Lower Costs and Reduce Resource Constraints with Location-based AI for Utilities

Process massive datasets at speed and scale for optimized operations

More utilities than ever are focusing IT and OT efforts on grid resiliency and reliability, sustainability goals and improved customer service. Solar and wind are increasing while hydro is being reduced. Demand is growing with electrification, caused by increased electric vehicle adoption, including light-duty and heavy-duty fleets as well as passenger cars, in addition to heat pumps, appliances, public transportation and building retrofits. Heating and cooling patterns are changing due to climate change. Moreover, customers are becoming both consumers and producers of electricity.

As a result, utilities today face a digital deluge of data. They often manage a combination of advanced distribution management systems, advanced metering infrastructure, energy management systems, distributed energy resource management systems, outage management systems and more. While the data streams widen and grow, making sense of the incoming informational flow becomes costly, timely and untenable.

That’s where artificial intelligence (AI) can help. AI provides a powerful means to assist with the harvesting and processing of data at superior speed and scale, reducing costs and resource strain over manual data management. Moreover, it liberates staff from mundane data-related activities so they can prioritize more critical and strategic work.

The result is more information available faster for answering vital questions and predicting future outcomes. AI enables a utility to be more responsive to its customers and more resilient to potential disruptions.

Data Doesn’t Always Deliver Insights

As companies embrace digital transformation, many discover they have too much data from too many sources.

Utilities often have multiple enterprise systems running independently. In addition, data is frequently collected from many sources and formats.

Manual data collection adds another layer of complexity and cost, with field crews using pen and paper to document work with no standard format. Moreover, with smart meters, bi-directional networks, electric vehicle charging stations and distributed energy resources, utilities generate more data than they can handle. Poor data management leads to inefficient processes, increased resource expenditures and higher business costs.

Even as AI deployments expand across industries, utilities and other asset-intensive organizations hesitate to adopt it. Many companies fear security risks and lack in-house knowledge about building data pipelines and models. They also view AI potentially leading to job displacement or adding to their data quality challenges.

Despite these perceptions, AI offers several business benefits that can improve data management, leading to smarter, faster decision making and lower costs. This is especially true when you combine AI with geospatial solutions. Location-based AI allows organizations to manage data at extraordinary speed and scale. The integrative nature of geospatial technology connects systems, applications, data, people and processes using geography. Combining AI with geospatial solutions increases operational efficiencies and performance without adding additional resources.

Do More with Less

At its highest level, location-based AI, including machine learning (ML) techniques, allows utilities to efficiently manage and correct massive amounts of unstructured and real-time data. By training models to understand spatial relationships, utilities can connect and enrich different data types, from video, text, PDFs, satellite and drone imagery, audio and social, using spatial relationships.

They can process climate change data such as flooding, hurricanes, snowstorms and tornadoes. Location-based AI can also consume real-time data from smart meters, grid monitoring sensors, satellites, pressure and flow sensors, as well as leak detection sensors.

Organizations can rectify and extract value from information accuracy and completeness at a superior scale and lightning speed. They can then employ query and map visualization to improve decision making and predict future outcomes.

As utilities face an aging workforce where long-term employees will retire and take decades of institutional knowledge, GIS-based AI allows companies to capture, catalog, index and organize information to support the next generation of operators and improve accessibility to operational insight. It provides an enterprise view of information previously siloed or not digitally maintained.

AI can uniquely process essential knowledge to run the utility business. It fills the gap between unstructured data and securing answers, making information access as easy as a conversation.

 
Companies can lower costs and reduce resource constraints using GIS-based AI to:

 
Automate data management

Geospatial technology combined with AI allows utilities to improve data management. For example, utilities can perform data conflation, which uses the location-based attributes of data to compare, merge, create, and improve accuracy for enormous data sets in multiple systems and databases.

