...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Building and environment >Real-time prediction of indoor humidity with limited sensors using cross-sample learning
【24h】

Real-time prediction of indoor humidity with limited sensors using cross-sample learning

机译：Real-time prediction of indoor humidity with limited sensors using cross-sample learning

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

Indoor Environment Monitoring Systems (IEMSs) are equipped in buildings to improve building energy efficiency, ensure good human health and safety, and promote digital buildings. Because only a few sensors can be installed in a building, an IEMS only senses labeled positions where sensors are installed. Real-time prediction of the indoor environmental quality (IEQ) such as the humidity at unlabeled positions is still challenging. Previous approaches usually utilized a spatial interpolation algorithm, which predicts the IEQ of an unlabeled position using fixed coefficients from labeled positions assigned by experts. Noticing that labeled positions suggest different cues to infer an unlabeled position in different scenarios, the performance of traditional spatial interpolation algorithms can be improved by introducing dynamic coefficients for labeled positions. Inspired by this assumption, a novel machine-learning-based real-time prediction of indoor humidity status scheme, termed ML PIS, is proposed. First, a spatial graph model is proposed to represent the correlation between any two spatial positions. Second, a cross-sample training algorithm is designed to learn the coefficients of labeled positions within the spatial graph model, which further predicts the status of any unlabeled position. Finally, the performance of ML-PIS is systematically evaluated using humidity in a real-world environment. The experimental results showed that the mean absolute error of ML-PIS is less than 54.3% of that obtained with the inverse distance-weighted-based spatial interpolation algorithm (IDW); the root mean square error of ML-PIS is less than 50.4%; the relative error is less than 6.05%; and the accuracy is higher than 6.05%. The proposed ML-PIS is a generalized version of spatial interpolation algorithms. The proposed ML-PIS is able to provide accurate information for digital buildings, thereby allowing better decisions to be made in indoor environments.

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号