Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data

It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 a...
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Autor*in:	Norman Maswanganyi [verfasserIn] Caston Sigauke [verfasserIn] Edmore Ranganai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	additive quantile regression extremal mixture model extreme conditional quantiles nonlinear quantile regression scoring rules

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 14(2021), 20, p 6704
Übergeordnetes Werk:	volume:14 ; year:2021 ; number:20, p 6704

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en14206704

Katalog-ID:	DOAJ085559830

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allfields	10.3390/en14206704 doi (DE-627)DOAJ085559830 (DE-599)DOAJ973284ef8027421691626ba4407abfa3 DE-627 ger DE-627 rakwb eng Norman Maswanganyi verfasserin aut Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights and guidance for future electricity demand patterns. The prediction of extremely high quantiles of daily peak electricity demand could help system operators know the possible largest demand that will enable them to supply adequate electricity to consumers and shift demand to off-peak periods. The prediction of extreme conditional quantiles of daily peak electricity demand in the context of South Africa using additive quantile regression, nonlinear quantile regression, and extremal mixture models has not been performed previously to the best of our knowledge. additive quantile regression extremal mixture model extreme conditional quantiles nonlinear quantile regression scoring rules Technology T Caston Sigauke verfasserin aut Edmore Ranganai verfasserin aut In Energies MDPI AG, 2008 14(2021), 20, p 6704 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:14 year:2021 number:20, p 6704 https://doi.org/10.3390/en14206704 kostenfrei https://doaj.org/article/973284ef8027421691626ba4407abfa3 kostenfrei https://www.mdpi.com/1996-1073/14/20/6704 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 20, p 6704
spelling	10.3390/en14206704 doi (DE-627)DOAJ085559830 (DE-599)DOAJ973284ef8027421691626ba4407abfa3 DE-627 ger DE-627 rakwb eng Norman Maswanganyi verfasserin aut Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights and guidance for future electricity demand patterns. The prediction of extremely high quantiles of daily peak electricity demand could help system operators know the possible largest demand that will enable them to supply adequate electricity to consumers and shift demand to off-peak periods. The prediction of extreme conditional quantiles of daily peak electricity demand in the context of South Africa using additive quantile regression, nonlinear quantile regression, and extremal mixture models has not been performed previously to the best of our knowledge. additive quantile regression extremal mixture model extreme conditional quantiles nonlinear quantile regression scoring rules Technology T Caston Sigauke verfasserin aut Edmore Ranganai verfasserin aut In Energies MDPI AG, 2008 14(2021), 20, p 6704 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:14 year:2021 number:20, p 6704 https://doi.org/10.3390/en14206704 kostenfrei https://doaj.org/article/973284ef8027421691626ba4407abfa3 kostenfrei https://www.mdpi.com/1996-1073/14/20/6704 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 20, p 6704
allfields_unstemmed	10.3390/en14206704 doi (DE-627)DOAJ085559830 (DE-599)DOAJ973284ef8027421691626ba4407abfa3 DE-627 ger DE-627 rakwb eng Norman Maswanganyi verfasserin aut Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights and guidance for future electricity demand patterns. The prediction of extremely high quantiles of daily peak electricity demand could help system operators know the possible largest demand that will enable them to supply adequate electricity to consumers and shift demand to off-peak periods. The prediction of extreme conditional quantiles of daily peak electricity demand in the context of South Africa using additive quantile regression, nonlinear quantile regression, and extremal mixture models has not been performed previously to the best of our knowledge. additive quantile regression extremal mixture model extreme conditional quantiles nonlinear quantile regression scoring rules Technology T Caston Sigauke verfasserin aut Edmore Ranganai verfasserin aut In Energies MDPI AG, 2008 14(2021), 20, p 6704 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:14 year:2021 number:20, p 6704 https://doi.org/10.3390/en14206704 kostenfrei https://doaj.org/article/973284ef8027421691626ba4407abfa3 kostenfrei https://www.mdpi.com/1996-1073/14/20/6704 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 20, p 6704
allfieldsGer	10.3390/en14206704 doi (DE-627)DOAJ085559830 (DE-599)DOAJ973284ef8027421691626ba4407abfa3 DE-627 ger DE-627 rakwb eng Norman Maswanganyi verfasserin aut Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights and guidance for future electricity demand patterns. The prediction of extremely high quantiles of daily peak electricity demand could help system operators know the possible largest demand that will enable them to supply adequate electricity to consumers and shift demand to off-peak periods. The prediction of extreme conditional quantiles of daily peak electricity demand in the context of South Africa using additive quantile regression, nonlinear quantile regression, and extremal mixture models has not been performed previously to the best of our knowledge. additive quantile regression extremal mixture model extreme conditional quantiles nonlinear quantile regression scoring rules Technology T Caston Sigauke verfasserin aut Edmore Ranganai verfasserin aut In Energies MDPI AG, 2008 14(2021), 20, p 6704 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:14 year:2021 number:20, p 6704 https://doi.org/10.3390/en14206704 kostenfrei https://doaj.org/article/973284ef8027421691626ba4407abfa3 kostenfrei https://www.mdpi.com/1996-1073/14/20/6704 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 20, p 6704
