Novel machine learning paradigms-enabled methods for smart building operations in data-challenging contexts:Progress and perspectives
作者机构：Key Laboratory for Resilient Infrastructures of Coastal CitiesMinistry of EducationShenzhen University Sino-Australia Joint Research Center in BIM and Smart ConstructionShenzhen University College of Civil and Transportation EngineeringShenzhen University
出 版 物：《National Science Open》 (国家科学进展)
年 卷 期：2024年第3卷第3期
页 面：88-109页
学科分类：08[工学] 0813[工学-建筑学] 0814[工学-土木工程]
基 金：supported by the National Natural Science Foundation of China (52278117) the Philosophical and Social Science Program of Guangdong Province,China (GD22XGL20) the Shenzhen Science and Technology Program(20220531101800001 and 20220810160221001)
主 题：smart building operations building energy management transfer learning semi-supervised learning generative learning
摘 要：The increasing availability of building operational data has greatly encouraged the development of advanced data-driven technologies for smart building *** operational data typically suffer from data quality problems,such as insufficient labeled and imbalanced data,making them incompatible with conventional machine learning *** advances in data science have provided novel machine learning paradigms to tackle such data challenges for practical applications,such as transfer learning,semi-supervised learning,and generative *** review aims to present the progress and perspectives on the effective utilization of novel machine learning paradigms for three major building energy management tasks,i.e.,building energy predictions,fault detection and diagnosis,and control ***-depth discussions have been provided to demonstrate the pros and cons of different learning approaches in terms of data compatibility,modeling difficulties,and possible application scenarios,which could be helpful for enhancing the feasibility of data-driven technologies for smart building operations.
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