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

dc.contributor.author Sun, Gang
dc.contributor.author Hoff, Steven
dc.contributor.author Hoff, Steven
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-02-13T10:15:12.000
dc.date.accessioned 2020-06-29T22:33:17Z
dc.date.available 2020-06-29T22:33:17Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2009
dc.date.embargo 2013-04-30
dc.date.issued 2009-06-21
dc.description.abstract <p>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.<0.5, CRM˜0; IoA˜1; and Nash-Sutcliffe EF > 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.</p>
dc.description.comments <p>This is an ASABE Meeting Presentation, Paper No. <a href="http://elibrary.asabe.org/abstract.asp?aid=27362&t=3&dabs=Y&redir=&redirType=" target="_blank">096913</a>.</p>
dc.identifier archive/lib.dr.iastate.edu/abe_eng_conf/301/
dc.identifier.articleid 1307
dc.identifier.contextkey 4090405
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_conf/301
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/319
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_conf/301/2009_SunG_PredictionIndoorClimate.pdf|||Fri Jan 14 23:28:07 UTC 2022
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords Air quality
dc.subject.keywords Typical meteorological year
dc.subject.keywords Modeling
dc.subject.keywords Long-term mean
dc.title Prediction of Indoor Climate and Long-term Air Quality Using a Building Thermal Transient model, Artificial Neural Networks and Typical Meteorological Year
dc.type article
dc.type.genre conference
dspace.entity.type Publication
relation.isAuthorOfPublication 98b46d48-66a2-4458-9b42-8c4aa050664d
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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