What problem does it solve?
Con Edison's rollout of 5.3 million smart meters was expected to generate between 100 terabytes and 1 petabyte of data annually, far more than any team can review reading by reading. Buried in that volume are the installation and configuration issues that leave a meter or its network module unhealthy, which is what Con Edison's deployment set out to find. More generally, the same kind of data holds the individual appliances, such as electric vehicles, heat pumps and air conditioning, that a utility needs to understand as adoption grows, and the customers who would benefit most from an efficiency programme but are hard to identify from billing data alone.
How does it work?
- Ingest at scale. Readings stream from millions of meters into a data platform, alongside the utility's asset, billing and programme data.
- Watch meter and network health. Machine learning flags meters and communication modules showing signs of a deployment or configuration problem, so field crews fix the ones that actually need attention.
- Disaggregate usage. A separate model estimates which appliances, such as HVAC, water heating, electric vehicle charging or pool pumps, are driving each home's consumption from the meter signature alone, without a sensor on the appliance itself.
- Segment and target. The disaggregated data groups customers by what is actually happening in their home, such as households with an electric vehicle or an ageing HVAC system, so an efficiency, demand response or electrification programme can be targeted instead of broadcast to everyone.
- Feed operations and customer teams. A prioritised list, a dashboard or a personalised message reaches the team or the customer, closing the loop from raw meter data to action.
- Audience
- Back office
- Autonomy
- Assist
- Adoption
- Early adopters
- Channels
- Internal tools, API and system to system
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
No public deployment has disclosed a measurable outcome yet.
Value drivers: Lower cost to serve, Customer experience, Employee productivity.
Indicative value
A utility with 2 million smart meters and an energy efficiency programme budget
USD 600,000 to USD 10 million
Avoided field visit and rework cost from early meter health detection per year
How this is calculated
Formula: meters * issueRate * costPerIssue. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Smart meters in the analytics programme meters, smart meters | 1,500,000 | 2,500,000 | Range set around the 2 million meter reference organization, well within Con Edison's 5.3 million meter deployment. |
| Share of meters with a deployment or health issue found by analytics each year issueRate, fraction of meters | 0.005 | 0.02 | Editorial assumption, replace with your own meter health data. |
| Cost saved per flagged issue by folding it into a planned visit instead of a repeat or emergency visit costPerIssue, USD per issue | 80 | 200 | Editorial assumption for the difference between a planned and an emergency or repeat US utility field visit; replace with your own figure. No deployment on this page reports a per issue avoided cost. |
What it leaves out: Meter health savings only, and it assumes a flagged issue can be folded into a planned visit rather than always triggering a separate site visit, which is an editorial assumption no deployment on this page confirms. It also leaves out the analytics platform and integration cost, and any separate value from the efficiency and electrification programmes the same data supports, which no deployment on this page reports as a single company wide figure.
Who already uses it?
5 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Arizona Public Service (APS)
United States · Energy and utilities · 2026
Arizona Public Service embedded Bidgely's High Bill Analyzer into its call centre workflows to give advisors exact, appliance level answers for every bill spike, instead of a generic explanation of a high bill. On calls where advisors used the tool, Bidgely reports a 7 percent lift in customer experience scores, an 82 percent advisor experience score (4.1 out of 5), and agents naming it their favourite tool of the year.
- Satisfaction uplift: 7%
"The payoff: a 7% CX lift on calls using HBA, an 82% advisor experience score (4.1 / 5), and a tool agents named their favorite of the year."
Claimed by: vendor
NV Energy
United States · Energy and utilities · 2026
NV Energy used Bidgely's EV Intelligence to identify electric vehicle customers and their on peak charging across a sample population and narrow it down to the customers with the most on peak charging and the greatest load shifting potential for a managed charging programme. Starting from 20,241 sampled customers, the utility identified 419 EV users, narrowed that to 233 charging during peak hours, and focused recruitment on 103 high value customers who Bidgely reports contributed 63 percent of the load shifting potential. With targeted recruitment and telematics based managed charging, Bidgely reports that NV Energy achieved three times higher kW per car than the average population, about 1 kW per car over 20 or more events.
No outcome disclosed.
Fluvius
Belgium · Energy and utilities · 2025
Fluvius, the Flemish electricity and gas distribution system operator, wants a voltage indicator for every connection on its low voltage network, but voltage data is not yet available everywhere while the smart meter rollout continues. Fluvius uses a machine learning algorithm that looks for similar connections across its entire Flemish dataset and, based on matching features between measured and non measured connections, predicts a maximum and minimum voltage value for every connection. Fluvius calls the result its Client Voltage Indicator and uses it to monitor and optimize the low voltage network until every smart meter is active and providing real voltage data.
No outcome disclosed.
Southern California Gas Company (SoCalGas)
United States · Energy and utilities · 2020
Southern California Gas Company (SoCalGas) worked with Bidgely on a digital only home energy report programme for medium consumption residential gas customers, a segment that traditional paper based home energy reports, which target high consumption customers, do not reach. The reports were built on AMI meter disaggregation, and the programme exceeded its savings goal, saving 565,000 therms by December 2020, with more than 405,000 customers receiving the reports digitally at a 50 percent open rate.
No outcome disclosed.
