Renewable Generation Forecasting
“What will solar/wind generation be tomorrow?”
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A real-world example
What will solar/wind generation be tomorrow?
Renewable intermittency costs grid operators $5-15B annually in curtailment, balancing, and reserve capacity. Solar and wind forecasting errors of 15-25% force operators to maintain expensive spinning reserves. As renewable penetration increases, forecast accuracy becomes critical for grid stability and cost control. For a grid with 5 GW of renewable capacity, a 5% improvement in day-ahead forecasting saves $40-60M annually in reduced curtailment and reserve requirements.
How KumoRFM solves this
Graph-powered intelligence for energy and utilities
Kumo connects generators, weather forecasts, historical generation, and grid demand into a renewable energy graph. The GNN learns generation patterns that depend on spatial weather propagation (cloud fronts moving across solar farms), wake effects between wind turbines, and how grid demand context affects curtailment decisions. PQL predicts hourly generation per site for the next 24-48 hours, enabling optimized dispatch and storage decisions.
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
GENERATORS
| generator_id | type | capacity_mw | location | age_years |
|---|---|---|---|---|
| GEN01 | Solar Farm | 250 | Arizona | 4 |
| GEN02 | Wind Farm | 180 | Texas | 6 |
| GEN03 | Solar Farm | 120 | California | 2 |
WEATHER_FORECASTS
| location | date | hour | cloud_pct | wind_speed_mph | temp_f |
|---|---|---|---|---|---|
| Arizona | 2025-03-06 | 12:00 | 10% | 8 | 95 |
| Texas | 2025-03-06 | 14:00 | 25% | 22 | 72 |
| California | 2025-03-06 | 13:00 | 60% | 12 | 68 |
HISTORICAL_GENERATION
| generator_id | date | total_mwh | capacity_factor | curtailed_mwh |
|---|---|---|---|---|
| GEN01 | 2025-03-05 | 1,450 | 72.5% | 0 |
| GEN02 | 2025-03-05 | 980 | 54.4% | 45 |
| GEN03 | 2025-03-05 | 520 | 43.3% | 0 |
GRID_DEMAND
| region | date | peak_demand_mw | renewable_pct | storage_available_mwh |
|---|---|---|---|---|
| Southwest | 2025-03-06 | 18,500 | 32% | 2,400 |
| South Central | 2025-03-06 | 25,200 | 28% | 1,800 |
| Pacific | 2025-03-06 | 22,000 | 38% | 3,200 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(HISTORICAL_GENERATION.total_mwh, 0, 24, hours) FOR EACH GENERATORS.generator_id
Prediction output
Every entity gets a score, updated continuously
| GENERATOR_ID | TYPE | PREDICTED_MWH | CAPACITY_FACTOR | VS_YESTERDAY |
|---|---|---|---|---|
| GEN01 | Solar | 1,520 | 76.0% | +4.8% |
| GEN02 | Wind | 1,120 | 62.2% | +14.3% |
| GEN03 | Solar | 380 | 31.7% | -26.9% |
Understand why
Every prediction includes feature attributions — no black boxes
Generator GEN03 -- 120 MW Solar Farm in California
Predicted: 380 MWh predicted (31.7% capacity factor, -26.9% vs yesterday)
Top contributing features
Cloud cover forecast
60% (vs 25% yesterday)
35% attribution
Cloud front propagation from coast
Arriving 10 AM
24% attribution
Temperature impact on panel efficiency
68F (optimal)
17% attribution
Historical pattern for overcast days
30-35% capacity factor
14% attribution
Grid curtailment likelihood (low demand)
Possible PM
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 grid with 5 GW of renewable capacity saves $40-60M annually by improving day-ahead generation forecasting 5%. Kumo's renewable graph captures spatial weather propagation, wake effects, and grid demand context that site-level weather models miss.
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.
