Machine Learning to identify aberrant energy use to detect property failures

Sammanfattning: The digitalization of energy sector has provided immense amount of data about buildings which created an untapped opportunity for energy savings using energy data analytics. In recent years, there has been significant research on energy optimization using machine learning. With the advancement in deep neural networks, researchers have investigated the potential of using time series machine learning algorithms to develop sophisticated energy prediction and proactive alert systems for energy management. In this thesis, we aim to explore utility of time series machine learning algorithm for anomaly detection to alert customer about abnormal energy consumption. In our quest to find effective anomaly detection technique, we researched on several time-series anomaly detection techniques and selected long short-term memory (LSTM) network due to popular implementation and current scientific research interest. Our results indicate linear regression has achieved better prediction with MSE around 0.066 kWh compared to LSTM with 0.073 kWh. In terms of anomaly detection, baseline persistence has detected all five types of anomalies with average precision of 45.4% and average recall of 36.4%. Meanwhile, LSTM only detected two out of five anomalies with average precision of 100% and average recall of 17%. Therefore, investigation has shown great promise in use of persistence and linear regression models for anomaly detection due to simplicity and accuracy.