Grid Load Forecasting
“What will grid load be in each zone tomorrow?”
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A real-world example
What will grid load be in each zone tomorrow?
Load forecasting errors cost utilities $50-200 per MWh in balancing costs. Under-forecasting forces expensive peaker plants online; over-forecasting wastes committed generation capacity. Traditional models forecast at the system level and miss zone-specific dynamics: localized weather, event-driven demand spikes, and behind-the-meter solar that reduces apparent load. For a utility serving 2M meters, a 3% improvement in day-ahead zone-level accuracy saves $15-25M annually in dispatch costs.
How KumoRFM solves this
Graph-powered intelligence for energy and utilities
Kumo connects meters, zones, weather forecasts, events, and historical load into a grid graph. The GNN learns zone-level demand patterns including spatial correlations (when one zone peaks, which others follow), weather sensitivity by zone (coastal vs. inland), and event impacts (stadiums, conventions). PQL predicts hourly load per zone for the next 24 hours, enabling optimized generation dispatch and demand response activation.
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 | zone_id | customer_type | avg_daily_kwh |
|---|---|---|---|
| MTR001 | ZONE-A | Residential | 28 |
| MTR002 | ZONE-A | Commercial | 420 |
| MTR003 | ZONE-B | Industrial | 8,500 |
ZONES
| zone_id | substation | peak_capacity_mw | avg_load_mw |
|---|---|---|---|
| ZONE-A | SUB-North | 450 | 310 |
| ZONE-B | SUB-South | 680 | 520 |
| ZONE-C | SUB-West | 320 | 215 |
WEATHER
| zone_id | date | high_temp_f | humidity_pct | cloud_cover |
|---|---|---|---|---|
| ZONE-A | 2025-03-06 | 92 | 65% | Clear |
| ZONE-B | 2025-03-06 | 88 | 70% | Partly cloudy |
| ZONE-C | 2025-03-06 | 78 | 45% | Overcast |
EVENTS
| event_id | zone_id | type | expected_attendees | date |
|---|---|---|---|---|
| EVT01 | ZONE-A | Stadium concert | 45,000 | 2025-03-06 |
HISTORICAL_LOAD
| zone_id | date | peak_mw | total_mwh | solar_offset_mwh |
|---|---|---|---|---|
| ZONE-A | 2025-03-05 | 385 | 7,200 | 420 |
| ZONE-B | 2025-03-05 | 560 | 12,100 | 180 |
| ZONE-C | 2025-03-05 | 245 | 4,800 | 650 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(HISTORICAL_LOAD.total_mwh, 0, 24, hours) FOR EACH ZONES.zone_id
Prediction output
Every entity gets a score, updated continuously
| ZONE_ID | DATE | PREDICTED_PEAK_MW | PREDICTED_TOTAL_MWH | VS_YESTERDAY |
|---|---|---|---|---|
| ZONE-A | 2025-03-06 | 420 | 8,100 | +12.5% |
| ZONE-B | 2025-03-06 | 575 | 12,400 | +2.5% |
| ZONE-C | 2025-03-06 | 230 | 4,500 | -6.3% |
Understand why
Every prediction includes feature attributions — no black boxes
ZONE-A -- Tomorrow (March 6) load forecast
Predicted: 420 MW peak, 8,100 MWh total (+12.5% vs yesterday)
Top contributing features
Stadium concert (45K attendees)
+35 MW impact
30% attribution
Temperature forecast (92F = cooling demand)
+8% base load
26% attribution
Reduced solar offset (clear but hot = AC)
-12% offset efficiency
19% attribution
Weekday commercial load pattern
Thursday peak
14% attribution
Spatial correlation with ZONE-B heat wave
Correlated
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 utility serving 2M meters saves $15-25M annually in dispatch costs by improving zone-level load forecasting 3%. Kumo's grid graph captures event impacts, spatial weather correlations, and behind-the-meter solar offsets that system-level models aggregate away.
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.
