In the last decade, extended efforts have been poured into energy efficiency. Several energy consumption datasets were henceforth published, with each dataset varying in properties, uses and limitations. For instance, building energy consumption patterns are sourced from several sources, including ambient conditions, user occupancy, weather conditions and consumer preferences. Thus, a proper understanding of the available datasets will result in a strong basis for improving energy efficiency. Starting from the necessity of a comprehensive review of existing databases, this work is proposed to survey, study and visualize the numerical and methodological nature of building energy consumption datasets. A total of thirty-one databases are examined and compared in terms of several features, such as the geographical location, period of collection, number of monitored households, sampling rate of collected data, number of sub-metered appliances, extracted features and release date. Furthermore, data collection platforms and related modules for data transmission, data storage and privacy concerns used in different datasets are also analyzed and compared. Based on the analytical study, a novel dataset has been presented, namely Qatar university dataset, which is an annotated power consumption anomaly detection dataset. The latter will be very useful for testing and training anomaly detection algorithms, and hence reducing wasted energy. Moving forward, a set of recommendations is derived to improve datasets collection, such as the adoption of multi-modal data collection, smart Internet of things data collection, low-cost hardware platforms and privacy and security mechanisms. In addition, future directions to improve datasets exploitation and utilization are identified, including the use of novel machine learning solutions, innovative visualization tools and explainable mobile recommender systems. Accordingly, a novel visualization strategy based on using power consumption micro-moments has been presented along with an example of deploying machine learning algorithms to classify the micro-moment classes and identify anomalous power usage.

在过去的十年中，人们为提高能效投入了更多的精力。此后发布了几个能耗数据集，每个数据集的属性，用途和局限性都不同。例如，建筑能耗模式来自多种来源，包括环境条件，用户占用，天气条件和消费者偏好。因此，对可用数据集的正确理解将为提高能源效率提供坚实的基础。从对现有数据库进行全面审查的必要性出发，建议开展这项工作以调查，研究和可视化建筑能耗数据集的数值和方法性质。总共检查了31个数据库，并根据多项功能进行了比较，例如地理位置，收集时间，受监视家庭的数量，收集数据的采样率，次计量器具的数量，提取的功能部件和发布日期。此外，还分析和比较了用于不同数据集中的数据传输，数据存储和隐私问题的数据收集平台和相关模块。在分析研究的基础上，提出了一个新颖的数据集，即卡塔尔大学数据集，这是一个带注释的功耗异常检测数据集。后者对于测试和训练异常检测算法非常有用，因此可以减少浪费的能量。今后，将提出一组建议以改善数据集的收集，例如采用多模式数据收集，智能物联网数据收集，低成本硬件平台以及隐私和安全机制。此外，确定了改善数据集开发和利用的未来方向，包括使用新颖的机器学习解决方案，创新的可视化工具和可解释的移动推荐系统。因此，已经提出了基于使用功耗微矩的新颖可视化策略，以及部署机器学习算法以对微矩类别进行分类并识别异常用电的示例。