DataSheet1_Systematic Review of Deep Learning and Machine Learning for Building Energy.docx

The building energy (BE) management plays an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumption and energy demand datasets for a smarter energy management. The machine learning (ML) and deep learning (DL) methods and applications, in particular, have been promising for the advancement of accurate and high-performance energy models. The present study provides a comprehensive review of ML- and DL-based techniques applied for handling BE systems, and it further evaluates the performance of these techniques. Through a systematic review and a comprehensive taxonomy, the advances of ML and DL-based techniques are carefully investigated, and the promising models are introduced. According to the results obtained for energy demand forecasting, the hybrid and ensemble methods are located in the high-robustness range, SVM-based methods are located in good robustness limitation, ANN-based methods are located in medium-robustness limitation, and linear regression models are located in low-robustness limitations. 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-based models provided a lower robustness score. In addition, for energy load forecasting, LR-based models provided the lower robustness score. The hybrid and ensemble-based models provided a higher robustness score. The DL-based and SVM-based techniques provided a good robustness score, and ANN-based techniques provided a medium robustness score.

建筑能源（BE）管理在都市可持续性和智慧城市建设中扮演着至关重要的角色。近期，新颖的数据科学和数据驱动技术已在分析能源消耗和能源需求数据集以实现更智能的能源管理方面取得了显著进展。特别是机器学习（ML）和深度学习（DL）方法及其应用，对于精确且高性能能源模型的推进展现出了巨大的潜力。本研究对基于ML和DL的技术在处理建筑能源系统中的应用进行了全面综述，并进一步评估了这些技术的性能。通过系统性的综述和全面的分类，对基于ML和DL技术的进展进行了细致的调查，并介绍了具有潜力的模型。根据能源需求预测的结果，混合和集成方法位于高稳健性区间，基于SVM的方法位于良好的稳健性限制范围内，基于ANN的方法位于中等稳健性限制范围内，而线性回归模型则位于较低的稳健性限制范围内。另一方面，对于能源消耗预测，基于DL、混合和基于集成的方法提供了最高的稳健性评分。ANN、SVM和单ML模型提供了良好和中等稳健性，而基于LR的模型则提供了较低的稳健性评分。此外，对于能源负荷预测，基于LR的模型提供了较低的稳健性评分。混合和基于集成的方法提供了较高的稳健性评分。基于DL和基于SVM的技术提供了良好的稳健性评分，而基于ANN的技术则提供了中等稳健性评分。