Increasing the energy efficiency of the built environment has become a priority worldwide and especially in Europe. Because of the relatively low turnover rate of the existing built environment, energy efficiency retrofitting appears to be a fundamental step in reducing its energy consumption. Last experiences have shown that there is a vast energy efficiency potential lying in the building stock, and it is mainly untapped. One of the reasons is a lack of robust methodologies able to evaluate the effect of applied energy efficiency measures and inform about the expected impact of potential retrofitting strategies. Nowadays, dynamic measured data coming from automated metering infrastructure provides valuable information to evaluate the effect of energy conservation strategies. For this reason, energy performance modeling and assessment methods based on this data are starting to play a major role. In this paper, several methodologies for the measurement and verification of energy savings, and for the prediction and recommendation of energy retrofitting strategies, are analysed in detail. Practitioners looking at different options for these two processes, will find in this review a thorough and detailed overview of the different methods that can be used. Guidance is also provided to determine which method could work best depending on the specific case under analysis. The reviewed approaches include statistical learning models, machine learning models, Bayesian methods, deterministic approaches, and hybrid techniques that combine deterministic and data-driven modeling. Existing research gaps are identified and prospects for future investigation are presented within the main conclusions of this research work.

提高建筑环境的能源效率已成为全球范围内的重点工作，尤其是在欧洲。由于现有建筑环境的周转率较低，因此提高能效似乎是减少其能耗的基本步骤。最近的经验表明，建筑存量具有巨大的节能潜力，并且主要尚未开发。原因之一是缺乏能够评估已应用的能效措施的效果并告知潜在改装策略的预期影响的强大方法。如今，来自自动计量基础设施的动态测量数据可提供有价值的信息，以评估节能策略的效果。为此原因，基于这些数据的能源绩效建模和评估方法开始发挥重要作用。在本文中，详细分析了几种测量和验证节能量的方法，以及预测和推荐节能改造策略的方法。从实践中寻找这两个过程的不同选择，实践者将在本文中找到可以使用的不同方法的详尽详尽的概述。还提供了指南，以根据所分析的具体情况确定哪种方法最有效。审查的方法包括统计学习模型，机器学习模型，贝叶斯方法，确定性方法以及将确定性模型和数据驱动的模型相结合的混合技术。