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 journalArticlepeer-review

5 Citations (Scopus)


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 languageEnglish
Pages (from-to)323-327
Number of pages5
JournalInternational Journal of Precision Engineering and Manufacturing
Issue number2
Publication statusPublished - 2013 Feb


  • 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


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