Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building

The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits...
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Autor*in:	Xiao Han [verfasserIn] Chaohai Zhang [verfasserIn] Yi Tang [verfasserIn] Yujian Ye [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Smart building physical-data fusion modeling method energy consumption precision model thermal-electrical conversion

Übergeordnetes Werk:	In: Journal of Modern Power Systems and Clean Energy - IEEE, 2016, 10(2022), 2, Seite 482-491
Übergeordnetes Werk:	volume:10 ; year:2022 ; number:2 ; pages:482-491

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.35833/MPCE.2021.000050

Katalog-ID:	DOAJ046542078

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520			\|a The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy.
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allfields	10.35833/MPCE.2021.000050 doi (DE-627)DOAJ046542078 (DE-599)DOAJ95ad61dbd50940d58fc00848daff27e5 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Xiao Han verfasserin aut Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy. Smart building physical-data fusion modeling method energy consumption precision model thermal-electrical conversion Production of electric energy or power. Powerplants. Central stations Renewable energy sources Chaohai Zhang verfasserin aut Yi Tang verfasserin aut Yujian Ye verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 10(2022), 2, Seite 482-491 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:10 year:2022 number:2 pages:482-491 https://doi.org/10.35833/MPCE.2021.000050 kostenfrei https://doaj.org/article/95ad61dbd50940d58fc00848daff27e5 kostenfrei https://ieeexplore.ieee.org/document/9705280/ kostenfrei https://doaj.org/toc/2196-5420 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 2 482-491
spelling	10.35833/MPCE.2021.000050 doi (DE-627)DOAJ046542078 (DE-599)DOAJ95ad61dbd50940d58fc00848daff27e5 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Xiao Han verfasserin aut Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy. Smart building physical-data fusion modeling method energy consumption precision model thermal-electrical conversion Production of electric energy or power. Powerplants. Central stations Renewable energy sources Chaohai Zhang verfasserin aut Yi Tang verfasserin aut Yujian Ye verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 10(2022), 2, Seite 482-491 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:10 year:2022 number:2 pages:482-491 https://doi.org/10.35833/MPCE.2021.000050 kostenfrei https://doaj.org/article/95ad61dbd50940d58fc00848daff27e5 kostenfrei https://ieeexplore.ieee.org/document/9705280/ kostenfrei https://doaj.org/toc/2196-5420 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 2 482-491
allfields_unstemmed	10.35833/MPCE.2021.000050 doi (DE-627)DOAJ046542078 (DE-599)DOAJ95ad61dbd50940d58fc00848daff27e5 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Xiao Han verfasserin aut Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy. Smart building physical-data fusion modeling method energy consumption precision model thermal-electrical conversion Production of electric energy or power. Powerplants. Central stations Renewable energy sources Chaohai Zhang verfasserin aut Yi Tang verfasserin aut Yujian Ye verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 10(2022), 2, Seite 482-491 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:10 year:2022 number:2 pages:482-491 https://doi.org/10.35833/MPCE.2021.000050 kostenfrei https://doaj.org/article/95ad61dbd50940d58fc00848daff27e5 kostenfrei https://ieeexplore.ieee.org/document/9705280/ kostenfrei https://doaj.org/toc/2196-5420 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 2 482-491
allfieldsGer	10.35833/MPCE.2021.000050 doi (DE-627)DOAJ046542078 (DE-599)DOAJ95ad61dbd50940d58fc00848daff27e5 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Xiao Han verfasserin aut Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy. Smart building physical-data fusion modeling method energy consumption precision model thermal-electrical conversion Production of electric energy or power. Powerplants. Central stations Renewable energy sources Chaohai Zhang verfasserin aut Yi Tang verfasserin aut Yujian Ye verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 10(2022), 2, Seite 482-491 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:10 year:2022 number:2 pages:482-491 https://doi.org/10.35833/MPCE.2021.000050 kostenfrei https://doaj.org/article/95ad61dbd50940d58fc00848daff27e5 kostenfrei https://ieeexplore.ieee.org/document/9705280/ kostenfrei https://doaj.org/toc/2196-5420 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 2 482-491
allfieldsSound	10.35833/MPCE.2021.000050 doi (DE-627)DOAJ046542078 (DE-599)DOAJ95ad61dbd50940d58fc00848daff27e5 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Xiao Han verfasserin aut Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy. Smart building physical-data fusion modeling method energy consumption precision model thermal-electrical conversion Production of electric energy or power. Powerplants. Central stations Renewable energy sources Chaohai Zhang verfasserin aut Yi Tang verfasserin aut Yujian Ye verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 10(2022), 2, Seite 482-491 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:10 year:2022 number:2 pages:482-491 https://doi.org/10.35833/MPCE.2021.000050 kostenfrei https://doaj.org/article/95ad61dbd50940d58fc00848daff27e5 kostenfrei https://ieeexplore.ieee.org/document/9705280/ kostenfrei https://doaj.org/toc/2196-5420 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 2 482-491
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source	In Journal of Modern Power Systems and Clean Energy 10(2022), 2, Seite 482-491 volume:10 year:2022 number:2 pages:482-491
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An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). 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callnumber-first	T - Technology
author	Xiao Han
spellingShingle	Xiao Han misc TK1001-1841 misc TJ807-830 misc Smart building misc physical-data fusion modeling method misc energy consumption misc precision model misc thermal-electrical conversion misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building
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topic_title	TK1001-1841 TJ807-830 Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building Smart building physical-data fusion modeling method energy consumption precision model thermal-electrical conversion
topic	misc TK1001-1841 misc TJ807-830 misc Smart building misc physical-data fusion modeling method misc energy consumption misc precision model misc thermal-electrical conversion misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
topic_unstemmed	misc TK1001-1841 misc TJ807-830 misc Smart building misc physical-data fusion modeling method misc energy consumption misc precision model misc thermal-electrical conversion misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
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title	Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building
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title_full	Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building
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title_sort	physical-data fusion modeling method for energy consumption analysis of smart building
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title_auth	Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building
abstract	The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy.
abstractGer	The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy.
abstract_unstemmed	The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy.
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title_short	Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building
url	https://doi.org/10.35833/MPCE.2021.000050 https://doaj.org/article/95ad61dbd50940d58fc00848daff27e5 https://ieeexplore.ieee.org/document/9705280/ https://doaj.org/toc/2196-5420
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