Anomaly Detection Algorithm Using a Hybrid Modelling Approach for Energy Consumption Time Series

Many energy time series captured by real-time systems contain errors or anomalies that prevent accurate forecasts of time series evolution. However, accurate forecasting of load time series and fluctuating renewable energy feed-in as well as subsequent optimisation of the dispatch of controllable generators, storage and loads is crucial to ensure a cost-effective, sustainable and reliable energy supply. Therefore, we investigate methods and approaches for a system solution that automatically detect and replace anomalies in time series to enable accurate forecasts. Here, we introduce a hybrid anomaly detection system for energy consumption time series, which consists of two different neural networks (Seq2Seq and autoencoder) and two more classical approaches (entropy, SVM classification). This network is able to detect different types of anomalies, namely, outliers, zero points, incomplete data, change points and anomalous (parts of) time series. These types are defined for the first time mathematically. Our results show a clear advantage of the hybrid modelling approach for detecting anomalies in previously unknown energy time series compared to the single approaches. In addition, due to the generalisation capability of the hybrid model, our approach allows very good estimation of energy values without requiring a large amount of historical data to train the model.