Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction
The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identify...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mohammad Ehteram [verfasserIn] Hanieh Shabanian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Energy Reports - Elsevier, 2016, 10(2023), Seite 3402-3417 |
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| Übergeordnetes Werk: |
volume:10 ; year:2023 ; pages:3402-3417 |
| Links: |
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| DOI / URN: |
10.1016/j.egyr.2023.10.029 |
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| 520 | |a The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction. | ||
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10.1016/j.egyr.2023.10.029 doi (DE-627)DOAJ096746653 (DE-599)DOAJf4f01f7879394ef8a0dd62a7115bc519 DE-627 ger DE-627 rakwb eng TK1-9971 Mohammad Ehteram verfasserin aut Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction. Hybrid models Solar radiation M5T LSTM SALSTM-M5T Electrical engineering. Electronics. Nuclear engineering Hanieh Shabanian verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 3402-3417 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:3402-3417 https://doi.org/10.1016/j.egyr.2023.10.029 kostenfrei https://doaj.org/article/f4f01f7879394ef8a0dd62a7115bc519 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723014634 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 3402-3417 |
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10.1016/j.egyr.2023.10.029 doi (DE-627)DOAJ096746653 (DE-599)DOAJf4f01f7879394ef8a0dd62a7115bc519 DE-627 ger DE-627 rakwb eng TK1-9971 Mohammad Ehteram verfasserin aut Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction. Hybrid models Solar radiation M5T LSTM SALSTM-M5T Electrical engineering. Electronics. Nuclear engineering Hanieh Shabanian verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 3402-3417 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:3402-3417 https://doi.org/10.1016/j.egyr.2023.10.029 kostenfrei https://doaj.org/article/f4f01f7879394ef8a0dd62a7115bc519 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723014634 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 3402-3417 |
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10.1016/j.egyr.2023.10.029 doi (DE-627)DOAJ096746653 (DE-599)DOAJf4f01f7879394ef8a0dd62a7115bc519 DE-627 ger DE-627 rakwb eng TK1-9971 Mohammad Ehteram verfasserin aut Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction. Hybrid models Solar radiation M5T LSTM SALSTM-M5T Electrical engineering. Electronics. Nuclear engineering Hanieh Shabanian verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 3402-3417 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:3402-3417 https://doi.org/10.1016/j.egyr.2023.10.029 kostenfrei https://doaj.org/article/f4f01f7879394ef8a0dd62a7115bc519 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723014634 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 3402-3417 |
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10.1016/j.egyr.2023.10.029 doi (DE-627)DOAJ096746653 (DE-599)DOAJf4f01f7879394ef8a0dd62a7115bc519 DE-627 ger DE-627 rakwb eng TK1-9971 Mohammad Ehteram verfasserin aut Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction. Hybrid models Solar radiation M5T LSTM SALSTM-M5T Electrical engineering. Electronics. Nuclear engineering Hanieh Shabanian verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 3402-3417 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:3402-3417 https://doi.org/10.1016/j.egyr.2023.10.029 kostenfrei https://doaj.org/article/f4f01f7879394ef8a0dd62a7115bc519 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723014634 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 3402-3417 |
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10.1016/j.egyr.2023.10.029 doi (DE-627)DOAJ096746653 (DE-599)DOAJf4f01f7879394ef8a0dd62a7115bc519 DE-627 ger DE-627 rakwb eng TK1-9971 Mohammad Ehteram verfasserin aut Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction. Hybrid models Solar radiation M5T LSTM SALSTM-M5T Electrical engineering. Electronics. Nuclear engineering Hanieh Shabanian verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 3402-3417 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:3402-3417 https://doi.org/10.1016/j.egyr.2023.10.029 kostenfrei https://doaj.org/article/f4f01f7879394ef8a0dd62a7115bc519 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723014634 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 3402-3417 |
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Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. 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Mohammad Ehteram |
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Mohammad Ehteram misc TK1-9971 misc Hybrid models misc Solar radiation misc M5T misc LSTM misc SALSTM-M5T misc Electrical engineering. Electronics. Nuclear engineering Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction |
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TK1-9971 Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction Hybrid models Solar radiation M5T LSTM SALSTM-M5T |
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unveiling the salstm-m5t model and its python implementation for precise solar radiation prediction |
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Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction |
| abstract |
The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction. |
| abstractGer |
The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction. |
| abstract_unstemmed |
The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction. |
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Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction |
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https://doi.org/10.1016/j.egyr.2023.10.029 https://doaj.org/article/f4f01f7879394ef8a0dd62a7115bc519 http://www.sciencedirect.com/science/article/pii/S2352484723014634 https://doaj.org/toc/2352-4847 |
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Hanieh Shabanian |
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Hanieh Shabanian |
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820689033 |
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TK - Electrical and Nuclear Engineering |
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10.1016/j.egyr.2023.10.029 |
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TK1-9971 |
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2024-07-03T21:53:20.282Z |
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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">DOAJ096746653</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414001817.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.egyr.2023.10.029</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ096746653</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJf4f01f7879394ef8a0dd62a7115bc519</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="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Mohammad Ehteram</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. 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