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Standard Guide for Statistical Evaluation of Indoor Air Quality Models
Automaticky preložený názov:
Štandardné príručka pre štatistické vyhodnotenie modelov vnútorných kvalitu ovzdušia
NORMA vydaná dňa 1.9.2014
Označenie normy: ASTM D5157-97(2014)
Poznámka: NEPLATNÁ
Dátum vydania normy: 1.9.2014
Kód tovaru: NS-30066
Počet strán: 4
Približná hmotnosť: 12 g (0.03 libier)
Krajina: Americká technická norma
Kategória: Technické normy ASTM
Keywords:
indoor air quality, model, model performance, qualitative evaluation, quantitative evaluation, statistical, ICS Number Code 07.020 (Mathematics), 13.040.01 (Air quality in general)
Significance and Use | ||
4.1 Using the tools described in this guide, an individual seeking to apply an IAQ model should be able to (4.2 This guide can also be used for identifying specific areas of model deficiency that require further development or refinement. |
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1. Scope | ||
1.1 This guide provides quantitative and qualitative tools for evaluation of indoor air quality (IAQ) models. These tools include methods for assessing overall model performance as well as identifying specific areas of deficiency. Guidance is also provided in choosing data sets for model evaluation and in applying and interpreting the evaluation tools. The focus of the guide is on end results (that is, the accuracy of indoor concentrations predicted by a model), rather than operational details such as the ease of model implementation or the time required for model calculations to be performed. 1.2 Although IAQ models have been used
for some time, there is little guidance in the technical literature
on the evaluation of such models. Evaluation principles and tools
in this guide are drawn from past efforts related to outdoor air
quality or meteorological models, which have objectives similar to
those for IAQ models and a history of evaluation
literature.
Standard Terminology Relating to Sampling
and Analysis of Atmospheres (Includes all amendments and changes
9/7/2020). |
Posledná aktualizácia: 2024-11-04 (Počet položiek: 2 209 323)
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