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PREDICTIVE ANALYTICS
Financial Forecasting Engine
Regional Utility Provider (2.1M customers)
+3%
Forecast Accuracy
$25M
Annual Savings
-42%
Over-procurement Costs
-75%
Spot Market Penalties
The Challenge
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
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
Forecasting Pipeline
1
Data Lake
Smart Meters + Weather + Grid
2
Ensemble
LSTM + XGBoost + ARIMA
3
Results
Explainable Forecasts
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% |
Net annual savings: $25M in reduced energy procurement costs
Tech Stack
Azure Machine Learning
Azure Databricks
InfluxDB
Power BI
Custom SHAP Layer
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