  We consider the problem of automated anomaly detection for building level
heat load time series. An anomaly detection model must be applicable to a
diverse group of buildings and provide robust results on heat load time series
with low signal-to-noise ratios, several seasonalities, and significant
exogenous effects. We propose to employ a probabilistic forecast combination
approach based on an ensemble of deterministic forecasts in an anomaly
detection scheme that classifies observed values based on their probability
under a predictive distribution. We show empirically that forecast based
anomaly detection provides improved accuracy when employing a forecast
combination approach.
