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

Abstract

Enormous amounts of data are being produced everyday by submeters and smart sensors installed in different kinds of 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 to become widespread, costly and time-consuming issue. Moreover, this will help in better decision-making to reduce wasted energy and promote sustainable and energy efficiency behavior. In this regard, this paper is proposed to indepthly review existing frameworks of anomaly detection in power consumption and provide a critical analysis of existing solutions. Specifically, a comprehensive survey is introduced, in which a novel taxonomy is introduced to classify existing algorithms based on different factors adopted in their implementation, such as the machine learning algorithm, feature extraction approach, detection level, computing platform, application scenario and privacy preservation. To the best of the authors' knowledge, this is the first review article that discusses the 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 consumptions, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, and (iv) platforms for reproducibility. Following, insights about current research trends that anomaly detection technology needs to target for widespreading its application and facilitate its implementation are described before deriving a set of challenging future directions attracting significant research and development attention.

A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings

“…According to the literature, the field of ADD in buildings is progressively leveraging on the application of data analytics techniques [12] for addressing both detection and diagnosis tasks.…”

“…In this perspective, dimensionality reduction can be used with a low computational cost, for example, for removing irrelevant patterns and redundancy from energy consumption datasets. As reviewed in [12], various techniques were explored to enable the classification of data as normal or anomalous, such as principal component analysis (PCA) [27], linear discriminant analysis (LDA) [28], singular variable decomposition (SVD) [29].…”

Cloud Energy Micro-Moment Data Classification: A Platform Study

“…The QUD is a collection of readings from different mounted devices (e.g. light lamp, air conditioning, refrigerator, and computer) coupled with quantitative details, such as temperature, humidity, ambient light intensity, and space occupation [44]. To the best of the researchers' understanding, QUD is the first dataset in the Middle East in which a normal 240V voltage is used with variable recording duration ranging from 3 seconds to 3 hours [45].…”

Probabilistic Forecasting Based Joint Detection and Imputation of Clustered Bad Data in Residential Electricity Loads

“…With regard to anomaly detection, previous studies have employed traditional algorithms, such as the k-nearest neighbors (k-NN), support-vector machine (SVM), decisiontree (DT), as well as deep-learning methods, such as convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN) to obtain energy consumption data [12]. Especially regarding residential load data, Xu et al [13] suggested a detection method that combines RNNs and quantile regression.…”