As utilities continue accelerating electrification and clean energy initiatives, traditional load forecasting methods are no longer sufficient to support modern grid planning requirements. Rapid adoption of electric vehicles (EVs), distributed energy resources (DERs), heat pumps, and increasing customer energy demands are creating highly localized load growth patterns across distribution systems. Utilities are now under increasing pressure to improve forecasting accuracy at a much more granular level to proactively identify future capacity constraints and optimize infrastructure investments.
This case study highlights how AI-driven transformer-level load forecasting can help utilities modernize planning processes and improve visibility into future grid demand patterns.
Historically, many utility forecasting models have operated primarily at feeder, substation, or regional aggregation levels using historical growth trends and static planning assumptions. While these approaches provided high-level visibility, they often lacked the granularity required to identify localized overload risks occurring at the transformer level. As electrification continues accelerating, utilities need significantly deeper insight into how future demand growth will impact neighborhoods, customer clusters, and individual distribution assets.
To address this challenge, an AI-enabled transformer-level forecasting framework was developed using cloud-native analytics and machine learning technologies. The primary objective of the initiative was to create a scalable forecasting platform capable of predicting future electrical demand at the transformer level while enabling planners to evaluate multiple future growth scenarios dynamically.
The forecasting solution integrated multiple datasets across utility operations, planning, and external business drivers, including:
Advanced Metering Infrastructure (AMI) interval data
GIS and transformer connectivity data
Historical transformer loading patterns
Weather and climate data
Economic and demographic indicators
EV adoption projections
Distributed energy resource (DER) forecasts
Future commercial and residential development projects
Electrification growth assumptions
By combining these datasets, the platform enabled utilities to move beyond static forecasting and adopt a much more dynamic and data-driven planning approach.
One of the key capabilities introduced through the initiative was scenario-based forecasting. The solution generated Low, Base, and High forecast scenarios that allowed planners to evaluate different future growth trajectories based on changing assumptions. Rather than relying solely on historical growth trends, the models incorporated external drivers such as EV adoption rates, economic conditions, customer growth, and future infrastructure projects.
The forecasting models leveraged machine learning and advanced analytics techniques to identify hidden load growth patterns, transformer utilization trends, and localized demand changes that are often difficult to capture through traditional forecasting methods. The solution provided utilities with improved forecasting granularity and enabled planners to better understand how electrification and future customer behavior could impact the distribution grid over time.
The forecasting platform was designed on a modern cloud-native architecture capable of processing trillions of AMI and operational data points efficiently. Data pipelines standardized and curated large volumes of utility and external datasets into reusable analytical data products, enabling scalable and repeatable forecasting workflows. This approach significantly reduced manual effort associated with traditional spreadsheet-driven forecasting processes.
The initiative provided several operational and business benefits, including:
improved transformer-level visibility into future demand growth
earlier identification of localized overload risks
better infrastructure investment prioritization
improved electrification readiness planning
accelerated forecasting and scenario analysis cycles
reduced manual planning effort
increased confidence in long-term planning decisions
One of the most significant outcomes of the initiative was the ability to proactively identify future risk areas years in advance. Instead of reacting to overload conditions after they occur, planners could now evaluate how changing economic conditions, EV adoption, or future development projects may impact distribution infrastructure over time.
The solution also demonstrated the growing role of AI in utility planning modernization. By combining machine learning, cloud-native analytics, and scalable data engineering practices, utilities can significantly improve forecasting accuracy and planning agility while enabling more data-driven infrastructure investment decisions.
As electrification and distributed energy growth continue accelerating, transformer-level forecasting is expected to become a foundational capability for future distribution planning organizations. Utilities will increasingly require granular, dynamic, and scenario-driven forecasting solutions to support grid modernization initiatives, optimize capital investments, and ensure long-term reliability.
This case study illustrates how AI-enabled forecasting and cloud-native data platforms can help utilities modernize traditional planning processes and better prepare for the rapidly evolving energy landscape.