首页> 外文OA文献 >Prediction of Indoor Climate and Long-term Air Quality Using a Building Thermal Transient model, Artificial Neural Networks and Typical Meteorological Year
【2h】

Prediction of Indoor Climate and Long-term Air Quality Using a Building Thermal Transient model, Artificial Neural Networks and Typical Meteorological Year

机译:使用建筑物热瞬态模型,人工神经网络和典型气象年预测室内气候和长期空气质量

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

The objective of this research was to develop a building thermal analysis and air quality predictive (BTA-AQP) model to predict indoor climate and long-term air quality (NH3, H2S and CO2 concentrations and emissions) for swine deep-pit buildings. The paper presents the development of the BTA-AQP model using a building thermal transient model, artificial neural networks, and typical meteorological year (TMY3) data in predicting long-term air quality trends. The good model performance ratings (MSE/S.D.u3c0.5, CRM˜0; IoA˜1; and Nash-Sutcliffe EF u3e 0.5 for all the predicted parameters) and the graphical presentations reveal that the BTA-AQP model was able to accurately forecast indoor climate and gas concentrations and emissions for swine deep-pit buildings. By comparing the air quality results simulated by the BTA-AQP model using the TMY3 data set with those from a five-year local weather data set, it was found that the TMY3-based predictions followed the long-term mean patterns well, which indicates that the TMY3 data could be used to represent the long-term expectations of source air quality. Future work is needed to improve the accuracy of the BTA-AQP model in terms of four main sources of error: (1) Uncertainties in air quality data; (2) Prediction errors of the BTA model; (3) Prediction errors of the AQP model, and (4) Bias errors of the TMY3 and its limited application.
机译:这项研究的目的是开发一种建筑热分析和空气质量预测(BTA-AQP)模型,以预测猪深坑建筑的室内气候和长期空气质量(NH3,H2S和CO2浓度和排放)。本文介绍了使用建筑热瞬态模型,人工神经网络和典型气象年(TMY3)数据预测长期空气质量趋势的BTA-AQP模型的开发。良好的模型性能等级(对于所有预测参数,MSE / SD u3c0.5,CRM〜0; IoA〜1; Nash-Sutcliffe EF u3e 0.5)和图形表示表明,BTA-AQP模型能够准确预测猪深坑建筑物的室内气候,气体浓度和排放。通过将使用TMY3数据集的BTA-AQP模型模拟的空气质量结果与五年本地天气数据集的空气质量结果进行比较,发现基于TMY3的预测很好地遵循了长期平均模式,这表明TMY3数据可用于代表对源空气质量的长期期望。就四个主要误差源而言,需要进一步的工作来提高BTA-AQP模型的准确性:(1)空气质量数据的不确定性; (2)BTA模型的预测误差; (3)AQP模型的预测误差,以及(4)TMY3的偏差误差及其有限的应用。

著录项

  • 作者

    Sun Gang; Hoff Steven J.;

  • 作者单位
  • 年度 2009
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
  • 中图分类

相似文献

  • 外文文献
  • 中文文献
  • 专利

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