Estimation of Singapore's hourly solar radiation using hybrid-Markov transition matrices method

Ojin Kwon; Yong Jin Yoon; Seung Ki Moon; Hae Jin Choi; Joon Hyung Shim

doi:10.1007/s12541-013-0044-8

Estimation of Singapore's hourly solar radiation using hybrid-Markov transition matrices method

Ojin Kwon, Yong Jin Yoon, Seung Ki Moon, Hae Jin Choi, Joon Hyung Shim

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

In most cases, there is a substantial lack of weather data for renewable energy feasibility simulation. In this reason, generating weather data from limited monthly average information is essential in an implementation and simulation of smart grid system with a renewable energy. To predict solar radiation sequence and reduce the estimated error of the solar radiation in smart grid simulation, a novel solar data generating scheme which is called hybrid method of Markov transition matrices (MTM) and autoregressive model is developed. For case study to prove excellence of proposed hybrid method, an optimal MTM to estimate the daily solar radiation of Singapore is obtained by exploiting a historical data based on daily global solar radiation. Simulation results show that the root mean square error (RMSE) of proposed scheme is improved by approximately 50% comparing to that of the conventional MTM scheme.

Original language	English
Pages (from-to)	323-327
Number of pages	5
Journal	International Journal of Precision Engineering and Manufacturing
Volume	14
Issue number	2
DOIs	https://doi.org/10.1007/s12541-013-0044-8
Publication status	Published - 2013 Feb

Bibliographical note

Funding Information:
This work was funded by start-up grant (SUG: M58050000) from the School of Mechanical and Aerospace Engineering, Nanyang Technological University. This work was also supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Knowledge Economy (No. 20114030200020 and No. 20124010203250) and the Korea University and Chung-Ang University Grant Program.

Keywords

Autoregressive model
Hybrid MTM
Markov transition matrices
Singapore weather data
Synthetic solar radiation

ASJC Scopus subject areas

Mechanical Engineering
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

UN SDGs

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

Access to Document

10.1007/s12541-013-0044-8

Cite this

@article{01b6c8f7663b473e8391c77cf1c24201,

title = "Estimation of Singapore's hourly solar radiation using hybrid-Markov transition matrices method",

abstract = "In most cases, there is a substantial lack of weather data for renewable energy feasibility simulation. In this reason, generating weather data from limited monthly average information is essential in an implementation and simulation of smart grid system with a renewable energy. To predict solar radiation sequence and reduce the estimated error of the solar radiation in smart grid simulation, a novel solar data generating scheme which is called hybrid method of Markov transition matrices (MTM) and autoregressive model is developed. For case study to prove excellence of proposed hybrid method, an optimal MTM to estimate the daily solar radiation of Singapore is obtained by exploiting a historical data based on daily global solar radiation. Simulation results show that the root mean square error (RMSE) of proposed scheme is improved by approximately 50% comparing to that of the conventional MTM scheme.",

keywords = "Autoregressive model, Hybrid MTM, Markov transition matrices, Singapore weather data, Synthetic solar radiation",

author = "Ojin Kwon and Yoon, {Yong Jin} and Moon, {Seung Ki} and Choi, {Hae Jin} and Shim, {Joon Hyung}",

note = "Funding Information: This work was funded by start-up grant (SUG: M58050000) from the School of Mechanical and Aerospace Engineering, Nanyang Technological University. This work was also supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Knowledge Economy (No. 20114030200020 and No. 20124010203250) and the Korea University and Chung-Ang University Grant Program.",

year = "2013",

month = feb,

doi = "10.1007/s12541-013-0044-8",

language = "English",

volume = "14",

pages = "323--327",

journal = "International Journal of Precision Engineering and Manufacturing",

issn = "2234-7593",

publisher = "Korean Society of Precision Engineering",

number = "2",

}

TY - JOUR

T1 - Estimation of Singapore's hourly solar radiation using hybrid-Markov transition matrices method

AU - Kwon, Ojin

AU - Yoon, Yong Jin

AU - Moon, Seung Ki

AU - Choi, Hae Jin

AU - Shim, Joon Hyung

N1 - Funding Information: This work was funded by start-up grant (SUG: M58050000) from the School of Mechanical and Aerospace Engineering, Nanyang Technological University. This work was also supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Knowledge Economy (No. 20114030200020 and No. 20124010203250) and the Korea University and Chung-Ang University Grant Program.

PY - 2013/2

Y1 - 2013/2

N2 - In most cases, there is a substantial lack of weather data for renewable energy feasibility simulation. In this reason, generating weather data from limited monthly average information is essential in an implementation and simulation of smart grid system with a renewable energy. To predict solar radiation sequence and reduce the estimated error of the solar radiation in smart grid simulation, a novel solar data generating scheme which is called hybrid method of Markov transition matrices (MTM) and autoregressive model is developed. For case study to prove excellence of proposed hybrid method, an optimal MTM to estimate the daily solar radiation of Singapore is obtained by exploiting a historical data based on daily global solar radiation. Simulation results show that the root mean square error (RMSE) of proposed scheme is improved by approximately 50% comparing to that of the conventional MTM scheme.

AB - In most cases, there is a substantial lack of weather data for renewable energy feasibility simulation. In this reason, generating weather data from limited monthly average information is essential in an implementation and simulation of smart grid system with a renewable energy. To predict solar radiation sequence and reduce the estimated error of the solar radiation in smart grid simulation, a novel solar data generating scheme which is called hybrid method of Markov transition matrices (MTM) and autoregressive model is developed. For case study to prove excellence of proposed hybrid method, an optimal MTM to estimate the daily solar radiation of Singapore is obtained by exploiting a historical data based on daily global solar radiation. Simulation results show that the root mean square error (RMSE) of proposed scheme is improved by approximately 50% comparing to that of the conventional MTM scheme.

KW - Autoregressive model

KW - Hybrid MTM

KW - Markov transition matrices

KW - Singapore weather data

KW - Synthetic solar radiation

UR - http://www.scopus.com/inward/record.url?scp=84876581164&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876581164&partnerID=8YFLogxK

U2 - 10.1007/s12541-013-0044-8

DO - 10.1007/s12541-013-0044-8

M3 - Article

AN - SCOPUS:84876581164

SN - 2234-7593

VL - 14

SP - 323

EP - 327

JO - International Journal of Precision Engineering and Manufacturing

JF - International Journal of Precision Engineering and Manufacturing

IS - 2

ER -

Estimation of Singapore's hourly solar radiation using hybrid-Markov transition matrices method

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this