Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table

A Novel Approach for Anomaly Detection in Power Consumption Data

International audienceAnomalies are patterns in data that do not follow the expected behaviour and they are rarely encountered. Anomaly detection has been widely used within diverse research areas such as credit card fraud detection, image processing, and many other application domains. In this paper, we focus on detecting anomalies in power consumption data. The identification of unusual behaviours is important in order to foresee uncommon events and to improve energy efficiency. To this end, we propose a model to precisely identify anomalous days and another one to localize the detected anomalies. Normal days are identified using a simple Auto-Encoder reconstruction technique, whereas the localization of the anomaly throughout the day is performed using a combination of LSTM and K-means algorithms. This hybrid model that combines prediction and clustering techniques, permits to detect unusual behaviour based on the assumption that identical daily consumption can appear repeatedly due to users’ living habits. The model is evaluated using real-world power consumption data collected from Pecanstreet in the United States

A Novel Approach for Anomaly Detection in Power Consumption Data

International audienceAnomalies are patterns in data that do not follow the expected behaviour and they are rarely encountered. Anomaly detection has been widely used within diverse research areas such as credit card fraud detection, image processing, and many other application domains. In this paper, we focus on detecting anomalies in power consumption data. The identification of unusual behaviours is important in order to foresee uncommon events and to improve energy efficiency. To this end, we propose a model to precisely identify anomalous days and another one to localize the detected anomalies. Normal days are identified using a simple Auto-Encoder reconstruction technique, whereas the localization of the anomaly throughout the day is performed using a combination of LSTM and K-means algorithms. This hybrid model that combines prediction and clustering techniques, permits to detect unusual behaviour based on the assumption that identical daily consumption can appear repeatedly due to users’ living habits. The model is evaluated using real-world power consumption data collected from Pecanstreet in the United States