Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems

An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL),...
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Autor*in:	Shuto Tsuchida [verfasserIn] Hirofumi Nonaka [verfasserIn] Noboru Yamada [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	bifacial solar cell bifacial photovoltaic module deep reinforcement learning tracking photovoltaic system

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 15(2022), 21, p 8083
Übergeordnetes Werk:	volume:15 ; year:2022 ; number:21, p 8083

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en15218083

Katalog-ID:	DOAJ083620176

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520			\|a An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting.
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allfields	10.3390/en15218083 doi (DE-627)DOAJ083620176 (DE-599)DOAJ040393dc59a2411daaf86928544c36f3 DE-627 ger DE-627 rakwb eng Shuto Tsuchida verfasserin aut Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting. bifacial solar cell bifacial photovoltaic module deep reinforcement learning tracking photovoltaic system Technology T Hirofumi Nonaka verfasserin aut Noboru Yamada verfasserin aut In Energies MDPI AG, 2008 15(2022), 21, p 8083 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:21, p 8083 https://doi.org/10.3390/en15218083 kostenfrei https://doaj.org/article/040393dc59a2411daaf86928544c36f3 kostenfrei https://www.mdpi.com/1996-1073/15/21/8083 kostenfrei https://doaj.org/toc/1996-1073 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_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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 21, p 8083
spelling	10.3390/en15218083 doi (DE-627)DOAJ083620176 (DE-599)DOAJ040393dc59a2411daaf86928544c36f3 DE-627 ger DE-627 rakwb eng Shuto Tsuchida verfasserin aut Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting. bifacial solar cell bifacial photovoltaic module deep reinforcement learning tracking photovoltaic system Technology T Hirofumi Nonaka verfasserin aut Noboru Yamada verfasserin aut In Energies MDPI AG, 2008 15(2022), 21, p 8083 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:21, p 8083 https://doi.org/10.3390/en15218083 kostenfrei https://doaj.org/article/040393dc59a2411daaf86928544c36f3 kostenfrei https://www.mdpi.com/1996-1073/15/21/8083 kostenfrei https://doaj.org/toc/1996-1073 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_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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 21, p 8083
allfields_unstemmed	10.3390/en15218083 doi (DE-627)DOAJ083620176 (DE-599)DOAJ040393dc59a2411daaf86928544c36f3 DE-627 ger DE-627 rakwb eng Shuto Tsuchida verfasserin aut Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting. bifacial solar cell bifacial photovoltaic module deep reinforcement learning tracking photovoltaic system Technology T Hirofumi Nonaka verfasserin aut Noboru Yamada verfasserin aut In Energies MDPI AG, 2008 15(2022), 21, p 8083 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:21, p 8083 https://doi.org/10.3390/en15218083 kostenfrei https://doaj.org/article/040393dc59a2411daaf86928544c36f3 kostenfrei https://www.mdpi.com/1996-1073/15/21/8083 kostenfrei https://doaj.org/toc/1996-1073 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_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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 21, p 8083
allfieldsGer	10.3390/en15218083 doi (DE-627)DOAJ083620176 (DE-599)DOAJ040393dc59a2411daaf86928544c36f3 DE-627 ger DE-627 rakwb eng Shuto Tsuchida verfasserin aut Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting. bifacial solar cell bifacial photovoltaic module deep reinforcement learning tracking photovoltaic system Technology T Hirofumi Nonaka verfasserin aut Noboru Yamada verfasserin aut In Energies MDPI AG, 2008 15(2022), 21, p 8083 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:21, p 8083 https://doi.org/10.3390/en15218083 kostenfrei https://doaj.org/article/040393dc59a2411daaf86928544c36f3 kostenfrei https://www.mdpi.com/1996-1073/15/21/8083 kostenfrei https://doaj.org/toc/1996-1073 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_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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 21, p 8083
allfieldsSound	10.3390/en15218083 doi (DE-627)DOAJ083620176 (DE-599)DOAJ040393dc59a2411daaf86928544c36f3 DE-627 ger DE-627 rakwb eng Shuto Tsuchida verfasserin aut Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting. bifacial solar cell bifacial photovoltaic module deep reinforcement learning tracking photovoltaic system Technology T Hirofumi Nonaka verfasserin aut Noboru Yamada verfasserin aut In Energies MDPI AG, 2008 15(2022), 21, p 8083 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:21, p 8083 https://doi.org/10.3390/en15218083 kostenfrei https://doaj.org/article/040393dc59a2411daaf86928544c36f3 kostenfrei https://www.mdpi.com/1996-1073/15/21/8083 kostenfrei https://doaj.org/toc/1996-1073 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_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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 21, p 8083
language	English
source	In Energies 15(2022), 21, p 8083 volume:15 year:2022 number:21, p 8083
sourceStr	In Energies 15(2022), 21, p 8083 volume:15 year:2022 number:21, p 8083
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	bifacial solar cell bifacial photovoltaic module deep reinforcement learning tracking photovoltaic system Technology T
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container_title	Energies
authorswithroles_txt_mv	Shuto Tsuchida @@aut@@ Hirofumi Nonaka @@aut@@ Noboru Yamada @@aut@@
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author	Shuto Tsuchida
spellingShingle	Shuto Tsuchida misc bifacial solar cell misc bifacial photovoltaic module misc deep reinforcement learning misc tracking photovoltaic system misc Technology misc T Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems
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topic_title	Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems bifacial solar cell bifacial photovoltaic module deep reinforcement learning tracking photovoltaic system
topic	misc bifacial solar cell misc bifacial photovoltaic module misc deep reinforcement learning misc tracking photovoltaic system misc Technology misc T
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title	Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems
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title_sort	deep reinforcement learning for the optimal angle control of tracking bifacial photovoltaic systems
title_auth	Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems
abstract	An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting.
abstractGer	An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting.
abstract_unstemmed	An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting.
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title_short	Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems
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