Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods
Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), win...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kisi, Ozgur [verfasserIn] Alizamir, Meysam [verfasserIn] Trajkovic, Slavisa [verfasserIn] Shiri, Jalal [verfasserIn] Kim, Sungwon [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 52(2020), 3 vom: 20. Sept., Seite 2297-2318 |
|---|---|
| Übergeordnetes Werk: |
volume:52 ; year:2020 ; number:3 ; day:20 ; month:09 ; pages:2297-2318 |
| Links: |
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| DOI / URN: |
10.1007/s11063-020-10350-4 |
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| Katalog-ID: |
SPR042059623 |
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| 520 | |a Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), wind speed ($ W_{s} $), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient ($ R^{2} $), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising $ T_{max} $, $ T_{min} $, $ H_{s} $, $ W_{s} $ and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31–21%, 23–18%, and 26–25% for ANN4, ELM4, and RBF4, respectively. | ||
| 650 | 4 | |a Bayesian model averaging |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Solar radiation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Wavelet |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Artificial neural networks |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Extreme learning machines |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Radial basis function |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Alizamir, Meysam |e verfasserin |4 aut | |
| 700 | 1 | |a Trajkovic, Slavisa |e verfasserin |4 aut | |
| 700 | 1 | |a Shiri, Jalal |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Sungwon |e verfasserin |4 aut | |
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10.1007/s11063-020-10350-4 doi (DE-627)SPR042059623 (SPR)s11063-020-10350-4-e DE-627 ger DE-627 rakwb eng 000 ASE 54.72 bkl Kisi, Ozgur verfasserin aut Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), wind speed ($ W_{s} $), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient ($ R^{2} $), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising $ T_{max} $, $ T_{min} $, $ H_{s} $, $ W_{s} $ and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31–21%, 23–18%, and 26–25% for ANN4, ELM4, and RBF4, respectively. Bayesian model averaging (dpeaa)DE-He213 Ensemble method (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Wavelet (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Extreme learning machines (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Alizamir, Meysam verfasserin aut Trajkovic, Slavisa verfasserin aut Shiri, Jalal verfasserin aut Kim, Sungwon verfasserin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 52(2020), 3 vom: 20. Sept., Seite 2297-2318 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:52 year:2020 number:3 day:20 month:09 pages:2297-2318 https://dx.doi.org/10.1007/s11063-020-10350-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 52 2020 3 20 09 2297-2318 |
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10.1007/s11063-020-10350-4 doi (DE-627)SPR042059623 (SPR)s11063-020-10350-4-e DE-627 ger DE-627 rakwb eng 000 ASE 54.72 bkl Kisi, Ozgur verfasserin aut Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), wind speed ($ W_{s} $), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient ($ R^{2} $), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising $ T_{max} $, $ T_{min} $, $ H_{s} $, $ W_{s} $ and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31–21%, 23–18%, and 26–25% for ANN4, ELM4, and RBF4, respectively. Bayesian model averaging (dpeaa)DE-He213 Ensemble method (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Wavelet (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Extreme learning machines (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Alizamir, Meysam verfasserin aut Trajkovic, Slavisa verfasserin aut Shiri, Jalal verfasserin aut Kim, Sungwon verfasserin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 52(2020), 3 vom: 20. Sept., Seite 2297-2318 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:52 year:2020 number:3 day:20 month:09 pages:2297-2318 https://dx.doi.org/10.1007/s11063-020-10350-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 52 2020 3 20 09 2297-2318 |
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10.1007/s11063-020-10350-4 doi (DE-627)SPR042059623 (SPR)s11063-020-10350-4-e DE-627 ger DE-627 rakwb eng 000 ASE 54.72 bkl Kisi, Ozgur verfasserin aut Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), wind speed ($ W_{s} $), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient ($ R^{2} $), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising $ T_{max} $, $ T_{min} $, $ H_{s} $, $ W_{s} $ and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31–21%, 23–18%, and 26–25% for ANN4, ELM4, and RBF4, respectively. Bayesian model averaging (dpeaa)DE-He213 Ensemble method (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Wavelet (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Extreme learning machines (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Alizamir, Meysam verfasserin aut Trajkovic, Slavisa verfasserin aut Shiri, Jalal verfasserin aut Kim, Sungwon verfasserin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 52(2020), 3 vom: 20. Sept., Seite 2297-2318 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:52 year:2020 number:3 day:20 month:09 pages:2297-2318 https://dx.doi.org/10.1007/s11063-020-10350-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 52 2020 3 20 09 2297-2318 |
