首页> 外文期刊>Atmospheric environment >Development of hourly probabilistic utility NO_x emission inventories using time series techniques: Part Ⅱ―multivariate approach
【24h】

Development of hourly probabilistic utility NO_x emission inventories using time series techniques: Part Ⅱ―multivariate approach

机译:利用时间序列技术建立每小时概率效用NO_x排放量清单:第二部分—多元方法

获取原文
获取原文并翻译 | 示例
           

摘要

Inter-unit dependence in the time series of capacity factors was accounted for in developing time series models for predictions of uncertainty in hourly NO_x 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 497-705 t/d which represents an uncertainty range of ― 15% to + 20% of the average value of 587 t/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.
机译:在开发用于预测基本负荷燃煤电厂每小时NO_x排放不确定性的时间序列模型中,考虑了容量因子时间序列中的单元间依赖性。针对1995年的基本案例,1998年的替代案例以及2007年的未来情景,对9家工厂的32个单位进行了分析。分析中使用了多元时间序列模型,以说明相关单位排放之间的依赖性。使用这种方法的权衡是建模过程涉及的复杂性,包括模型参数的选择和仿真过程中的计算工作量。为了模型开发,必须有足够的同时记录的所有相关单位的数据。将结果与随附论文中使用的单元间独立方法的结果进行比较。容量因子的单位间相关性高达0.86,总排放量的关联性高达0.62。 1995年案例的每日总库存的95%置信区间为497-705 t / d,这表示不确定范围为587 t / d平均值的15%至+ 20%。 2007年的案例的不确定性范围为-8%至+ 15%。这些不确定性范围比从单元间独立方法获得的相应范围宽。与观测数据相比,矢量自回归时间序列方法在容量因子中占单位间相关性的模拟比单位间独立性方法更准确。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
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