A hybrid spatio-temporal forecasting of solar generating resources for grid integration

Seung Beom Nam, Jin Hur

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Recently, the installed solar generating resources have been increasing rapidly. Consequently, forecasting for solar generating resources are becoming an important work to integrate utility-scale solar generating resources into power systems. As solar generating resources are variable, uncontrollable, and uncertain, accurate and reliable forecasting enables higher penetrations of solar generating resources to be deployed on the electrical power grid. Accurate forecasting of solar resources contributes to evaluation of system reserves over large geographic area and to transmission system planning. To increase the penetration of solar generating resources on the electric power grid, the accurate power forecasting of geographically distributed solar generating resources is needed. In this paper, we propose a hybrid spatio-temporal forecasting of solar generating resources based on the naïve Bayesian classifier approach and spatial modelling approach. To validate our forecasting model, we use the empirical data from the practical solar farms in South Korea.

Original languageEnglish
Pages (from-to)503-510
Number of pages8
StatePublished - 15 Jun 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd


  • Hybrid spatio-temporal forecasting
  • Kriging
  • Naïve Bayes classifier
  • Solar generating resources


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