Renewable Forecasting and Predictive Maintenance

An energy operator manages renewable assets whose output varies with weather and equipment condition. Forecasts differ across sites, while maintenance teams rely on periodic inspections and alarm thresholds. Faults may go unnoticed until generation falls.

• Sensor feeds may be delayed, missing, or miscalibrated.
• Weather forecasts contain uncertainty and change over time.
• Sites differ in equipment, terrain, capacity, and maintenance history.
• False alarms waste site visits; missed alerts can allow faults to worsen.

Combine time-series forecasting with anomaly detection and an operator alert workflow. Use generation history, weather forecasts, telemetry, maintenance logs, and outage records. Forecasts include uncertainty ranges, and alerts show the signals that triggered them.

• Audit units, sampling frequency, missing periods, and asset metadata.
• Use time-aware training and evaluation splits.
• Compare forecasts with simple operational baselines.
• Separate planned outages, curtailment, grid events, sensor faults, and potential equipment faults.
• Rank alerts by operational impact and provide trend evidence.
• Feed confirmed maintenance outcomes into controlled improvement.

Measure forecast MAE or RMSE against baseline across seasons and sites. For anomaly detection, assess precision, recall, lead time, and false alerts per asset-day where labels exist. Test sensor loss, drift, extreme weather, and planned outages. Alerts support qualified operators.

• Forecast error versus baseline.
• Time between anomaly onset and alert.
• False alerts and missed confirmed events.
• Unplanned downtime and response time.