Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach
In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gairaa, Kacem [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
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| Umfang: |
12 |
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| Übergeordnetes Werk: |
Enthalten in: Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease - Soke, Fatih ELSEVIER, 2019, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:57 ; year:2016 ; pages:238-249 ; extent:12 |
| Links: |
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| DOI / URN: |
10.1016/j.rser.2015.12.111 |
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| Katalog-ID: |
ELV013655981 |
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| 245 | 1 | 0 | |a Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach |
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| 520 | |a In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. | ||
| 520 | |a In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. | ||
| 650 | 7 | |a Global radiation |2 Elsevier | |
| 650 | 7 | |a Time-series |2 Elsevier | |
| 650 | 7 | |a ARMA |2 Elsevier | |
| 650 | 7 | |a Combined approach |2 Elsevier | |
| 650 | 7 | |a ANNs |2 Elsevier | |
| 700 | 1 | |a Khellaf, Abdallah |4 oth | |
| 700 | 1 | |a Messlem, Youcef |4 oth | |
| 700 | 1 | |a Chellali, Farouk |4 oth | |
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10.1016/j.rser.2015.12.111 doi GBV00000000000066A.pica (DE-627)ELV013655981 (ELSEVIER)S1364-0321(15)01494-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.90 bkl 44.65 bkl Gairaa, Kacem verfasserin aut Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach 2016transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. Global radiation Elsevier Time-series Elsevier ARMA Elsevier Combined approach Elsevier ANNs Elsevier Khellaf, Abdallah oth Messlem, Youcef oth Chellali, Farouk oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:57 year:2016 pages:238-249 extent:12 https://doi.org/10.1016/j.rser.2015.12.111 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 57 2016 238-249 12 045F 620 |
| spelling |
10.1016/j.rser.2015.12.111 doi GBV00000000000066A.pica (DE-627)ELV013655981 (ELSEVIER)S1364-0321(15)01494-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.90 bkl 44.65 bkl Gairaa, Kacem verfasserin aut Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach 2016transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. Global radiation Elsevier Time-series Elsevier ARMA Elsevier Combined approach Elsevier ANNs Elsevier Khellaf, Abdallah oth Messlem, Youcef oth Chellali, Farouk oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:57 year:2016 pages:238-249 extent:12 https://doi.org/10.1016/j.rser.2015.12.111 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 57 2016 238-249 12 045F 620 |
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10.1016/j.rser.2015.12.111 doi GBV00000000000066A.pica (DE-627)ELV013655981 (ELSEVIER)S1364-0321(15)01494-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.90 bkl 44.65 bkl Gairaa, Kacem verfasserin aut Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach 2016transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. Global radiation Elsevier Time-series Elsevier ARMA Elsevier Combined approach Elsevier ANNs Elsevier Khellaf, Abdallah oth Messlem, Youcef oth Chellali, Farouk oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:57 year:2016 pages:238-249 extent:12 https://doi.org/10.1016/j.rser.2015.12.111 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 57 2016 238-249 12 045F 620 |
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10.1016/j.rser.2015.12.111 doi GBV00000000000066A.pica (DE-627)ELV013655981 (ELSEVIER)S1364-0321(15)01494-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.90 bkl 44.65 bkl Gairaa, Kacem verfasserin aut Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach 2016transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. Global radiation Elsevier Time-series Elsevier ARMA Elsevier Combined approach Elsevier ANNs Elsevier Khellaf, Abdallah oth Messlem, Youcef oth Chellali, Farouk oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:57 year:2016 pages:238-249 extent:12 https://doi.org/10.1016/j.rser.2015.12.111 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 57 2016 238-249 12 045F 620 |
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10.1016/j.rser.2015.12.111 doi GBV00000000000066A.pica (DE-627)ELV013655981 (ELSEVIER)S1364-0321(15)01494-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.90 bkl 44.65 bkl Gairaa, Kacem verfasserin aut Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach 2016transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. Global radiation Elsevier Time-series Elsevier ARMA Elsevier Combined approach Elsevier ANNs Elsevier Khellaf, Abdallah oth Messlem, Youcef oth Chellali, Farouk oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:57 year:2016 pages:238-249 extent:12 https://doi.org/10.1016/j.rser.2015.12.111 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 57 2016 238-249 12 045F 620 |
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Enthalten in Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease Amsterdam [u.a.] volume:57 year:2016 pages:238-249 extent:12 |
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Enthalten in Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease Amsterdam [u.a.] volume:57 year:2016 pages:238-249 extent:12 |
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Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease |
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estimation of the daily global solar radiation based on box–jenkins and ann models: a combined approach |
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Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach |
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In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. |
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In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. |
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In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. |
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