Goodness of Fit Statistical Analysis for Different Variables of PEV Driver Behaviour

Abdulaziz Almutairi; Majed Alotaibi; M. M.A. Salama

doi:10.1109/CCECE.2018.8447806

Goodness of Fit Statistical Analysis for Different Variables of PEV Driver Behaviour

Abdulaziz Almutairi
, Majed Alotaibi
, M. M.A. Salama

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Modelling plug-in electric vehicles (PEVs) charging load for use in many power system applications requires reliable estimates of a number of random variables that characterize the PEV charging process. Among these variables are the variables relevant to the driver's behaviour (e.g., arrival and departure times and daily mileage). Determining reliable estimates of these variables is challenging, since no currently sufficient real data can be relied upon for precise descriptions of these variables. The alternative is to use sample data for each variable from the available transportation mobility data, and to estimate a proper probability distribution function (PDF) that can preserve the random characteristics of each variable and generate the desired synthetic data. This paper presents a statistical evaluation study for different collections of PDFs in order to find the best model to precisely reflect the random characteristics of each driver behaviour variable. The most commonly used PDFs, along with some advanced PDFs, have been verified against the observed sample data based on consideration of a well-known goodness of fit statistical test.

Original language	English
Title of host publication	2018 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538624104
DOIs	https://doi.org/10.1109/CCECE.2018.8447806
State	Published - 27 Aug 2018
Externally published	Yes

Publication series

Name	Canadian Conference on Electrical and Computer Engineering
Volume	2018-May
ISSN (Print)	0840-7789

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Driver behaviour
Goodness of fit test
Probability distribution function

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/CCECE.2018.8447806

Cite this

Almutairi, A., Alotaibi, M., & Salama, M. M. A. (2018). Goodness of Fit Statistical Analysis for Different Variables of PEV Driver Behaviour. In 2018 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2018 Article 8447806 (Canadian Conference on Electrical and Computer Engineering; Vol. 2018-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCECE.2018.8447806

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title = "Goodness of Fit Statistical Analysis for Different Variables of PEV Driver Behaviour",

abstract = "Modelling plug-in electric vehicles (PEVs) charging load for use in many power system applications requires reliable estimates of a number of random variables that characterize the PEV charging process. Among these variables are the variables relevant to the driver's behaviour (e.g., arrival and departure times and daily mileage). Determining reliable estimates of these variables is challenging, since no currently sufficient real data can be relied upon for precise descriptions of these variables. The alternative is to use sample data for each variable from the available transportation mobility data, and to estimate a proper probability distribution function (PDF) that can preserve the random characteristics of each variable and generate the desired synthetic data. This paper presents a statistical evaluation study for different collections of PDFs in order to find the best model to precisely reflect the random characteristics of each driver behaviour variable. The most commonly used PDFs, along with some advanced PDFs, have been verified against the observed sample data based on consideration of a well-known goodness of fit statistical test.",

keywords = "Driver behaviour, Goodness of fit test, Probability distribution function",

author = "Abdulaziz Almutairi and Majed Alotaibi and Salama, \{M. M.A.\}",

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year = "2018",

month = aug,

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doi = "10.1109/CCECE.2018.8447806",

language = "English",

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Almutairi, A, Alotaibi, M & Salama, MMA 2018, Goodness of Fit Statistical Analysis for Different Variables of PEV Driver Behaviour. in 2018 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2018., 8447806, Canadian Conference on Electrical and Computer Engineering, vol. 2018-May, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CCECE.2018.8447806

Goodness of Fit Statistical Analysis for Different Variables of PEV Driver Behaviour. / Almutairi, Abdulaziz; Alotaibi, Majed; Salama, M. M.A.
2018 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8447806 (Canadian Conference on Electrical and Computer Engineering; Vol. 2018-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Goodness of Fit Statistical Analysis for Different Variables of PEV Driver Behaviour

AU - Almutairi, Abdulaziz

AU - Alotaibi, Majed

AU - Salama, M. M.A.

PY - 2018/8/27

Y1 - 2018/8/27

N2 - Modelling plug-in electric vehicles (PEVs) charging load for use in many power system applications requires reliable estimates of a number of random variables that characterize the PEV charging process. Among these variables are the variables relevant to the driver's behaviour (e.g., arrival and departure times and daily mileage). Determining reliable estimates of these variables is challenging, since no currently sufficient real data can be relied upon for precise descriptions of these variables. The alternative is to use sample data for each variable from the available transportation mobility data, and to estimate a proper probability distribution function (PDF) that can preserve the random characteristics of each variable and generate the desired synthetic data. This paper presents a statistical evaluation study for different collections of PDFs in order to find the best model to precisely reflect the random characteristics of each driver behaviour variable. The most commonly used PDFs, along with some advanced PDFs, have been verified against the observed sample data based on consideration of a well-known goodness of fit statistical test.

AB - Modelling plug-in electric vehicles (PEVs) charging load for use in many power system applications requires reliable estimates of a number of random variables that characterize the PEV charging process. Among these variables are the variables relevant to the driver's behaviour (e.g., arrival and departure times and daily mileage). Determining reliable estimates of these variables is challenging, since no currently sufficient real data can be relied upon for precise descriptions of these variables. The alternative is to use sample data for each variable from the available transportation mobility data, and to estimate a proper probability distribution function (PDF) that can preserve the random characteristics of each variable and generate the desired synthetic data. This paper presents a statistical evaluation study for different collections of PDFs in order to find the best model to precisely reflect the random characteristics of each driver behaviour variable. The most commonly used PDFs, along with some advanced PDFs, have been verified against the observed sample data based on consideration of a well-known goodness of fit statistical test.

KW - Driver behaviour

KW - Goodness of fit test

KW - Probability distribution function

UR - https://www.scopus.com/pages/publications/85053593704

U2 - 10.1109/CCECE.2018.8447806

DO - 10.1109/CCECE.2018.8447806

M3 - Conference contribution

AN - SCOPUS:85053593704

SN - 9781538624104

T3 - Canadian Conference on Electrical and Computer Engineering

BT - 2018 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Almutairi A, Alotaibi M, Salama MMA. Goodness of Fit Statistical Analysis for Different Variables of PEV Driver Behaviour. In 2018 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8447806. (Canadian Conference on Electrical and Computer Engineering). doi: 10.1109/CCECE.2018.8447806

Goodness of Fit Statistical Analysis for Different Variables of PEV Driver Behaviour

Abstract

Publication series

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this