Rethinking the limitations of research on occupants’ window-opening behavior: A review

• Published in: Energy and buildings 2022-12, Vol.277, p.112552, Article 112552
• Main Authors: Liu, Yiqiao, Chong, Wen Tong, Yau, Yat Huang, Chang, Li, Cui, Tong, Yu, Haowei, Cui, Ying, Pan, Song
• Format: Article
• Language: English
• Subjects: Behavioral diversity; Data collection methods; Factor analysis; Prediction models; Window-opening behavior
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2022.112552

[Display omitted] •The strengths and weaknesses of existing data collection methods are analyzed.•The main correlated factors for different building types are summarized.•Existing methods to characterize behavioral diversity are classified and compared.•The adaptability of various prediction models is discussed.•The mutual validity of model evaluation indicators is examined. Opening windows is by far the simplest means of ventilation. It is also crucial for energy efficiency and for creating a comfortable indoor environment. During the last decade, researchers have made great strides in investigating occupants’ window operation patterns. However, the different research methods used to capture, recognize, and model behavior across the literature make it difficult to compare the results between studies. This has greatly impeded the application of window opening behavior models to building energy performance simulation software. Therefore, this paper aims to provide a comprehensive review of occupants' window opening behavior in relation to four aspects. The first aspect is data collection methods, whereby different data collection methods are analyzed, and the limitations of the existing monitoring devices are pointed out. The second aspect is the factor analysis, whereby the main correlated factors for different building types are summarized. In particular, it is assessed whether indoor temperature and indoor CO2 concentration are drivers of the window opening behavior. The third aspect is the characterization of behavioral diversity, whereby the existing methods to characterize behavioral diversity are classified and compared. The final aspect is prediction models, whereby the adaptability of various prediction models and the mutual validity of model evaluation indicators are discussed. In the future, scholars will need to make efforts to expand the test sample size, quantify window opening angles, consider the diversity of behavior and unify model evaluation metrics. This would help the application of predictive models in supporting building design and the evaluation of the effectiveness of energy efficiency technologies.