Buildings' energy consumption prediction models based on buildings’ characteristics: Research trends, taxonomy, and performance measures

Building's energy consumption prediction is essential to achieve energy efficiency and sustain-ability. Building's energy consumption is highly dependent on buildings' characteristics such as shape, orientation, roof type among others. This paper offers a systematic literature review of studies that proposed building's characteristics based energy consumption prediction models. In particular, the paper reviews the types of buildings, their characteristics, the type of energy predicted, the dataset, the artificial intelligence (AI) methods used for energy consumption prediction, and the implemented research evaluation performance measures. The review findings show that a small number of studies consider buildings' characteristics as predictors for energy consumption. Most of the studies use historical energy consumption data, i.e., time-series data, to predict future buildings' energy consumption. The present study contributes a new taxonomy of the most common AI methods used for energy consumption predictions based on buildings' characteristics. The study also provides a comparative analysis of the different AI methods in terms of their contributions regarding the prediction of energy consumption. The review identifies research gaps in the existing studies, which is used to highlight future research directions. •Comprehensive review of energy prediction studies based on buildings characteristics.•Taxonomy of machine/deep learning methods used in the reviewed prediction models.•Comparative analysis of prediction methods regarding energy prediction accuracy.•Performance metrics suitable for each of the identified machine/deep learning methods.•Summaries and statistics of input/output variables, datasets, and performance metrics.