings opportunities instantly. Gauge real-time spending and get actionable notifications and automated energy management to help you reduce costs.
- REAL-TIME ENERGY DATA: REQUIRES 2.4 GHz WIFI WITH AN INTERNET CONNECTION to monitor energy use with iPhone / Android / Web app. Vue sensors collect energy data and are accurate from ±2%. The Vue is UL and CE Listed for your safety. 1 second data is only available in the app (when actively open) and retained 3 hours. Minute and hour data are retained in the cloud. 1 minute data is retained 7 days, 1 hour data is retained indefinitely. Export cloud data whenever you want in the app.
Insufficient or poor-quality data
A model cannot compensate for missing, inaccurate or unrepresentative data. EPRI warned that deficient data can produce erroneous results that then influence operational decisions.
Workforce training and judgment
Workers need training to recognize when AI is appropriate, interpret its outputs and identify situations requiring human judgment. Automation does not remove responsibility for checking a recommendation before acting on it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should utilities build confidence in an AI system?
Renshaw’s testimony pointed to controls and testing, robust datasets and explainability as elements of responsible deployment. In practical terms, a utility evaluating a model should ask:
- What decision will the model support, and what happens if its output is wrong?
- Are the training and operational datasets sufficient, accurate and representative of the conditions the utility will encounter?
- What privacy, access-control and cybersecurity protections cover the data, model and connected systems?
- Can operators understand why the model produced a recommendation and override it when necessary?
- What testing, monitoring and change controls will detect performance degradation after deployment?
Comparing the use cases EPRI described
| Use case | Operational purpose | Data and safeguards to consider | What the testimony establishes |
|---|---|---|---|
| Asset efficiency | Find inefficient operation or maintenance needs | Equipment and operating data; data quality, testing and explainability | Potential application only; no quantified outcome |
| Wildfire risk and early detection | Assess risk and identify fires sooner | Environmental and infrastructure data; privacy, security and false-alert controls | Potential application only; no reported detection or damage metric |
| Vegetation management | Prioritize inspection and clearance near infrastructure | Geographic, vegetation and line data; accuracy testing and worker review | Potential application only; no outage or cost result |
| Storm recovery | Prioritize restoration and coordinate response | Outage, damage and field reports; resilient systems and human oversight | Potential application only; no restoration-time result |
| Energy-use optimization | Improve operational efficiency and manage demand | Load and system data; security, privacy and explainable recommendations | Potential application only; no measured bill, demand or emissions effect |
What this means for households and energy affordability
EPRI connected AI’s potential to affordability, reliability and efficiency, which are ultimately relevant to electricity customers. However, the October 2023 materials do not show that AI had lowered consumer bills, reduced outages or changed rates. Household effects would depend on how utilities deploy systems, the costs of implementation, regulatory treatment and whether operational gains are passed through to customers.
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What the testimony does—and does not—show
- It shows that EPRI viewed AI as capable of having a major effect on the energy industry, with positive and negative consequences.
- It identifies concrete utility applications: asset efficiency, wildfire evaluation and detection, vegetation management, storm recovery and energy-use optimization.
- It identifies privacy, cybersecurity, data quality and workforce preparation as central implementation concerns.
- It does not establish present-day deployment totals, realized savings, outage reductions, emissions reductions or consumer-bill effects.
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