Weighting factor selection of the ensemble model for improving forecast accuracy of photovoltaic generating resources

Kihan Kim, Jin Hur

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Among renewable energy sources, solar power is rapidly growing as a major power source for future power systems. However, solar power has uncertainty due to the effects of weather factors, and if the penetration rate of solar power in the future increases, it could reduce the reliability of the power system. A study of accurate solar power forecasting should be done to improve the stability of the power system operation. Using the empirical data from solar power plants in South Korea, the short-term forecasting of solar power outputs were carried out for 2016. We performed solar power forecasting with the support vector regression (SVR) model, the naïve Bayes classifier (NBC), and the hourly regression model. We proposed the ensemble method including the selection of weighting factors for each model to improve forecasting accuracy. The forecasted solar power generation error was indicated using normalized mean absolute error (NMAE) compared to the plant capacity. For the ensemble method, the results of each forecasting model were weighted with the reciprocal of the standard deviation of the forecast error, thus improving the solar power outputs forecast accuracy.

Original languageEnglish
Article number3315
JournalEnergies
Volume12
Issue number17
DOIs
StatePublished - 28 Aug 2019

Keywords

  • Day ahead power forecasting
  • Ensemble
  • Machine leaning
  • Naïve Bayes classifier
  • Solar power forecasting
  • Support vector regression

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