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The building energy (BE) management has an essential role in urbansustainability and smart cities. Recently, the novel data science anddata-driven technologies have shown significant progress in analyzing theenergy consumption and energy demand data sets for a smarter energy management.The machine learning (ML) and deep learning (DL) methods and applications, inparticular, have been promising for the advancement of the accurate andhigh-performance energy models. The present study provides a comprehensivereview of ML and DL-based techniques applied for handling BE systems, and itfurther evaluates the performance of these techniques. Through a systematicreview and a comprehensive taxonomy, the advances of ML and DL-based techniquesare carefully investigated, and the promising models are introduced. Accordingto the results obtained for energy demand forecasting, the hybrid and ensemblemethods are located in high robustness range, SVM-based methods are located ingood robustness limitation, ANN-based methods are located in medium robustnesslimitation and linear regression models are located in low robustnesslimitations. On the other hand, for energy consumption forecasting, DL-based,hybrid, and ensemble-based models provided the highest robustness score. ANN,SVM, and single ML models provided good and medium robustness and LR-basedmodels provided the lower robustness score. In addition, for energy loadforecasting, LR-based models provided the lower robustness score. The hybridand ensemble-based models provided a higher robustness score. The DL-based andSVM-based techniques provided a good robustness score and ANN-based techniquesprovided a medium robustness score.

Systematic review of deep learning and machine learning for building energy