Seven AI Use Cases That Deliver Value to Utilities

ML solutions can learn from data patterns across customers, distribution infrastructure and power generation assets. This newly generated information can be used to build infrastructure to meet net-zero requirements and goals. For example, geospatial-powered AI and deep learning models can be used to identify suitable sites for solar panels. These tools can measure land elevation, evaluate land use data, assess weather patterns, incorporate environmental factors and determine proximity to infrastructure and grid assets to consider solar power generation potential.

Utilities can use satellite data and ML models to identify vegetation species and other attributes to prioritize actions. For example, tree species identification can help accurately identify when and where to perform tree trimming and vegetation removal. Utilities can employ AI to take advantage of these higher-quality datasets to identify unstable slopes and determine if there’s a risk of landslides or if vegetation will slide into a transmission or distribution line.

Predictive AI models can sift through historical and forecasted meteorological data to identify areas in danger of extreme weather or climate risks, including where to harden assets to protect vulnerable populations from hurricanes, flooding and wildfire events. Utilities can overlay asset, weather and location-accurate maps and perform ML-powered simulations around storm scenarios. They can process imagery to detect dead and diseased trees, determine where to remove fuel and what utility poles to harden and coat with fire retardant material.

AI provides a foundation for grid modernization by accessing and analyzing vast volumes of real-time operational technology (OT) sensor data combined with IT application data. ML algorithms can then predict when grid components will fail and recommend when, where, and in what sequence to repair or replace parts. It can also adjust power distribution based on analysis of demand patterns to optimize energy usage and lower costs. AI-enabled real-time grid monitoring provides on-the-fly response capabilities to enhance capacity and reliability while reducing outages and mitigating their impact.

AMI 2.0—the next generation of smart meters—offers significantly more data points to evaluate and drive decisions. Utilities can use AI to extrapolate and understand more details about residential and industrial power usage. For example, new smart meters can determine appliance-level consumption, which utilities can leverage to offer new products and services, such as upgrade rebates. Utilities can also use AI/ML to quickly combine and process diverse data and identify areas of opportunity to market renewable energy products and services.

Chatbots can use large language models and prompt engineering to guide customers through common complaints and topics. Based on predefined keywords and phrases and using location-based data and analytics, the chatbot will supply a robust, rich experience for users with service questions. Whether calling to report an outage or asking about the duration of a service disruption, customers can ask questions in their own words and receive a response with more helpful context and detail.