A short-term power output forecasting based on augmented naïve bayes classifiers for high wind power penetrations

Gyeongmin Kim, Jin Hur

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Renewable-power-generating resources can provide unlimited clean energy and emit at most minute amounts of air pollutants and greenhouse gases, whereas fossil fuels are contributing to environmental pollution problems and climate change. The share of global power capacity comprising renewable-power-generating resources is increasing. However, due to the variability and uncertainty of wind resources, predicting the power output of these resources remains a key problem that must be resolved to establish stable power system operation and planning. In this study, we propose an ensemble prediction model for wind-power-generating resources based on augmented naïve Bayes classifiers. To select the principal component that affects the wind power outputs from among various meteorological factors, such as temperature, wind speed, and wind direction, prediction of wind-power-generating resources was performed using multiple linear regression (MLR) and a naïve Bayes classification model based on the selected meteorological factors. We proposed applying the analogue ensemble (AnEn) algorithm and the ensemble learning technique to predict the wind power. To validate this proposed hybrid prediction model, we analyzed empirical data from the wind farm of Jeju Island in South Korea and found that the proposed model has lower error than the single prediction models.

Original languageEnglish
Article number12723
JournalSustainability (Switzerland)
Volume13
Issue number22
DOIs
StatePublished - 1 Nov 2021

Keywords

  • Analogue ensemble
  • Augmented naïve Bayes classifier
  • Multiple linear regression
  • Wind-power-generating resources

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