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 recommender systems.

中文翻译：

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建筑能耗数据集：调查、分类和未来方向

在过去的十年中，人们在能源效率方面投入了大量精力。此后发布了几个能源消耗数据集，每个数据集的属性、用途和限制各不相同。例如，建筑能源消耗模式有多种来源，包括环境条件、用户入住率、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奠定坚实的基础。从对现有数据库进行全面审查的必要性出发，这项工作旨在调查、研究和可视化建筑能耗数据集的数值和方法学性质。从地理位置、收集时期、监测户数、采集数据的抽样率、子计量器具的数量、提取的特征和发布日期。此外，还分析和比较了不同数据集中使用的数据收集平台和相关模块，用于数据传输、数据存储和隐私问题。在分析研究的基础上，提出了一个新的数据集，即卡塔尔大学数据集，它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，从而减少浪费的能源。展望未来，提出了一套改进数据集收集的建议，例如采用多模态数据收集、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，