2009 ASABE Annual International Meeting
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.
American Society of Agricultural and Biological Engineers
Sun, Gang and Hoff, Steven J., "Prediction of Indoor Climate and Long-term Air Quality Using a Building Thermal Transient model, Artificial Neural Networks and Typical Meteorological Year" (2009). Agricultural and Biosystems Engineering Conference Proceedings and Presentations. 301.