allfieldsSound	10.3390/en14206704 doi (DE-627)DOAJ085559830 (DE-599)DOAJ973284ef8027421691626ba4407abfa3 DE-627 ger DE-627 rakwb eng Norman Maswanganyi verfasserin aut Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights and guidance for future electricity demand patterns. The prediction of extremely high quantiles of daily peak electricity demand could help system operators know the possible largest demand that will enable them to supply adequate electricity to consumers and shift demand to off-peak periods. The prediction of extreme conditional quantiles of daily peak electricity demand in the context of South Africa using additive quantile regression, nonlinear quantile regression, and extremal mixture models has not been performed previously to the best of our knowledge. additive quantile regression extremal mixture model extreme conditional quantiles nonlinear quantile regression scoring rules Technology T Caston Sigauke verfasserin aut Edmore Ranganai verfasserin aut In Energies MDPI AG, 2008 14(2021), 20, p 6704 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:14 year:2021 number:20, p 6704 https://doi.org/10.3390/en14206704 kostenfrei https://doaj.org/article/973284ef8027421691626ba4407abfa3 kostenfrei https://www.mdpi.com/1996-1073/14/20/6704 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 20, p 6704
language	English
source	In Energies 14(2021), 20, p 6704 volume:14 year:2021 number:20, p 6704
sourceStr	In Energies 14(2021), 20, p 6704 volume:14 year:2021 number:20, p 6704
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	additive quantile regression extremal mixture model extreme conditional quantiles nonlinear quantile regression scoring rules Technology T
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container_title	Energies
authorswithroles_txt_mv	Norman Maswanganyi @@aut@@ Caston Sigauke @@aut@@ Edmore Ranganai @@aut@@
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author	Norman Maswanganyi
spellingShingle	Norman Maswanganyi misc additive quantile regression misc extremal mixture model misc extreme conditional quantiles misc nonlinear quantile regression misc scoring rules misc Technology misc T Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data
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topic_title	Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data additive quantile regression extremal mixture model extreme conditional quantiles nonlinear quantile regression scoring rules
topic	misc additive quantile regression misc extremal mixture model misc extreme conditional quantiles misc nonlinear quantile regression misc scoring rules misc Technology misc T
topic_unstemmed	misc additive quantile regression misc extremal mixture model misc extreme conditional quantiles misc nonlinear quantile regression misc scoring rules misc Technology misc T
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title	Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data
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title_sort	prediction of extreme conditional quantiles of electricity demand: an application using south african data
title_auth	Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data
abstract	It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights and guidance for future electricity demand patterns. The prediction of extremely high quantiles of daily peak electricity demand could help system operators know the possible largest demand that will enable them to supply adequate electricity to consumers and shift demand to off-peak periods. The prediction of extreme conditional quantiles of daily peak electricity demand in the context of South Africa using additive quantile regression, nonlinear quantile regression, and extremal mixture models has not been performed previously to the best of our knowledge.
abstractGer	It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights and guidance for future electricity demand patterns. The prediction of extremely high quantiles of daily peak electricity demand could help system operators know the possible largest demand that will enable them to supply adequate electricity to consumers and shift demand to off-peak periods. The prediction of extreme conditional quantiles of daily peak electricity demand in the context of South Africa using additive quantile regression, nonlinear quantile regression, and extremal mixture models has not been performed previously to the best of our knowledge.
abstract_unstemmed	It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights and guidance for future electricity demand patterns. The prediction of extremely high quantiles of daily peak electricity demand could help system operators know the possible largest demand that will enable them to supply adequate electricity to consumers and shift demand to off-peak periods. The prediction of extreme conditional quantiles of daily peak electricity demand in the context of South Africa using additive quantile regression, nonlinear quantile regression, and extremal mixture models has not been performed previously to the best of our knowledge.
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title_short	Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data
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