Consolidated Edison (Con Edison)
United States · Energy and utilities · 2019
Con Edison built an enterprise data analytics platform on C3 AI to run Advanced Metering Infrastructure operations for its 5.3 million meter smart meter rollout, aggregating two years of data from 13 source systems covering 5 million customer accounts and integrating over 180 billion rows of data a year across those systems. Two machine learning algorithms and 50 analytics identify deployment and installation issues and determine meter and network health, giving the utility a real time, prioritised view from an individual meter up to the whole system, in a 10 month project.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- Interval meter reads at the frequency the advanced metering infrastructure supports
- Meter and network asset data, such as install date, model and communication module
- Customer and premise data linking a meter to a household or business
Systems to integrate
- Meter data management system
- Geospatial information system for network topology
- Customer information system or CRM, for targeting and outreach
- Demand side management or efficiency programme platforms
Complexity: High
Con Edison's deployment aggregated two years of data from 13 source systems covering 5 million customer accounts into an integrated data image; that integration work is typically harder than the machine learning itself.
- 1
Start with meter and network health, not customer analytics
Con Edison's first phase covered deployment and installation issues plus meter and network health, using two machine learning algorithms and 50 analytics; the company planned further customer insight and distribution and transmission automation applications for later phases.
- 2
Unify the source systems before modelling
Con Edison's deployment integrated two years of data across 13 source systems before the machine learning models were configured; budget and staff for that integration effort, not just the model.
- 3
Disaggregate before you personalise
Appliance level disaggregation from the meter signal is what lets a programme target the households with an electric vehicle or an ageing HVAC system, rather than mailing everyone the same offer.
- 4
Close the loop into an actual programme
Southern California Gas Company's deployment fed a digital only home energy report programme for its medium consumption gas customers, which exceeded its savings goal, not a dashboard; without a programme on the receiving end, better analytics changes nothing for the customer.
- 5
Scale meter by meter, programme by programme
Widen coverage and add new applications, such as electrification planning or peak forecast, once the foundational data platform is proven.
Guardrails
- Customer usage data used for targeting is handled under the utility's own data privacy and retention rules
- A person reviews the priority list before a field crew is dispatched based on a flagged meter
- Model changes are tested against historical data before being applied to live operations
KPIs to instrument
- Meters flagged with a health or deployment issue, and the share confirmed correct on inspection
- Programme enrollment and savings among AI targeted customers versus a general mailing
- Time from a flagged issue to resolution
Human in the loop
Operations analysts triage the prioritised list of flagged meters before a field crew is dispatched, and programme managers decide which segments an efficiency or electrification campaign actually targets based on the disaggregated data.
Common failure modes
- A flood of technically correct but unprioritised flags
- Millions of meters can produce more anomalies than any team can act on; rank by impact and route only the highest priority batches to a person. Con Edison's application produces a prioritised list of meters that require attention.
- Disaggregation that does not hold up across meter and appliance types
- Appliance signatures vary by region, climate and equipment age; validate disaggregation accuracy on a local sample before using it to target a programme at scale.
What are the risks and rules?
EU AI Act
Depends on design
Annex III point 2 covers AI systems intended to be used as a safety component in the management and operation of critical digital infrastructure and the supply of water, gas, heating or electricity. Meter health prioritisation and usage disaggregation for programme targeting are not intended as safety components, so they stay outside that scope regardless of whether a person reviews the output. The tier would instead be high risk if the same kind of analytics were intended as a safety component in network operation or supply, for example directly controlling grid or metering protection systems; a human in the loop is then an Article 14 obligation for that high risk system, not a way to fall outside the category.
Rules that apply
Controls to put in place
- A documented separation between advisory analytics and any system that can act on grid protection or metering infrastructure directly
- Data minimisation and retention limits on the household level usage data used for disaggregation and targeting
Frequently asked questions
- What does AI actually do with smart meter data?
The deployments on this page show different jobs on the same underlying data. Con Edison used C3 AI to monitor the health of its 5.3 million meter rollout end to end, from an individual meter up to the whole system, while Southern California Gas Company used Bidgely's platform to disaggregate usage from smart meter data for digital energy efficiency reports for its medium consumption gas customers, a programme that exceeded its savings goal.
- How much data is involved?
Con Edison's smart meter deployment was expected to generate between 100 terabytes and 1 petabyte of data a year. Separately, and only once, building the analytics platform itself meant aggregating two years of data from 13 source systems covering 5 million customer accounts.
- Does this replace smart meters themselves?
No. It is the analytics layer on top of an existing advanced metering infrastructure rollout; the Con Edison and Southern California Gas Company deployments both assume the meters are already reporting interval data.
- Can it detect theft or non technical losses?
Detecting unusual consumption patterns is a well documented research area for smart meter analytics, but no deployment on this page reports a theft or loss detection result with a checked figure, so treat that specific claim as unproven until you have your own evidence.
How to cite this page
Blits.ai AI Use Case Library, "AI analytics for smart meter and AMI data", last verified 28 September 2026, https://www.blits.ai/ai-use-cases/smart-meter-analytics. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 30 September 2026: Added deployments: Fluvius.
- 29 September 2026: Added deployments: NV Energy, Arizona Public Service (APS).
- 28 September 2026: First published