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10.1007/s11063-020-10350-4 doi (DE-627)SPR042059623 (SPR)s11063-020-10350-4-e DE-627 ger DE-627 rakwb eng 000 ASE 54.72 bkl Kisi, Ozgur verfasserin aut Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), wind speed ($ W_{s} $), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient ($ R^{2} $), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising $ T_{max} $, $ T_{min} $, $ H_{s} $, $ W_{s} $ and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31–21%, 23–18%, and 26–25% for ANN4, ELM4, and RBF4, respectively. Bayesian model averaging (dpeaa)DE-He213 Ensemble method (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Wavelet (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Extreme learning machines (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Alizamir, Meysam verfasserin aut Trajkovic, Slavisa verfasserin aut Shiri, Jalal verfasserin aut Kim, Sungwon verfasserin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 52(2020), 3 vom: 20. Sept., Seite 2297-2318 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:52 year:2020 number:3 day:20 month:09 pages:2297-2318 https://dx.doi.org/10.1007/s11063-020-10350-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 52 2020 3 20 09 2297-2318 |
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10.1007/s11063-020-10350-4 doi (DE-627)SPR042059623 (SPR)s11063-020-10350-4-e DE-627 ger DE-627 rakwb eng 000 ASE 54.72 bkl Kisi, Ozgur verfasserin aut Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), wind speed ($ W_{s} $), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient ($ R^{2} $), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising $ T_{max} $, $ T_{min} $, $ H_{s} $, $ W_{s} $ and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31–21%, 23–18%, and 26–25% for ANN4, ELM4, and RBF4, respectively. Bayesian model averaging (dpeaa)DE-He213 Ensemble method (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Wavelet (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Extreme learning machines (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Alizamir, Meysam verfasserin aut Trajkovic, Slavisa verfasserin aut Shiri, Jalal verfasserin aut Kim, Sungwon verfasserin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 52(2020), 3 vom: 20. Sept., Seite 2297-2318 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:52 year:2020 number:3 day:20 month:09 pages:2297-2318 https://dx.doi.org/10.1007/s11063-020-10350-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 52 2020 3 20 09 2297-2318 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR042059623</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111030126.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201126s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11063-020-10350-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR042059623</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11063-020-10350-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kisi, Ozgur</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), wind speed ($ W_{s} $), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient ($ R^{2} $), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising $ T_{max} $, $ T_{min} $, $ H_{s} $, $ W_{s} $ and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. 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Kisi, Ozgur ddc 000 bkl 54.72 misc Bayesian model averaging misc Ensemble method misc Solar radiation misc Wavelet misc Artificial neural networks misc Extreme learning machines misc Radial basis function Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods |
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000 ASE 54.72 bkl Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods Bayesian model averaging (dpeaa)DE-He213 Ensemble method (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Wavelet (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Extreme learning machines (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 |
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solar radiation estimation in mediterranean climate by weather variables using a novel bayesian model averaging and machine learning methods |
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Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods |
| abstract |
Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), wind speed ($ W_{s} $), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient ($ R^{2} $), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising $ T_{max} $, $ T_{min} $, $ H_{s} $, $ W_{s} $ and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31–21%, 23–18%, and 26–25% for ANN4, ELM4, and RBF4, respectively. |
| abstractGer |
Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), wind speed ($ W_{s} $), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient ($ R^{2} $), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising $ T_{max} $, $ T_{min} $, $ H_{s} $, $ W_{s} $ and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31–21%, 23–18%, and 26–25% for ANN4, ELM4, and RBF4, respectively. |
| abstract_unstemmed |
Abstract The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature ($ T_{max} $), minimum temperature ($ T_{min} $), sunshine hours ($ H_{s} $), wind speed ($ W_{s} $), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient ($ R^{2} $), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising $ T_{max} $, $ T_{min} $, $ H_{s} $, $ W_{s} $ and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31–21%, 23–18%, and 26–25% for ANN4, ELM4, and RBF4, respectively. |
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| container_issue |
3 |
| title_short |
Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods |
| url |
https://dx.doi.org/10.1007/s11063-020-10350-4 |
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| author2 |
Alizamir, Meysam Trajkovic, Slavisa Shiri, Jalal Kim, Sungwon |
| author2Str |
Alizamir, Meysam Trajkovic, Slavisa Shiri, Jalal Kim, Sungwon |
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| doi_str |
10.1007/s11063-020-10350-4 |
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2024-07-04T00:38:44.012Z |
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| score |
7.398096 |