Managing Anomalies in Energy Time Series for Automated Forecasting

The increasing number of recorded energy time series enables the automated operation of smart grid applications such as load analysis, load forecasting, and load management. However, to perform well, these applications usually require clean data that well represents the typical behavior of the underlying system. Unfortunately, recorded time series often contain anomalies that do not reflect the typical behavior of the system and are, thus, problematic for automated smart grid applications such as automated forecasting. While various anomaly management strategies exist, a rigorous comparison is lacking. Therefore, in the present paper, we introduce and compare three different general strategies for managing anomalies in energy time series forecasting, namely the raw, the detection, and the compensation strategy. We compare these strategies using a representative selection of forecasting methods and real-world data with inserted synthetic anomalies. The comparison shows that applying the compensation strategy is generally beneficial for managing anomalies despite requiring additional computational costs because it mostly outperforms the detection and the raw strategy when the input data contains anomalies.