Energy Consumption Optimization
“What will energy consumption be for this production schedule?”
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A real-world example
What will energy consumption be for this production schedule?
Energy represents 5-15% of manufacturing costs and is rising. Production schedules are optimized for throughput, not energy consumption. Shifting high-energy processes to off-peak hours or sequencing equipment startups to avoid demand peaks can cut energy costs 10-20%. For a plant spending $30M per year on energy, a 15% reduction saves $4.5M annually while reducing carbon footprint.
How KumoRFM solves this
Graph-powered intelligence for manufacturing
Kumo connects production schedules, equipment, energy meters, and production orders into a factory energy graph. The GNN learns each equipment's energy profile under different operating conditions, how startup sequences create demand peaks, and how schedule adjustments ripple through the energy curve. PQL predicts total energy consumption per proposed schedule, enabling planners to compare alternatives before committing.
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
SCHEDULES
| schedule_id | shift | date | total_orders |
|---|---|---|---|
| SCH01 | Morning | 2025-03-05 | 42 |
| SCH02 | Afternoon | 2025-03-05 | 38 |
| SCH03 | Night | 2025-03-05 | 25 |
EQUIPMENT
| equipment_id | type | rated_power_kw | efficiency_pct |
|---|---|---|---|
| EQ201 | Furnace | 500 | 88% |
| EQ202 | Compressor | 120 | 92% |
| EQ203 | Conveyor | 15 | 95% |
ENERGY_METERS
| meter_id | equipment_id | kwh_last_shift | peak_kw |
|---|---|---|---|
| MET01 | EQ201 | 3,800 | 520 |
| MET02 | EQ202 | 880 | 135 |
| MET03 | EQ203 | 110 | 18 |
PRODUCTION_ORDERS
| order_id | schedule_id | product | qty | equipment_id |
|---|---|---|---|---|
| PO601 | SCH01 | Steel-Part-A | 200 | EQ201 |
| PO602 | SCH01 | Plastic-Part-B | 500 | EQ202 |
| PO603 | SCH02 | Assembly-C | 150 | EQ203 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(ENERGY_METERS.kwh_last_shift, 0, 8, hours) FOR EACH SCHEDULES.schedule_id
Prediction output
Every entity gets a score, updated continuously
| SCHEDULE_ID | SHIFT | PREDICTED_KWH | PEAK_KW | EST_COST |
|---|---|---|---|---|
| SCH01 | Morning | 5,200 | 580 | $520 |
| SCH02 | Afternoon | 4,100 | 460 | $410 |
| SCH03 | Night | 2,800 | 340 | $196 |
Understand why
Every prediction includes feature attributions — no black boxes
Schedule SCH01 -- Morning shift, 42 orders
Predicted: 5,200 kWh predicted ($520 estimated cost)
Top contributing features
Furnace EQ201 cold start penalty
+400 kWh
32% attribution
Peak demand charge (morning rate)
580 kW peak
24% attribution
Order volume above average
42 vs 35 avg
19% attribution
Compressor concurrent operation
3 hours overlap
14% attribution
Equipment efficiency at current age
88% avg
11% 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 plant spending $30M per year on energy saves $4.5M by optimizing production schedules for energy consumption. Kumo's factory energy graph predicts consumption per schedule alternative, identifying cold-start penalties and peak demand overlaps that simple metering misses.
Related use cases
Explore more manufacturing 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.
