A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data

Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such...
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Autor*in:	Edmore Ranganai [verfasserIn] Mphiliseni B Nzuza [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Übergeordnetes Werk:	In: Journal of Energy in Southern Africa - University of Cape Town, 2018, 26(2017), 1, Seite 125-137
Übergeordnetes Werk:	volume:26 ; year:2017 ; number:1 ; pages:125-137

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.17159/2413-3051/2015/v26i1a2215

Katalog-ID:	DOAJ017592801

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allfields	10.17159/2413-3051/2015/v26i1a2215 doi (DE-627)DOAJ017592801 (DE-599)DOAJbda3e1229edd4a03b6e24b6b4c00faf0 DE-627 ger DE-627 rakwb eng TJ163.26-163.5 GE1-350 Edmore Ranganai verfasserin aut A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena. Energy conservation Environmental sciences Mphiliseni B Nzuza verfasserin aut In Journal of Energy in Southern Africa University of Cape Town, 2018 26(2017), 1, Seite 125-137 (DE-627)721350054 (DE-600)2677897-X 24133051 nnns volume:26 year:2017 number:1 pages:125-137 https://doi.org/10.17159/2413-3051/2015/v26i1a2215 kostenfrei https://doaj.org/article/bda3e1229edd4a03b6e24b6b4c00faf0 kostenfrei https://journals.assaf.org.za/jesa/article/view/2215 kostenfrei https://doaj.org/toc/1021-447X Journal toc kostenfrei https://doaj.org/toc/2413-3051 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 26 2017 1 125-137
spelling	10.17159/2413-3051/2015/v26i1a2215 doi (DE-627)DOAJ017592801 (DE-599)DOAJbda3e1229edd4a03b6e24b6b4c00faf0 DE-627 ger DE-627 rakwb eng TJ163.26-163.5 GE1-350 Edmore Ranganai verfasserin aut A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena. Energy conservation Environmental sciences Mphiliseni B Nzuza verfasserin aut In Journal of Energy in Southern Africa University of Cape Town, 2018 26(2017), 1, Seite 125-137 (DE-627)721350054 (DE-600)2677897-X 24133051 nnns volume:26 year:2017 number:1 pages:125-137 https://doi.org/10.17159/2413-3051/2015/v26i1a2215 kostenfrei https://doaj.org/article/bda3e1229edd4a03b6e24b6b4c00faf0 kostenfrei https://journals.assaf.org.za/jesa/article/view/2215 kostenfrei https://doaj.org/toc/1021-447X Journal toc kostenfrei https://doaj.org/toc/2413-3051 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 26 2017 1 125-137
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allfieldsSound	10.17159/2413-3051/2015/v26i1a2215 doi (DE-627)DOAJ017592801 (DE-599)DOAJbda3e1229edd4a03b6e24b6b4c00faf0 DE-627 ger DE-627 rakwb eng TJ163.26-163.5 GE1-350 Edmore Ranganai verfasserin aut A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena. Energy conservation Environmental sciences Mphiliseni B Nzuza verfasserin aut In Journal of Energy in Southern Africa University of Cape Town, 2018 26(2017), 1, Seite 125-137 (DE-627)721350054 (DE-600)2677897-X 24133051 nnns volume:26 year:2017 number:1 pages:125-137 https://doi.org/10.17159/2413-3051/2015/v26i1a2215 kostenfrei https://doaj.org/article/bda3e1229edd4a03b6e24b6b4c00faf0 kostenfrei https://journals.assaf.org.za/jesa/article/view/2215 kostenfrei https://doaj.org/toc/1021-447X Journal toc kostenfrei https://doaj.org/toc/2413-3051 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 26 2017 1 125-137
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callnumber-first	T - Technology
author	Edmore Ranganai
spellingShingle	Edmore Ranganai misc TJ163.26-163.5 misc GE1-350 misc Energy conservation misc Environmental sciences A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data
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topic_title	TJ163.26-163.5 GE1-350 A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data
topic	misc TJ163.26-163.5 misc GE1-350 misc Energy conservation misc Environmental sciences
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title	A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data
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title_full	A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data
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title_sort	comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data
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title_auth	A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data
abstract	Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena.
abstractGer	Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena.
abstract_unstemmed	Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena.
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title_short	A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data
url	https://doi.org/10.17159/2413-3051/2015/v26i1a2215 https://doaj.org/article/bda3e1229edd4a03b6e24b6b4c00faf0 https://journals.assaf.org.za/jesa/article/view/2215 https://doaj.org/toc/1021-447X https://doaj.org/toc/2413-3051
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up_date	2024-07-04T02:12:15.196Z
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A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data

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