Smart Street Lighting Controller with Predictive Maintenance

The system performs preliminary actions by predicting lamp failures before they occur using machine learning models that analyze historical lighting data. This allows maintenance to be scheduled optimally - not too early (wasting lamp life) and not too late (causing inconvenience). The prediction model calculates probability scores for each lamp failing within a specific time window, enabling proactive but timely replacement scheduling.

The system implements feedback by continuously monitoring lighting data from lamps and using this information to update failure predictions. The machine learning model learns from historical data patterns, including actual failure events, to improve prediction accuracy over time. This closed-loop feedback mechanism ensures maintenance scheduling is based on actual lamp performance rather than fixed schedules.

The system extracts only the most relevant features from the collected lighting data for model training and prediction. Instead of processing entire raw datasets, the system identifies and extracts key features such as lighting intensity patterns, duration metrics, and temporal characteristics that are most predictive of lamp failure. This extraction approach maintains prediction accuracy while significantly reducing data management and processing requirements.

The system collects more data than strictly necessary for basic monitoring but processes only the essential portions needed for prediction. The machine learning model is trained on comprehensive historical data to learn patterns, but during operation, it uses optimized feature sets and sampling strategies to make predictions without processing every single data point, thus balancing accuracy with processing efficiency.