Probabilistic Forecasting Model of Solar Power Outputs Based on the Na ve Bayes Classifier and Kriging Models

Seungbeom Nam, Jin Hur

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Solar power's variability makes managing power system planning and operation difficult. Facilitating a high level of integration of solar power resources into a grid requires maintaining the fundamental power system so that it is stable when interconnected. Accurate and reliable forecasting helps to maintain the system safely given large-scale solar power resources; this paper therefore proposes a probabilistic forecasting approach to solar resources using the R statistics program, applying a hybrid model that considers spatio-temporal peculiarities. Information on how the weather varies at sites of interest is often unavailable, so we use a spatial modeling procedure called kriging to estimate precise data at the solar power plants. The kriging method implements interpolation with geographical property data. In this paper, we perform day-ahead forecasts of solar power based on the probability in one-hour intervals by using a Naïve Bayes Classifier model, which is a classification algorithm. We augment forecasting by taking into account the overall data distribution and applying the Gaussian probability distribution. To validate the proposed hybrid forecasting model, we perform a comparison of the proposed model with a persistence model using the normalized mean absolute error (NMAE). Furthermore, we use empirical data from South Korea's meteorological towers (MET) to interpolate weather variables at points of interest.

Original languageEnglish
Article numberen11112982
Issue number11
StatePublished - Nov 2018

Bibliographical note

Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (


  • Hybrid spatio-temporal model
  • Probabilistic forecasting
  • Solar power forecasting


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