Consumption Anomaly Detection
“Which meters show abnormal consumption?”
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A real-world example
Which meters show abnormal consumption?
Non-technical losses (energy theft, meter tampering, billing errors) cost utilities $96B globally per year. In developed markets, non-technical losses average 1-3% of revenue. Traditional threshold-based detection catches obvious anomalies but misses sophisticated theft patterns where consumption is gradually reduced or shifted. For a utility with $4B in annual revenue, a 1% non-technical loss rate means $40M in recoverable revenue.
How KumoRFM solves this
Graph-powered intelligence for energy and utilities
Kumo connects meters, readings, customers, weather, and tariffs into a consumption graph. The GNN learns normal consumption patterns per meter relative to its neighbors, customer type, weather, and tariff structure. Anomalies are detected not by absolute thresholds but by deviation from the graph-learned baseline: when a meter's consumption diverges from its neighborhood while weather and tariff conditions remain similar. This catches gradual theft and meter degradation that threshold-based systems miss.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
METERS
| meter_id | customer_id | type | install_date | zone_id |
|---|---|---|---|---|
| MTR101 | CUST01 | Smart | 2021-03-15 | ZONE-A |
| MTR102 | CUST02 | Smart | 2020-08-20 | ZONE-A |
| MTR103 | CUST03 | Legacy | 2015-01-10 | ZONE-B |
READINGS
| meter_id | date | daily_kwh | peak_kw | power_factor |
|---|---|---|---|---|
| MTR101 | 2025-03-01 | 32 | 4.2 | 0.95 |
| MTR102 | 2025-03-01 | 12 | 2.1 | 0.72 |
| MTR103 | 2025-03-01 | 85 | 12.5 | 0.88 |
CUSTOMERS
| customer_id | type | sqft | occupants | tariff |
|---|---|---|---|---|
| CUST01 | Residential | 2,200 | 4 | Standard |
| CUST02 | Residential | 2,400 | 3 | Standard |
| CUST03 | Commercial | 8,500 | N/A | Commercial |
WEATHER
| zone_id | date | avg_temp_f | heating_degree_days |
|---|---|---|---|
| ZONE-A | 2025-03-01 | 55 | 10 |
| ZONE-B | 2025-03-01 | 52 | 13 |
TARIFFS
| tariff_id | name | rate_per_kwh | peak_rate |
|---|---|---|---|
| T01 | Standard | $0.12 | $0.18 |
| T02 | Commercial | $0.09 | $0.14 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(READINGS.is_anomaly, 0, 1, days) FOR EACH METERS.meter_id
Prediction output
Every entity gets a score, updated continuously
| METER_ID | CUSTOMER | DAILY_KWH | EXPECTED_KWH | ANOMALY_PROB |
|---|---|---|---|---|
| MTR101 | CUST01 | 32 | 30 | 0.08 |
| MTR102 | CUST02 | 12 | 28 | 0.91 |
| MTR103 | CUST03 | 85 | 82 | 0.12 |
Understand why
Every prediction includes feature attributions — no black boxes
Meter MTR102 -- Residential customer CUST02 in ZONE-A
Predicted: 91% anomaly probability (12 kWh vs 28 kWh expected)
Top contributing features
Consumption 57% below neighborhood average
12 vs 28 kWh
32% attribution
Power factor degradation
0.72 (normal: 0.90+)
25% attribution
Gradual decline over 60 days
-45% trend
19% attribution
Weather conditions should increase consumption
55F (heating)
14% attribution
Similar home size/occupancy uses 28 kWh
2,400 sqft / 3 people
10% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A utility with $4B in annual revenue recovers $20-30M by detecting non-technical losses that threshold-based systems miss. Kumo's consumption graph identifies meters deviating from graph-learned neighborhood baselines, catching gradual theft and meter degradation.
Related use cases
Explore more energy & utilities use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
