Know What the Grid Will Do Before It Does It
Whether you're planning curtailment, procuring power, sizing batteries, or deploying chargers — every decision starts with knowing what load looks like tomorrow. Our TS2Vec models deliver 3.21% MAPE, validated across four weather extremes on ERCOT.
Lower Error Than XGBoost
TS2Vec outperforms the best traditional ML model on 6-month ERCOT backtesting.
Weather Scenarios Validated
Low temp drops, medium drops, high temp drops, and sustained heat — all within target.
ERCOT Backtested
Real market data validation, not synthetic benchmarks. Production-grade accuracy.
Built for Companies That Run on Grid Intelligence
Crypto Miners
Curtailment planning and energy cost optimization for compute-intensive operations.
- Schedule voluntary curtailment ahead of grid stress — earn demand response credits instead of eating penalties
- Shift compute-intensive operations into forecasted low-price windows to cut energy costs 15-30%
- Evaluate grid stability at prospective sites using historical load pattern analysis
Data Centers
Power procurement optimization and cooling load prediction for facility operators.
- Buy day-ahead power within 3% of actual consumption — stop overpaying on balancing markets
- Predict weather-driven cooling load spikes to schedule HVAC pre-cooling during off-peak hours
- Anticipate facility load approaching contract limits to avoid peak demand charges
Battery Storage
Charge/discharge optimization and ancillary services bidding for storage operators.
- Time charge cycles to low-demand periods and discharge during forecasted price spikes for arbitrage
- Improve bidding accuracy for ancillary services — frequency regulation and spinning reserves
- Right-size battery installations using historical load patterns at specific grid nodes
EV Companies
Charging infrastructure planning and fleet optimization for EV operators.
- Forecast local grid capacity to site chargers where the grid can handle them — avoid costly transformer upgrades
- Schedule fleet charging during forecasted low-demand windows to minimize demand charges
- Identify high-demand periods for V2G revenue — sell back to the grid when prices peak
Why General-Purpose Models Fall Short
Off-the-shelf forecasting breaks when conditions get extreme — exactly when accuracy matters most. Our approach learns patterns directly from raw data and adapts to your specific load profile.
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Manual feature engineering
Defining seasonality, weather impact, and calendar effects by hand
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Breaks on extreme days
Non-linear relationships during sudden weather shifts defeat traditional models
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Retrain per new target
Changing the forecast item or time period means rebuilding the entire model
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4.37% MAPE (best case)
XGBoost on ERCOT 6-month backtesting
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Self-supervised learning
TS2Vec automatically learns hidden dynamics from raw data — no hand-tuning
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Validated across 4 weather extremes
Low/med/high temp drops and sustained heat — all within target accuracy
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One model adapts to new targets
Universal embeddings work across forecast items, time periods, and segments
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3.21% MAPE
26% lower error than XGBoost, 23% lower than LSTM
Three Steps to Better Forecasts
We reduce the complexity of time series forecasting to a simple, repeatable process. No manual feature engineering required.
Find similar days
Our model scans years of historical data to find the days whose weather patterns most closely match tomorrow's forecast.
Build a local model
Using weather and load data from those similar days, we build a regression model tuned to the specific conditions you're about to face.
Forecast with confidence
The local model predicts hourly load for the target day. Because it's trained on genuinely similar conditions, it stays accurate where general models fail.
TS2Vec Embeddings
Every historical day is mapped to a high-dimensional vector — a numerical fingerprint capturing its complete weather and load DNA. Learned through self-supervised contrastive training, no labeled data needed.
Vector Distance Ranking
We calculate the distance between the query day's vector and every historical vector. The top-n closest vectors identify structurally similar days — not just by temperature, but by the full pattern.
GAN-Augmented Robustness
For extreme cold days with sparse historical precedent, generative adversarial networks synthesize realistic embeddings. This fills training gaps and maintains accuracy for rare scenarios.
For the business, this translates directly to better forecasting and planning, improved price estimations, and better decision-making based on more accurate insights.
6-Month Backtesting Comparison (ERCOT)
| Model | MAPE | Key Differentiator |
|---|---|---|
| TS2Vec (Similar Day) | 3.21% | |
| Traditional ML (XGBoost) | 4.37% | |
| Deep Learning (LSTM) | 4.19% |
Low Temp, Sudden Drop
Med Temp, Sudden Drop
High Temp, Sudden Drop
High Temp, Constant
Let's run the numbers on your grid
We'll show you how TS2Vec-powered forecasting performs against your current models — using your own market data.