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.
翻译:在不同建筑中安装的次计和智能传感器每天生成大量数据;如果利用得当,这些数据可以帮助终端用户、能源生产商和公用事业公司发现异常的电力消耗量,了解每个异常现象的原因;因此,异常现象的发现可以阻止一个小问题成为广泛、昂贵和耗时的问题;此外,这将有助于改善决策,以减少浪费能源,促进可持续和能源效率行为;在这方面,提议本文件深入审查在电力消费中发现异常现象的现有框架,并对现有解决办法进行批判性分析;具体而言,开展一项全面调查,采用新的分类法,根据在执行过程中采用的不同因素,如机器学习算法、特征提取法、检测水平、计算平台、应用情景和隐私保护,对一个小问题进行分类;此外,这是讨论在建设能源消费过程中发现异常现象的首篇评论文章;在进行前瞻性、具有挑战性、具有挑战性的问题、困难和挑战之前,将进行彻底讨论,包括:缺乏关于当前调查、当前调查趋势的准确定义,以便评估现有数据可变性,(三)评估现有数据可追溯性,评估现有数据可追溯性,以便了解当前研究(三)评估现有数据可追溯性,评估现有数据可追溯性,并评估现有数据可追溯性技术。