Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Development of hourly probabilistic utility NOx emission inventories using time series techniques: Part II—multivariate approach
AbstractInter-unit dependence in the time series of capacity factors was accounted for in developing time series models for predictions of uncertainty in hourly NOx emissions for base load coal-fired power plants. Analyses were conducted for 32 units from 9 plants for a 1995 base case, and 1998 alternate case, and a future scenario in 2007. Multivariate time series models were employed in the analyses to account for the dependence between emissions from correlated units. The trade-off of using this approach is the complexity involved in the modeling process, including selection of model parameters and computational effort in the simulation process. Sufficient simultaneously recorded data for all correlated units must be available for purposes of model development. The results were compared to those of the inter-unit independent approach employed in a companion paper. Inter-unit correlations for capacity factor were as high as 0.86 and for total emissions were as high as 0.62. The total daily inventory for the 1995 case had a 95% confidence interval of 497–705t/d which represents an uncertainty range of −15% to +20% of the average value of 587t/d. The 2007 case had an uncertainty range of −8% to +15%. These uncertainty ranges are wider than the corresponding ranges obtained from the inter-unit independent approach. Simulations from the vector autoregressive time series approach that accounted for inter-unit correlation in capacity factor were more accurate than the inter-unit independent approach when compared to observed data.
Development of hourly probabilistic utility NOx emission inventories using time series techniques: Part II—multivariate approach
AbstractInter-unit dependence in the time series of capacity factors was accounted for in developing time series models for predictions of uncertainty in hourly NOx emissions for base load coal-fired power plants. Analyses were conducted for 32 units from 9 plants for a 1995 base case, and 1998 alternate case, and a future scenario in 2007. Multivariate time series models were employed in the analyses to account for the dependence between emissions from correlated units. The trade-off of using this approach is the complexity involved in the modeling process, including selection of model parameters and computational effort in the simulation process. Sufficient simultaneously recorded data for all correlated units must be available for purposes of model development. The results were compared to those of the inter-unit independent approach employed in a companion paper. Inter-unit correlations for capacity factor were as high as 0.86 and for total emissions were as high as 0.62. The total daily inventory for the 1995 case had a 95% confidence interval of 497–705t/d which represents an uncertainty range of −15% to +20% of the average value of 587t/d. The 2007 case had an uncertainty range of −8% to +15%. These uncertainty ranges are wider than the corresponding ranges obtained from the inter-unit independent approach. Simulations from the vector autoregressive time series approach that accounted for inter-unit correlation in capacity factor were more accurate than the inter-unit independent approach when compared to observed data.
Development of hourly probabilistic utility NOx emission inventories using time series techniques: Part II—multivariate approach
Abdel-Aziz, Amr (Autor:in) / Frey, H.Christopher (Autor:in)
Atmospheric Environment ; 37 ; 5391-5401
04.09.2003
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
A probabilistic approach to screen and improve emission inventories
| Elsevier | 2020
Development of Probabilistic Emission Inventories of Air Toxics for Jacksonville, Florida
| Taylor & Francis Verlag | 2004
Generation of synthetic 4 s utility-scale PV output time series from hourly solar irradiance data
| American Institute of Physics | 2021
| British Library Conference Proceedings | 1997