Financial Forecasting Engine
Regional Utility Provider — 2.1M customers across 4 states.
2025
The challenge
What we found.
A major regional utility serving 2.1 million customers across 4 states struggled with demand forecasting accuracy. Their existing statistical models were built in the 1990s and couldn't account for rapid adoption of rooftop solar, EV charging patterns disrupting traditional load curves, and extreme weather events.
Forecast errors of 8–12% were costing $47M annually in over-procurement and spot market penalties.
The approach
How we built it.
PRR built a hybrid forecasting system combining traditional time-series methods with modern ML:
- Integrated weather APIs, smart meter data (15-minute intervals), EV charging networks, and solar generation feeds
- Ensemble model (LSTM networks + XGBoost + ARIMA) weighted by forecast horizon
- Custom SHAP explainability layer for regulatory reporting requirements
- Real-time adaptation with models retraining weekly on rolling 3-year windows
- 01Data LakeSmart meters + weather + grid
- 02EnsembleLSTM + XGBoost + ARIMA
- 03ResultsExplainable forecasts
The outcome
The impact.
| Metric | Before | After | Improvement |
|---|---|---|---|
| Forecast MAPE (day-ahead) | 8.4% | 3.1% | 63% reduction |
| Forecast MAPE (week-ahead) | 12.1% | 5.8% | 52% reduction |
| Over-procurement costs | $31M/year | $18M/year | -42% |
| Spot market penalties | $16M/year | $4M/year | -75% |
Stack
Tools used.
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