Smarter Utilities: Machine Learning in Meter Data Management
In the early days of metering, utilities often struggled to get enough data. Today, the problem is the opposite. With the rise of Advanced Metering Infrastructure (AMI), utilities now collect more data than many legacy systems and organizations can handle. In some cases, this can mean up to hundreds of millions of individual meter reads every day. This challenge will increase significantly in the next decade.
A 2024 Transforma Insights report estimates that global smart meters will double from 1.7 billion in 2023 to 3.4 billion in 2033. And, with this flood of information comes new challenges of how to process and make sense of it all quickly, efficiently, and accurately.
In the early days of metering, utilities often struggled to get enough data. Today, the problem is the Utilities increasingly need smarter, faster, and more cost-effective ways to manage metering data to improve the accuracy of billable reads, provide grid data, reduce manual exceptions, identify data issues before they cause problems, and more. This must be done while minimizing infrastructure costs and staff workload.
Static rules-based systems have long been the standard for Validation, Estimation, and Editing (VEE). Often these rules were too stringent or configured improperly, resulting in too many exceptions for users to review, and too many service orders to investigate. In contrast, machine learning (ML) models, especially those built using advanced techniques like deep learning, can learn patterns from historical data, predict expected outcomes, and identify when something looks off, even if that “anomalous” pattern has never been seen before. These models are data-driven and adaptive by design. And, they’re proving to be especially well-suited for metering data.
A New Way to Detect Anomalies
One promising technique involves autoencoder-based deep learning models trained on historical AMI data. These models use a self-supervised approach: they learn from both normal and intentionally corrupted examples to identify when something about a new data point seems inconsistent. The result is a system that can not only flag potentially anomalous readings but also assign them a severity score, which can help utilities prioritize which issues are worth investigating.
Models built using this approach have shown strong performance across both high- and low-usage scenarios during initial testing. Compared with traditional rules-based VEE, they’ve demonstrated higher accuracy in detecting real issues and a substantial reduction in false positives. The tested ML model achieved a 63% reduction in false positive exceptions in high-usage scenarios. That translates directly into fewer erroneous user exceptions, less time spent on manual interventions, fewer truck rolls, and fewer customer complaints due to inaccurate bills.
And because ML-based anomaly detection can identify unexpected patterns, it also helps surface emerging issues that may not yet be defined by static rules.
The vision is not just to prove the value of ML in theory, but to embed it directly into the metering data flow. Reads that pass the model’s normalcy checks can move immediately to final measurement, bypassing costly and compute-heavy VEE steps. Only data that’s flagged as suspect would be routed for traditional, manual rule-based validation—reducing overall processing time and cost.
This kind of real-time “energy transaction normalcy check” is inspired by techniques used in the credit card industry for fraud detection. And just like in that domain, the benefits in metering are clear: faster processing, more reliable outcomes, better focus on the cases that truly need attention, and less on false alarms.
Broader Future Applications
Anomaly detection is just the beginning for AI/ML application to utilities’ customer experiences. As utilities get more comfortable with ML, the range of use cases continues to expand. ML has already been successfully adopted in areas such as EV detection, and early-stage research and proof-of-concept efforts are exploring how ML could support additional functions.
Even beyond metering, ML is making its way into other parts of the utility customer experience. AI-powered summarization tools are being piloted to help customer service agents rapidly understand the context of incoming calls, reducing handling time and improving service consistency. Payment propensity models are being tested to predict whether, and when, a customer is likely to pay their next bill, helping utilities take proactive steps to offer the right kind of assistance or payment arrangements.
Other initiatives are exploring how ML can tailor the next-best product or offer for a given customer, based on usage history, participation in other programs, and even signals such as appliance usage patterns or solar output.
These capabilities all share common threads of making better use of the massive amounts of data utilities already have and doing so in ways that are scalable, adaptive and intelligent.
The use of ML in meter data management (MDM) is still in its early stages, but the direction is undeniable. As utilities continue to modernize their systems, ML offers a powerful way to help improve data accuracy, help reduce operational costs, and extract more value from the data they already collect.
And, importantly, these efforts don’t exist in isolation. They’re part of a broader push across the industry to use AI and ML to improve everything from billing and collections to customer service, distribution management and planning, and program targeting. MDM is simply one of the first areas where the payoff is already visible and growing.
References
Transforma Insights. (2024, August 15). Global smart meters to double to 3.4 billion by 2033, generating USD40 billion in annual revenue. https://transformainsights.com/news/global-smart-meters-2033
About the Author
Kevin Yordy has worked with machine learning in some regard for almost 20 years. Starting in 2006, he spent six years at a startup focused on developing and applying new NLP and LSA (Latent Semantic Analysis) techniques for social media analysis. The company, Collective Intellect, was acquired by Oracle in 2012. Since then, Kevin has worked in a few different groups. In 2017, he was the first product manager to join the emerging “Adaptive Intelligent Applications” group within Oracle, one of the first dedicated AI/ML teams focused on embedding ML into different Oracle applications. Kevin worked closely with data science teams to develop innovative models for diverse areas such as ecommerce, customer service, and HR. He joined the Oracle Utilities team in 2022 as a product manager for the data science research team, where he focused on deep learning and other innovative solutions to emerging energy utility problems. Kevin has since moved into a role working on ML-related projects from inception to product launch.