Utilities can build a massive spatial inventory of assets and land using terrestrial, satellite, and unmanned aerial vehicle (UAV) imagery, then automate cleansing and processing the raw data into useable formats using these sources using AI models, removing the laborious, time-consuming data work from staff. Automatically monitor equipment and measure temperatures 24/7 using thermal cameras—saving time and resources.

Automate workflows

Location-based AI can automate numerous processes. For example, it can locate and detect disturbances or fluctuations in the electrical grid. GIS-enabled object detection and feature extraction allow companies to autodetect massive numbers of utility-specific devices and structures from imagery.

In another example, natural language processors (NLP) AI agents can perform routine and repetitive tasks such as reading documents and emails, analyzing language and images and understanding the intent and content of communications.

Improve analytics

AI-powered predictive analytics can help utilities optimize energy usage and implement real-time monitoring and control of energy consumption. AI-based analytics can improve distribution grid management. As new devices are attached to the grid, AI-based solutions can accurately pinpoint outages and disturbances faster and more precisely than traditional applications or human assessment.

Location-based AI bots receiving data from sensor-enabled devices can help operators determine the location of an outage or grid disturbance, redistribute energy around the issue and restore power more quickly. Field crews can then be dispatched immediately to the correct location with the right equipment and material.

Improve decisions

By analyzing historical data and identifying patterns within a specific service territory, AI systems can recommend energy-saving measures, reduce energy waste and enhance overall efficiency.

Companies can monitor electricity demand and usage in real time, enabling control system staff to make accurate decisions on the fly to reallocate energy supply and improve service delivery. In addition, location-based AI helps agents perform cognitive tasks, navigate uncertainty and resolve inconsistencies using map-based query and visualization.

Predict with accuracy

Location-based AI brings unprecedented abilities around forecasting. Companies can effectively analyze consumer behavior and weather patterns data to estimate energy consumption and proactively allocate resources. Similarly, gas utilities can predict leaks or bursts in pipelines early to perform maintenance on pipelines and address corrosion concerns before they cause costly delays.

How to Get Started

For utilities looking to employ AI, there are a few critical issues to consider from the start.

First, think beyond technical know-how, including governance and policy creation, particularly security issues. For example, organizations are already discovering that putting customer and sensitive data into generative AI solutions (e.g., ChatGPT, Claude and Gemini) can cause serious risks.

Governance involves tracking and documenting data inside AI to avoid improperly exposing data, such as critical infrastructure data or personally identifiable information of staff or customers. It also involves monitoring for errors, hallucinations and confabulation.

Utilities should consider creating their own internal version of a generative AI solution to keep data safe and secure. Education and policy creation are a vital part of an awareness strategy. Companies can employ an innovation contest to use crowdsourcing to ideate and creatively problem-solve.

In addition, take advantage of the cloud as part of your AI strategy. By moving AI-based applications to the cloud, companies reduce costs and increase scalability and performance. They can employ a usage-based model for scaling compute power and ensuring your AI performance.

Most importantly, the critical first step is simply beginning. More utilities than ever are starting to employ AI-based solutions. Understanding the importance of governance, building internal AI applications, and taking advantage of the cloud provide a foundation to build.

Adapt to the Changing AI Landscape with TRC

TRC helps utilities across the country around the world supply safe and secure location-based AI solutions. We maintain proven protocols for identifying where and how to apply location-based AI tools for data identification, classification, organization and delivery. They include data encryption, access controls and security protocols to protect sensitive customer information.

TRC consultants, technologists, developers, and industry experts’ knowledge and skills are built on decades of experience. They employ agile methodologies, scrum and other client-first project management techniques that result in lower acquisition and total ownership costs and ensure on-time, on-budget and on-target project delivery.

TRC creates new pathways for AI and GIS success through:

• Experience serving energy industry clients
• Technical IT/OT, geospatial and AI technical knowledge
• A track record of delivering self-sustaining, self-sufficient projects
• Commitment to transparency, communication and ownership
• Solutions, packages and patterns that accelerate time-to-value

Visit today to learn more about TRC’s AI solutions.