故障检测、诊断和预测（FDD&P）可以通过降低供暖、通风和空调（HVAC）的能耗，同时保持乘员舒适性，从而极大地帮助改善建筑运行性能。特别是，预测作为一种新兴技术，正吸引着建筑运营商和研究人员的大量关注，因为它通过持续监测建筑能源系统的健康状况，实现了主动预防故障的策略。本文提出了一种基于机器学习的暖通空调预测方法。基于机器学习和数据挖掘技术，提出的方法可以帮助从历史建筑运行和维护数据中开发预测模型。在介绍了所提出的方法之后，我们讨论了为评估所提出方法的可行性和实用性而进行的建筑运行模拟。 这些数值实验的结果表明，基于机器学习的方法可以有效地用于暖通空调的预测。引用:2019年冬季会议，佐治亚州亚特兰大，会议论文

Fault detection, diagnostics, and prognostics (FDD&P) can greatly help improve the performance of building operations by reducing energy consumption for heating, ventilation and air-conditioning (HVAC) while maintaining occupant comfort at the same time. In particular, prognostics as an emerging technique, is attracting an amount of attention from building operators and researchers because it enables a pro-active fault prevention strategy through continuously monitoring the health of building energy systems. In this paper, we propose to develop a machine learning-based method for HVAC prognostics. Building on techniques from machine learning and data mining, the proposed methods can help develop predictive models from the historic building operation and maintenance data. After presenting the proposed method, we discuss the building operation simulation conducted to generate data for evaluating the feasibility and usefulness of the proposed methods. The results from these numerical experiments demonstrated that the machine learning-based methods can be effective for HVAC prognostics.