An end-to-end AutoML pipeline that trains an AutoML model on historical ONS open data and produces monthly forecasts of electric energy consumption across Brazil's four subsystems (N, NE, S, SE) and the national grid (SIN). Updated automatically on the 1st of each month via GitHub Actions.
Tip: Click a legend item to show/hide it. Double-click to isolate a single series. Use the range buttons or the slider below the chart to zoom into a period. The orange region is the forecast; to the left of the line, actual data overlaps the forecast for comparison.
Daily energy load, reservoir storage (EAR) and generation mix from the ONS open data portal.
H2O AutoML — best model: GBM with 37 engineered features (temporal, lag, rolling, ratios). Test MAPE: 2.19%. Current month MAPE (5 days): 6.31%. Tracked and versioned in MLflow, with a rolling-window accuracy check that automatically retrains and redeploys the model on sustained drift.
GitHub Actions runs on the 1st of every month: checks for new ONS data, incrementally updates the consolidated dataset, and re-runs inference. GitHub: Energy_Consumption_AutoML.