Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN tr...
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Autor*in:	Yiqing Chen [verfasserIn] Zongzhu Chen [verfasserIn] Kang Li [verfasserIn] Tiezhu Shi [verfasserIn] Xiaohua Chen [verfasserIn] Jinrui Lei [verfasserIn] Tingtian Wu [verfasserIn] Yuanling Li [verfasserIn] Qian Liu [verfasserIn] Binghua Shi [verfasserIn] Jia Guo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	carbon emission oscillatory particle swarm optimization long short-term memory

Übergeordnetes Werk:	In: Processes - MDPI AG, 2013, 11(2023), 3011, p 3011
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:3011, p 3011

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/pr11103011

Katalog-ID:	DOAJ096835826

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520			\|a Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.
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spelling	10.3390/pr11103011 doi (DE-627)DOAJ096835826 (DE-599)DOAJff0b5d2a1b2540d4a9ceac4408f7cb71 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Yiqing Chen verfasserin aut Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions. carbon emission oscillatory particle swarm optimization long short-term memory Chemical technology Chemistry Zongzhu Chen verfasserin aut Kang Li verfasserin aut Tiezhu Shi verfasserin aut Xiaohua Chen verfasserin aut Jinrui Lei verfasserin aut Tingtian Wu verfasserin aut Yuanling Li verfasserin aut Qian Liu verfasserin aut Binghua Shi verfasserin aut Jia Guo verfasserin aut In Processes MDPI AG, 2013 11(2023), 3011, p 3011 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:3011, p 3011 https://doi.org/10.3390/pr11103011 kostenfrei https://doaj.org/article/ff0b5d2a1b2540d4a9ceac4408f7cb71 kostenfrei https://www.mdpi.com/2227-9717/11/10/3011 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 3011, p 3011
allfields_unstemmed	10.3390/pr11103011 doi (DE-627)DOAJ096835826 (DE-599)DOAJff0b5d2a1b2540d4a9ceac4408f7cb71 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Yiqing Chen verfasserin aut Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions. carbon emission oscillatory particle swarm optimization long short-term memory Chemical technology Chemistry Zongzhu Chen verfasserin aut Kang Li verfasserin aut Tiezhu Shi verfasserin aut Xiaohua Chen verfasserin aut Jinrui Lei verfasserin aut Tingtian Wu verfasserin aut Yuanling Li verfasserin aut Qian Liu verfasserin aut Binghua Shi verfasserin aut Jia Guo verfasserin aut In Processes MDPI AG, 2013 11(2023), 3011, p 3011 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:3011, p 3011 https://doi.org/10.3390/pr11103011 kostenfrei https://doaj.org/article/ff0b5d2a1b2540d4a9ceac4408f7cb71 kostenfrei https://www.mdpi.com/2227-9717/11/10/3011 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 3011, p 3011
allfieldsGer	10.3390/pr11103011 doi (DE-627)DOAJ096835826 (DE-599)DOAJff0b5d2a1b2540d4a9ceac4408f7cb71 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Yiqing Chen verfasserin aut Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions. carbon emission oscillatory particle swarm optimization long short-term memory Chemical technology Chemistry Zongzhu Chen verfasserin aut Kang Li verfasserin aut Tiezhu Shi verfasserin aut Xiaohua Chen verfasserin aut Jinrui Lei verfasserin aut Tingtian Wu verfasserin aut Yuanling Li verfasserin aut Qian Liu verfasserin aut Binghua Shi verfasserin aut Jia Guo verfasserin aut In Processes MDPI AG, 2013 11(2023), 3011, p 3011 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:3011, p 3011 https://doi.org/10.3390/pr11103011 kostenfrei https://doaj.org/article/ff0b5d2a1b2540d4a9ceac4408f7cb71 kostenfrei https://www.mdpi.com/2227-9717/11/10/3011 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 3011, p 3011
allfieldsSound	10.3390/pr11103011 doi (DE-627)DOAJ096835826 (DE-599)DOAJff0b5d2a1b2540d4a9ceac4408f7cb71 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Yiqing Chen verfasserin aut Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions. carbon emission oscillatory particle swarm optimization long short-term memory Chemical technology Chemistry Zongzhu Chen verfasserin aut Kang Li verfasserin aut Tiezhu Shi verfasserin aut Xiaohua Chen verfasserin aut Jinrui Lei verfasserin aut Tingtian Wu verfasserin aut Yuanling Li verfasserin aut Qian Liu verfasserin aut Binghua Shi verfasserin aut Jia Guo verfasserin aut In Processes MDPI AG, 2013 11(2023), 3011, p 3011 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:3011, p 3011 https://doi.org/10.3390/pr11103011 kostenfrei https://doaj.org/article/ff0b5d2a1b2540d4a9ceac4408f7cb71 kostenfrei https://www.mdpi.com/2227-9717/11/10/3011 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 3011, p 3011
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callnumber-first	T - Technology
author	Yiqing Chen
spellingShingle	Yiqing Chen misc TP1-1185 misc QD1-999 misc carbon emission misc oscillatory particle swarm optimization misc long short-term memory misc Chemical technology misc Chemistry Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory
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topic_title	TP1-1185 QD1-999 Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory carbon emission oscillatory particle swarm optimization long short-term memory
topic	misc TP1-1185 misc QD1-999 misc carbon emission misc oscillatory particle swarm optimization misc long short-term memory misc Chemical technology misc Chemistry
topic_unstemmed	misc TP1-1185 misc QD1-999 misc carbon emission misc oscillatory particle swarm optimization misc long short-term memory misc Chemical technology misc Chemistry
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title	Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory
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title_sort	research of carbon emission prediction: an oscillatory particle swarm optimization for long short-term memory
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title_auth	Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory
abstract	Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.
abstractGer	Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.
abstract_unstemmed	Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.
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title_short	Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory
url	https://doi.org/10.3390/pr11103011 https://doaj.org/article/ff0b5d2a1b2540d4a9ceac4408f7cb71 https://www.mdpi.com/2227-9717/11/10/3011 https://doaj.org/toc/2227-9717
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author2	Zongzhu Chen Kang Li Tiezhu Shi Xiaohua Chen Jinrui Lei Tingtian Wu Yuanling Li Qian Liu Binghua Shi Jia Guo
author2Str	Zongzhu Chen Kang Li Tiezhu Shi Xiaohua Chen Jinrui Lei Tingtian Wu Yuanling Li Qian Liu Binghua Shi Jia Guo
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up_date	2024-07-03T22:33:57.334Z
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In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. 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Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

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