  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.
