Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

中文翻译：

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机器学习估计建筑能耗和性能：回顾

不断增长的人口和建造新建筑物的市政业务需求不断增长，被认为是造成温室气体的最主要因素。因此，提高建筑部门的能源效率已成为减少气体排放量和化石燃料消耗的重要目标。减少新建筑物的CO2排放和能耗的最有效方法是在非常早期的设计阶段就考虑能源效率。另一方面，有效的能源管理和智能翻新可以增强现有库存的能源绩效。所有这些解决方案都需要准确的能量预测，以实现最佳决策。最近几年，已经提出了一般意义上的人工智能（AI）和特定术语中的机器学习（ML）技术来预测建筑能耗和性能。本文对包括人工神经网络，支持向量机，基于高斯回归和聚类在内的四种主要ML方法进行了实质性回顾，这些方法通常用于预测和改善建筑物的能源性能。