Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM

The periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively...

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Autores principales:	Mao Yang, Tian Peng, Xin Su, Miaomiao Ma
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	meteorological factors wavelet packet decomposition least squares support vector machine the improved generalized error mixture distribution short-term probability interval prediction General Works A
Acceso en línea:	https://doaj.org/article/b3c20a4522e440119efd5d93671ecd47
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spelling	oai:doaj.org-article:b3c20a4522e440119efd5d93671ecd472021-11-30T18:48:25ZShort-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM2296-598X10.3389/fenrg.2021.757385https://doaj.org/article/b3c20a4522e440119efd5d93671ecd472021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fenrg.2021.757385/fullhttps://doaj.org/toc/2296-598XThe periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively predicting the PV power range will greatly improve the economics and stability of the grid. Therefore, this paper proposes an improved generalized based on the combination of wavelet packet (WP) and least squares support vector machine (LSSVM) to obtain higher accuracy point prediction results. The error mixed distribution function is used to fit the probability distribution of the prediction error, and the probability prediction is performed to obtain the prediction interval. The coverage rate and average width of the prediction interval are used as indicators to evaluate the prediction results of the interval. By comparing with the results of conventional methods based on normal distribution, at 95 and 90% confidence levels, the method proposed in this paper achieves higher coverage while reducing the average bandwidth by 5.238 and 3.756%, which verifies the effectiveness of the proposed probability interval prediction method.Mao YangTian PengXin SuMiaomiao MaMiaomiao MaFrontiers Media S.A.articlemeteorological factorswavelet packet decompositionleast squares support vector machinethe improved generalized error mixture distributionshort-term probability interval predictionGeneral WorksAENFrontiers in Energy Research, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	meteorological factors wavelet packet decomposition least squares support vector machine the improved generalized error mixture distribution short-term probability interval prediction General Works A
spellingShingle	meteorological factors wavelet packet decomposition least squares support vector machine the improved generalized error mixture distribution short-term probability interval prediction General Works A Mao Yang Tian Peng Xin Su Miaomiao Ma Miaomiao Ma Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM
description	The periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively predicting the PV power range will greatly improve the economics and stability of the grid. Therefore, this paper proposes an improved generalized based on the combination of wavelet packet (WP) and least squares support vector machine (LSSVM) to obtain higher accuracy point prediction results. The error mixed distribution function is used to fit the probability distribution of the prediction error, and the probability prediction is performed to obtain the prediction interval. The coverage rate and average width of the prediction interval are used as indicators to evaluate the prediction results of the interval. By comparing with the results of conventional methods based on normal distribution, at 95 and 90% confidence levels, the method proposed in this paper achieves higher coverage while reducing the average bandwidth by 5.238 and 3.756%, which verifies the effectiveness of the proposed probability interval prediction method.
format	article
author	Mao Yang Tian Peng Xin Su Miaomiao Ma Miaomiao Ma
author_facet	Mao Yang Tian Peng Xin Su Miaomiao Ma Miaomiao Ma
author_sort	Mao Yang
title	Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM
title_short	Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM
title_full	Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM
title_fullStr	Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM
title_full_unstemmed	Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM
title_sort	short-term photovoltaic power interval prediction based on the improved generalized error mixture distribution and wavelet packet -lssvm
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/b3c20a4522e440119efd5d93671ecd47
work_keys_str_mv	AT maoyang shorttermphotovoltaicpowerintervalpredictionbasedontheimprovedgeneralizederrormixturedistributionandwaveletpacketlssvm AT tianpeng shorttermphotovoltaicpowerintervalpredictionbasedontheimprovedgeneralizederrormixturedistributionandwaveletpacketlssvm AT xinsu shorttermphotovoltaicpowerintervalpredictionbasedontheimprovedgeneralizederrormixturedistributionandwaveletpacketlssvm AT miaomiaoma shorttermphotovoltaicpowerintervalpredictionbasedontheimprovedgeneralizederrormixturedistributionandwaveletpacketlssvm AT miaomiaoma shorttermphotovoltaicpowerintervalpredictionbasedontheimprovedgeneralizederrormixturedistributionandwaveletpacketlssvm
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Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM

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