Multi-Agent-Based Unsupervised Detection of Energy Consumption Anomalies on Smart Campus

Abstract

The smart campus is becoming a reality with the advancement of information and communication technologies. For energy efficiency, it is essential to detect abnormal energy consumption in a smart campus, which is important for a “smart” campus. However, the obtained data are usually continuously generated by ubiquitous sensing devices, and the abnormal patterns hidden in the data are usually unknown, which makes detecting anomalies in such a context more challenging. Moreover, evaluating the quality of anomaly detection algorithms is difficult without labeled datasets. If the data are annotated well, classical criteria such as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">receiver operating characteristic</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">precision recall</i> curves can be used to compare the performance of different anomaly detection algorithms. In a smart campus environment, it is difficult to acquire labeled data to train a model due to the limited capabilities of the sensing devices. Therefore, distributed intelligence is preferred. In this paper, we present a multi-agent-based unsupervised anomaly detection method. We tackle these challenges in two stages with this method. First, we label the data using ensemble models. Second, we propose a method based on deep learning techniques to detect anomalies in an unsupervised fashion. The result of the first stage is used to evaluate the performance of the proposed method. We validate the proposed method with several datasets, and the experimental results demonstrate the effectiveness of our method.