A Short-Term Power Output Forecasting Based on Augmented Naïve 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-...

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Autores principales:	Gyeongmin Kim, Jin Hur
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	augmented naïve Bayes classifier multiple linear regression analogue ensemble wind-power-generating resources Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/4eac79baac294e9bb9d479be21f0cc3c
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spelling	oai:doaj.org-article:4eac79baac294e9bb9d479be21f0cc3c2021-11-25T19:03:50ZA Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations10.3390/su1322127232071-1050https://doaj.org/article/4eac79baac294e9bb9d479be21f0cc3c2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12723https://doaj.org/toc/2071-1050Renewable-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.Gyeongmin KimJin HurMDPI AGarticleaugmented naïve Bayes classifiermultiple linear regressionanalogue ensemblewind-power-generating resourcesEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12723, p 12723 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	augmented naïve Bayes classifier multiple linear regression analogue ensemble wind-power-generating resources Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	augmented naïve Bayes classifier multiple linear regression analogue ensemble wind-power-generating resources Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Gyeongmin Kim Jin Hur A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations
description	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.
format	article
author	Gyeongmin Kim Jin Hur
author_facet	Gyeongmin Kim Jin Hur
author_sort	Gyeongmin Kim
title	A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations
title_short	A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations
title_full	A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations
title_fullStr	A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations
title_full_unstemmed	A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations
title_sort	short-term power output forecasting based on augmented naïve bayes classifiers for high wind power penetrations
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4eac79baac294e9bb9d479be21f0cc3c
work_keys_str_mv	AT gyeongminkim ashorttermpoweroutputforecastingbasedonaugmentednaivebayesclassifiersforhighwindpowerpenetrations AT jinhur ashorttermpoweroutputforecastingbasedonaugmentednaivebayesclassifiersforhighwindpowerpenetrations AT gyeongminkim shorttermpoweroutputforecastingbasedonaugmentednaivebayesclassifiersforhighwindpowerpenetrations AT jinhur shorttermpoweroutputforecastingbasedonaugmentednaivebayesclassifiersforhighwindpowerpenetrations
_version_	1718410339423354880

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

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