Anomaly detection in electricity demand time series data

Abstract: The digitalization of the energy industry has made tremendous energy data available. This data is utilized across the entire energy value chain to provide value for customers and energy providers. One area that has gained recent attention in the energy industry is the electricity load forecasting for better scheduling and bidding on the electricity market. However, the electricity data that is used for forecasting is prone to have anomalies, which can affect the accuracy of forecasts. In this thesis we propose two anomaly detection methods to tackle the issue of anomalies in electricity demand data. We propose Long short-term memory (LSTM) and Feed-forward neural network (FFNN) based methods, and compare their anomaly detection performance on two real-world electricity demand datasets. Our results indicate that the LSTM model tends to produce a more robust behavior than the FFNN model on the dataset with regular daily and weekly patterns. However, there was no significant difference between the performance of the two models when the data was noisy and showed no regular patterns. While our results suggest that the LSTM model is effective when a regular pattern in data is present, the results were not found to be statistically significant to claim superiority of LSTM over FFNN.