Design of a Fuzzy Logic Based Framework for Comprehensive Anomaly Detection in Real-World Energy Consumption Data.

Due to the rapid growth of energy consumption worldwide, it has become a necessity that the energy waste caused by buildings is explicated by the aid of automated systems that can identify anomalous behaviour. Comprehensible anomaly detection, however, is a challenging task considering the lack of annotated real-world data in addition to the real-world uncertainties such as changing weather conditions and varying building features. Fuzzy Logic enables modelling knowledge-based non-linear systems that can handle these uncertainties, and facilitates modelling human interpretable systems. This paper proposes a new method for annotating anomalies and a novel framework for interpretable anomaly detection in real-world gas consumption data belonging to the educational buildings of the Hogeschool van Amsterdam. The proposed architecture uses the Wang and Mendel rule learning with k-means clustering and does not require prior knowledge of the data, while preserving transparency of the model behaviour. Experiments have shown that the proposed system matches the performance of existing baselines using an artificial neural network while providing additional desired features such as transparency of the model behaviour and interpretability of the detected anomalies.