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（Building Energy, BE）管理对于城市可持续发展与智慧城市建设具有至关重要的作用。近年来，新兴数据科学与数据驱动技术在面向智慧能源管理的能耗与能源需求数据集分析领域取得了显著进展。其中，机器学习（Machine Learning, ML）与深度学习（Deep Learning, DL）方法及应用，在构建精准高性能能源模型方面展现出了良好的发展前景。本研究对应用于建筑能源系统的机器学习与深度学习技术开展了系统性综述，并进一步对这些技术的性能进行了评估。通过系统性综述与全面分类体系，本研究深入探究了机器学习与深度学习技术的发展进展，并介绍了性能优异的相关模型。针对能源需求预测任务的结果显示：混合方法与集成方法处于高鲁棒性区间，基于支持向量机（Support Vector Machine, SVM）的方法具备良好鲁棒性，基于人工神经网络（Artificial Neural Network, ANN）的方法鲁棒性处于中等水平，而线性回归模型的鲁棒性较低。另一方面，在能耗预测任务中，基于深度学习、混合方法与集成方法的模型获得了最高的鲁棒性评分；基于人工神经网络、支持向量机与单一机器学习模型的鲁棒性处于良好与中等水平；基于线性回归（Linear Regression, LR）的模型鲁棒性评分较低。此外，在能源负荷预测任务中，基于线性回归的模型鲁棒性评分最低；混合方法与集成方法的模型鲁棒性评分最高；基于深度学习与支持向量机的技术鲁棒性表现良好，基于人工神经网络的技术鲁棒性处于中等水平。