2025 / ENS Partnership (course project)
Electricity Price Forecasting (ENS Partnership)
Time-series forecasting for day-ahead price spreads
Time SeriesForecastingEnergyML
Illustrative forecast chart (synthetic)
Overview
Built a forecasting workflow for electricity price spreads with lag features, calendar effects, and model monitoring. Visuals are illustrative where original files are missing.
Problem
Day-ahead price spreads are volatile and require robust forecasting to inform hedging and operational decisions.
Role
Individual project
Timeline
Spring 2025
Tools
Python / pandas / XGBoost / LightGBM / statsmodels
Data
- Hourly market prices with calendar + lag features
- Feature sets include rolling stats, holiday flags, and price spreads
Approach
- Built baseline seasonal naive + rolling average models
- Trained gradient-boosted regressors with lag features
- Added calibration checks and backtesting windows
Evaluation
- Illustrative comparison of baselines vs boosted models (labeled)
- Rolling-window MAE/MAPE tracking and error analysis by season
Results
- Illustrative metrics show boosted models outperform baselines
- Generated forecast intervals for operational planning
Deployment
- Daily batch scoring with drift checks
- Alerting when residuals exceed control limits
Limitations
- Illustrative figures used where original assets are missing
- Weather and demand signals could further improve accuracy
Evidence
Illustrative forecast chart (synthetic)
Forecasting pipeline diagram (concept)
Repro Steps
- Synthetic visuals are labeled as illustrative
- Pipeline description included for replication
Next Steps
- Integrate weather forecasts and load predictions
- Evaluate probabilistic forecasts with quantile loss
- Deploy in a lightweight dashboard