A Note on Model Selection Based on the Percentage of Accuracy-Precision

[+] Author and Article Information
Ehsan Heidaryan

Department of Chemical Engineering,
Engineering School,
University of São Paulo (USP),
Caixa Postal 61548,
São Paulo 05424-970, Brazil;
Department of Chemical Engineering,
Imperial College London,
South Kensington Campus,
London SW7 2AZ, UK
e-mail: heidaryan@engineer.com

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received October 16, 2018; final manuscript received October 22, 2018; published online November 19, 2018. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 141(4), 045501 (Nov 19, 2018) (4 pages) Paper No: JERT-18-1786; doi: 10.1115/1.4041844 History: Received October 16, 2018; Revised October 22, 2018

Mathematical methods such as empirical correlations, analytical models, numerical simulations, and data-intensive computing (data-driven models) are the key to the modeling of energy science and engineering. Accrediting of different models and deciding on the best method, however, is a serious challenge even for experts, as the application of models is not limited only to estimations, but to predictions and derivative properties. In this note, by combining meaningful metrics of accuracy and precision, a new metric for determining the best-in-class method was defined.

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Grahic Jump Location
Fig. 1

An example of marksmanship illustrating the concepts of accuracy and precision: (a) inaccurate and imprecise, (b) accurate and imprecise, (c) inaccurate and precise, and (d) accurate and precise (adapted from Ref. [16])

Grahic Jump Location
Fig. 2

Converting the worst case of Fig. 1 to a cross plot

Grahic Jump Location
Fig. 3

Locus of VCAP for different equations used in the example



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