This paper proposes a system for the prediction of events in the smart grid. The system infers a label indicating if an event is going to occur in a future time window, in a specific asset, from data of events generated by grid assets and exogenous variables (e.g. weather data). The system design presented follows a sliding-window classification approach, bag-of-words event representation and makes use of random forests models. The systems performance is evaluated in an experimental case study, backed by real data, with the aim of predicting future interruptions in distribution transformers. Performance results indicate that the system is able to deal with highly imbalanced data and validate its adequacy in dealing with the approached problem, achieving up to 0.75 area under the receiver operating characteristic curve in testing.