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
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U2 - 10.1007/s12541-013-0044-8
DO - 10.1007/s12541-013-0044-8
M3 - Article
AN - SCOPUS:84876581164
SN - 1229-8557
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 -
