A Short-Term Power Output Forecasting Based on Augmented Naive Bayes Classifiers for High Wind Power Penetrations

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 naive 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 naive 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.

Automated summary

Renewable-power-generating resources offer unlimited clean energy with minimal air pollutants and greenhouse gases, in contrast to fossil fuels which contribute to environmental pollution and climate change. The global capacity of renewable power is increasing, but predicting the output of wind resources remains a key challenge. This study introduces an ensemble prediction model for wind power based on augmented naive Bayes classifiers, which shows lower error rates compared to single prediction models when applied to empirical data from a wind farm in Jeju Island, South Korea.