Research on the classification and control of human factor characteristics of coal mine accidents based on K-Means clustering analysis

Miao, Dejun; Wang, Wenhao; Lv, Yueying; Liu, Lu; Yao, Kouakou F. E.; Sui, Xiuhua

doi:10.1016/j.ergon.2023.103481

Cited by 6 publications

(1 citation statement)

References 31 publications

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“…Notably, research [32] has shown that integrating PR models with K-means clustering significantly improves the precision of predictions concerning pipeline failures in water supply networks. K-means clustering has also been effectively applied in various fields, including the classification of industrial accidents [33], water pollution [34], urban waste [35], and building sub-zoning [36], further exemplifying the versatility and effectiveness of these analytical tools.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of Passive Solar Heating Systems’ Energy-Saving Potential across Varied Climatic Conditions: The Development of the Passive Solar Heating Indicator (PSHI)

Mo,

Zhang,

Yao

et al. 2024

Buildings

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This study aims to evaluate the energy-saving potential of passive solar heating systems in diverse global climates and introduce a new indicator, the passive solar heating indicator (PSHI), to enhance the efficiency of building designs. By collecting climate data from 600 cities worldwide through a simulation model, the present study employs polynomial regression to analyze the impact of outdoor temperature and solar radiation intensity on building energy savings. It also uses K-means cluster analysis to scientifically categorize cities based on their energy-saving potential. The findings underscore the benefits of both direct and indirect solar heating strategies in different climates. Significantly, the PSHI shows superior predictive accuracy and applicability over traditional indices, such as the irradiation temperature difference ratio (ITR) and the irradiation degree hour ratio (C-IDHR), especially when outdoor temperatures are close to indoor design temperatures. Moreover, the application of a cluster analysis provides hierarchical guidance on passive heating designs globally, paving the way for more accurate and customized energy-efficient building strategies.

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