Energy Demand Forecasting
Forecasting hourly energy demand for a utility provider.
Overview
Improved forecasts reduce over-provisioning and blackouts.
Problem & Constraints
Accurate demand forecasts are critical for grid stability and cost savings.
Data
3 years hourly data, weather features, train/val/test split, no leakage.
Approach
Baseline: ARIMA → LSTM → Feature engineering → Hyperparameter tuning.
Evaluation
MAE: 0.13, RMSE: 0.21
Results
Improved forecast accuracy by 22%, enabled dynamic pricing.
Production / Engineering
- Inference design: See repo for details
- Latency & cost: See highlights
- Monitoring: Monitoring ideas in repo
Demo
Demo coming soon.
Links
What I'd Do Next
See repo for future improvements and roadmap.