Forecasting Power Outages in Western Uganda: An Engineering Blueprint

An outage forecast is useful only when it is honest about uncertainty and connected to a practical response. It should help a facility prepare for elevated risk; it should never imply that a distribution network failure can be predicted with certainty.

Uganda's electricity distribution landscape has changed, including the transition from Umeme's concession to UEDCL in 2025. Any analysis spanning that period must account for operator, asset, maintenance, and reporting changes rather than treating the entire history as one consistent dataset.

Establish the Data Foundation

A credible project would begin with timestamped interruption and restoration records, feeder or service-area identifiers, planned-maintenance notices, weather observations, and customer-impact information where access is authorised. Document missing events, timestamp quality, network changes, and the difference between planned and unplanned interruptions.

Build a Baseline Before a Model

Start with simple seasonal and recent-history baselines. Compare them with more complex models only after enough clean historical data is available. Weather may contribute useful context in some locations, but the relationship must be tested locally; heat, rainfall, wind, lightning, and feeder condition should not be treated as universal predictors.

Design the Forecast as a Decision Aid

A notification should identify the service area, forecast horizon, confidence or risk band, last data refresh, and the factors behind the estimate. A low-confidence forecast should say so. Do not tell customers that a specific outage will occur at a precise hour unless the utility has confirmed a planned interruption.

Useful responses may include checking backup-power readiness, protecting cold-chain equipment, charging batteries, and reviewing continuity plans. Hospitals and other critical facilities should follow their own approved resilience procedures.

Validate Against Real Operations

Use time-based holdout periods so future data never leaks into model training. Measure calibration, precision, recall, false alarms, missed events, and performance by feeder and season. Compare the system with a simple baseline and have utility engineers review the failure cases.

ERA's sector performance reports and UEDCL's published annual reporting provide useful starting points for understanding the sector. They do not substitute for a complete, permissioned, event-level dataset.

The goal is a transparent operational signal that helps people prepare—not a promise to predict every interruption.